Add files using upload-large-folder tool

output/preprocess/Atherosclerosis/clinical_data/GSE123086.csv

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output/preprocess/Atherosclerosis/clinical_data/GSE123088.csv

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output/preprocess/Atherosclerosis/clinical_data/GSE57691.csv

@@ -1,2 +1,2 @@
 ,GSM1386783,GSM1386784,GSM1386785,GSM1386786,GSM1386787,GSM1386788,GSM1386789,GSM1386790,GSM1386791,GSM1386792,GSM1386793,GSM1386794,GSM1386795,GSM1386796,GSM1386797,GSM1386798,GSM1386799,GSM1386800,GSM1386801,GSM1386802,GSM1386803,GSM1386804,GSM1386805,GSM1386806,GSM1386807,GSM1386808,GSM1386809,GSM1386810,GSM1386811,GSM1386812,GSM1386813,GSM1386814,GSM1386815,GSM1386816,GSM1386817,GSM1386818,GSM1386819,GSM1386820,GSM1386821,GSM1386822,GSM1386823,GSM1386824,GSM1386825,GSM1386826,GSM1386827,GSM1386828,GSM1386829,GSM1386830,GSM1386831,GSM1386832,GSM1386833,GSM1386834,GSM1386835,GSM1386836,GSM1386837,GSM1386838,GSM1386839,GSM1386840,GSM1386841,GSM1386842,GSM1386843,GSM1386844,GSM1386845,GSM1386846,GSM1386847,GSM1386848,GSM1386849,GSM1386850
-Atherosclerosis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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output/preprocess/Atherosclerosis/code/GSE109048.py

@@ -0,0 +1,306 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE109048"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE109048"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE109048.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE109048.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE109048.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Gene expression availability
+is_gene_available = True  # Platelet gene expression profiling indicates mRNA GE data.
+
+# 2) Variable availability and conversion
+# Available keys from Sample Characteristics:
+# {0: ['tissue: Platelets'], 1: ['diagnosis: sCAD', 'diagnosis: healthy', 'diagnosis: STEMI']}
+trait_row = 1   # Use diagnosis to infer atherosclerosis presence
+age_row = None
+gender_row = None
+
+def _after_colon(val):
+    if val is None:
+        return None
+    if isinstance(val, str):
+        parts = val.split(":", 1)
+        v = parts[1] if len(parts) > 1 else parts[0]
+        return v.strip()
+    return val
+
+def convert_trait(x):
+    # Map any CAD/MI diagnosis to atherosclerosis present (1); healthy to 0; unknown -> None
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = str(v).strip().lower()
+    # Positive mappings
+    pos_terms = {
+        "scad", "s-cad", "stable cad", "cad", "coronary artery disease",
+        "stemi", "ami", "acute myocardial infarction", "myocardial infarction", "mi"
+    }
+    if vl in pos_terms:
+        return 1
+    # Negative mappings
+    neg_terms = {"healthy", "control", "healthy donor", "hd"}
+    if vl in neg_terms:
+        return 0
+    # Heuristics
+    if "cad" in vl or "coronary" in vl:
+        return 1
+    if "infarction" in vl or "stemi" in vl or "ami" in vl or vl == "mi":
+        return 1
+    if "healthy" in vl or "control" in vl or vl == "hd":
+        return 0
+    return None
+
+def convert_age(x):
+    # Not available in this dataset
+    return None
+
+def convert_gender(x):
+    # Not available in this dataset
+    return None
+
+# 3) Save metadata with initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Affymetrix probe set IDs (e.g., '2824546_st') are not human gene symbols and require mapping.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+import re
+import pandas as pd
+
+# Preserve original probe-level data
+expr_df = gene_data.copy()
+expr_ids = set(expr_df.index.astype(str))
+n_expr = len(expr_ids)
+
+# Enumerate all SOFT files and pick the one with the highest overlap to expression IDs
+soft_files = [os.path.join(in_cohort_dir, f) for f in os.listdir(in_cohort_dir) if 'soft' in f.lower()]
+if not soft_files:
+    raise FileNotFoundError("No SOFT files found in cohort directory for annotation mapping.")
+
+best = {
+    "file": None,
+    "annotation": None,
+    "probe_col": None,
+    "gene_col": None,
+    "mode": None,  # 'direct' or 'add_st'
+    "overlap": 0
+}
+
+def compute_overlap(series: pd.Series, expr_ids: set) -> tuple:
+    # Returns (overlap_direct_count, overlap_add_st_count)
+    vals = series.astype(str).str.strip()
+    unique_vals = pd.unique(vals)
+    direct = set(unique_vals) & expr_ids
+
+    # Add "_st" to numeric-like tokens (support integers or floats ending with .0)
+    def to_num_str(x):
+        m = re.fullmatch(r'(\d+)(?:\.0)?', x)
+        return m.group(1) if m else None
+
+    numeric_vals = [to_num_str(v) for v in unique_vals]
+    numeric_vals = [v for v in numeric_vals if v is not None]
+    add_st = set(v + "_st" for v in numeric_vals) & expr_ids
+
+    return len(direct), len(add_st)
+
+# Scan all SOFT files
+for sfile in soft_files:
+    try:
+        ann = get_gene_annotation(sfile)
+    except Exception as e:
+        print(f"WARNING: Failed to parse SOFT file {sfile}: {e}")
+        continue
+    if ann is None or len(ann.columns) == 0:
+        continue
+
+    # Candidate gene symbol column
+    if 'gene_assignment' in ann.columns:
+        gene_col = 'gene_assignment'
+    elif 'mrna_assignment' in ann.columns:
+        gene_col = 'mrna_assignment'
+    else:
+        # fallback to any object/text column
+        obj_cols = [c for c in ann.columns if ann[c].dtype == 'object']
+        gene_col = obj_cols[0] if obj_cols else ann.columns[0]
+
+    # Evaluate all columns for overlap with expression IDs
+    for col in ann.columns:
+        # Skip the gene_col itself for probe matching if it's the same
+        try:
+            direct_cnt, addst_cnt = compute_overlap(ann[col], expr_ids)
+        except Exception:
+            continue
+
+        # Prefer direct match over add_st when counts tie (more reliable)
+        if direct_cnt > best["overlap"]:
+            best.update({"file": sfile, "annotation": ann, "probe_col": col, "gene_col": gene_col,
+                         "mode": "direct", "overlap": direct_cnt})
+        elif addst_cnt > best["overlap"]:
+            best.update({"file": sfile, "annotation": ann, "probe_col": col, "gene_col": gene_col,
+                         "mode": "add_st", "overlap": addst_cnt})
+
+# Validate best match
+if best["annotation"] is None or best["probe_col"] is None:
+    raise RuntimeError("Failed to identify any valid probe ID column from available SOFT files for mapping.")
+
+coverage = best["overlap"] / max(1, n_expr)
+print(f"Best SOFT: {os.path.basename(best['file'])}, probe_col: {best['probe_col']}, gene_col: {best['gene_col']}, "
+      f"mode: {best['mode']}, overlap: {best['overlap']} of {n_expr} ({coverage:.1%})")
+
+# Fail fast on low coverage
+if coverage < 0.20:
+    raise RuntimeError(f"Low probe-to-annotation ID overlap ({coverage:.1%}). "
+                       f"Likely wrong platform annotation for IDs like '_st'. Aborting mapping.")
+
+# Build mapping dataframe using the selected SOFT and columns
+ann = best["annotation"]
+probe_col = best["probe_col"]
+gene_col = best["gene_col"]
+mode = best["mode"]
+
+mapping_df = ann[[probe_col, gene_col]].copy()
+mapping_df.columns = ['ID_raw', 'Gene']
+
+if mode == "direct":
+    mapping_df['ID'] = mapping_df['ID_raw'].astype(str).str.strip()
+else:
+    # add "_st" to integer-like IDs; drop others
+    raw = mapping_df['ID_raw'].astype(str).str.strip()
+    extracted = raw.str.extract(r'^(\d+)(?:\.0)?$')[0]
+    mapping_df['ID'] = extracted.where(extracted.notna(), None)
+    mapping_df['ID'] = mapping_df['ID'].astype(object)
+    mapping_df['ID'] = mapping_df['ID'].map(lambda x: f"{x}_st" if pd.notnull(x) else None)
+
+# Keep only valid rows and those present in expression data
+mapping_df = mapping_df.dropna(subset=['ID', 'Gene'])
+mapping_df = mapping_df[mapping_df['ID'].isin(expr_ids)]
+
+# Apply mapping using helper (extracts human symbols, splits multi-gene probes, sums to gene level)
+gene_data = apply_gene_mapping(expression_df=expr_df, mapping_df=mapping_df)
+
+print(f"Mapped probes: {len(mapping_df)}; Resulting genes: {gene_data.shape[0]}")
+
+# Step 7: Gene Identifier Mapping
+import pandas as pd
+
+# Start from the probe-level matrix loaded earlier
+expr_df = gene_data.copy()
+# Ensure numeric expression values to avoid arithmetic errors during mapping
+expr_df = expr_df.apply(pd.to_numeric, errors='coerce')
+
+# Load SOFT annotation
+ann = get_gene_annotation(soft_file)
+
+# Identify the best probe ID column by overlap with expression IDs
+expr_ids = set(expr_df.index.astype(str))
+overlaps = {}
+for col in ann.columns:
+    try:
+        s = ann[col].astype(str).str.strip()
+        overlaps[col] = s.isin(expr_ids).sum()
+    except Exception:
+        overlaps[col] = -1
+
+# Choose the column with maximum direct overlap
+probe_col = max(overlaps, key=overlaps.get)
+overlap_count = overlaps[probe_col]
+
+# Choose gene symbol-containing column with preference order
+if 'gene_assignment' in ann.columns:
+    gene_col = 'gene_assignment'
+elif 'mrna_assignment' in ann.columns:
+    gene_col = 'mrna_assignment'
+else:
+    # Fallback: pick a column name containing 'gene' (case-insensitive), else last column
+    gene_like = [c for c in ann.columns if 'gene' in c.lower()]
+    gene_col = gene_like[0] if gene_like else ann.columns[-1]
+
+print(f"Selected mapping columns -> probe_col: {probe_col}, gene_col: {gene_col}")
+total_probes = len(expr_ids)
+coverage = overlap_count / max(1, total_probes)
+print(f"Direct ID overlap: {overlap_count} of {total_probes} ({coverage:.1%})")
+
+# Build mapping dataframe and restrict to available probes
+mapping_df = get_gene_mapping(annotation=ann, prob_col=probe_col, gene_col=gene_col)
+mapping_df = mapping_df[mapping_df['ID'].isin(expr_ids)]
+
+# Apply mapping to obtain gene-level expression
+gene_data = apply_gene_mapping(expression_df=expr_df, mapping_df=mapping_df)
+
+print(f"Mapped probes used: {mapping_df['ID'].nunique()}; Resulting genes: {gene_data.shape[0]}")

output/preprocess/Atherosclerosis/code/GSE123086.py

@@ -0,0 +1,551 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE123086"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE123086"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE123086.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE123086.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Determine gene expression availability
+is_gene_available = True  # Agilent one-color microarray-based gene expression per background info
+
+# 2) Identify rows and define converters
+trait_row = 1   # 'primary diagnosis' includes ATHEROSCLEROSIS and HEALTHY_CONTROL among others
+age_row = 3     # contains most 'age:' entries (row 4 also has some ages but row 3 is more complete)
+gender_row = 2  # contains 'Sex: Female' and 'Sex: Male' (though mixed with diagnosis2 entries)
+
+def _extract_value(cell):
+    if cell is None:
+        return None
+    s = str(cell)
+    if ':' in s:
+        return s.split(':', 1)[1].strip()
+    return s.strip()
+
+def convert_trait(cell):
+    v = _extract_value(cell)
+    if v is None or v == '':
+        return None
+    v_up = v.upper().strip()
+    # 1 = ATHEROSCLEROSIS; 0 = HEALTHY_CONTROL; Others set to None to avoid mixing with other diseases
+    if v_up == 'ATHEROSCLEROSIS':
+        return 1
+    if v_up == 'HEALTHY_CONTROL':
+        return 0
+    return None
+
+def convert_age(cell):
+    v = _extract_value(cell)
+    if v is None or v == '':
+        return None
+    try:
+        val = float(v)
+        if 0 <= val <= 120:
+            return val
+        return None
+    except Exception:
+        return None
+
+def convert_gender(cell):
+    v = _extract_value(cell)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    if v_low.startswith('male'):
+        return 1
+    if v_low.startswith('female'):
+        return 0
+    return None
+
+# 3) Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait data available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Improve age completeness by filling missing Age with values from row 4
+    try:
+        age_row4_df = get_feature_data(clinical_data, 4, 'Age', convert_age)
+        if 'Age' in selected_clinical_df.index and 'Age' in age_row4_df.index:
+            selected_clinical_df.loc['Age'] = selected_clinical_df.loc['Age'].fillna(age_row4_df.loc['Age'])
+    except Exception:
+        pass  # Fallback silently if the helper isn't available or row 4 doesn't exist
+
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+import os
+import re
+import pandas as pd
+
+def infer_requires_mapping_from_ids(ids):
+    # Sample up to 1000 IDs for speed
+    sample = pd.Series(ids).astype(str).dropna().unique()
+    sample = sample[:1000]
+    if len(sample) == 0:
+        return True  # conservative default
+
+    def is_numeric(s):
+        return s.isdigit()
+
+    def is_ensembl(s):
+        s = s.upper()
+        return s.startswith('ENSG') or s.startswith('ENST') or s.startswith('ENSP')
+
+    def is_probe_like(s):
+        su = s.upper()
+        return (
+            '_AT' in su or su.startswith('ILMN_') or su.startswith('A_') or su.startswith('AFFX') or
+            su.startswith('GPL') or su.startswith('GSM') or su.startswith('GSE') or su.startswith('XM_') or
+            su.startswith('XR_') or su.startswith('NR_') or su.startswith('NM_')
+        )
+
+    # Gene symbol heuristic: letters with optional digits/hyphen/dot; exclude numeric and known other IDs
+    sym_regex = re.compile(r'^[A-Za-z][A-Za-z0-9\-\.]*$')
+
+    n_numeric = sum(is_numeric(s) for s in sample)
+    n_ensembl = sum(is_ensembl(s) for s in sample)
+    n_probe = sum(is_probe_like(s) for s in sample)
+    n_symbol_like = sum(bool(sym_regex.match(s)) and not (is_numeric(s) or is_ensembl(s) or is_probe_like(s)) for s in sample)
+
+    n = len(sample)
+    # If most are numeric/ensembl/probe-like, mapping is required
+    frac_non_symbol = (n_numeric + n_ensembl + n_probe) / n if n > 0 else 1.0
+    if frac_non_symbol >= 0.6:
+        return True
+    # If symbol-like dominate, no mapping needed
+    if n_symbol_like / n >= 0.6:
+        return False
+    # Default conservative: mapping required
+    return True
+
+requires_gene_mapping = True  # default conservative
+try:
+    if os.path.exists(out_gene_data_file):
+        df_gene = pd.read_csv(out_gene_data_file, index_col=0)
+        gene_ids = df_gene.index.tolist()
+        requires_gene_mapping = infer_requires_mapping_from_ids(gene_ids)
+    else:
+        # If file not present, rely on observed IDs in the prompt (numeric-like), so mapping needed
+        requires_gene_mapping = True
+except Exception:
+    requires_gene_mapping = True
+
+print(f"requires_gene_mapping = {str(requires_gene_mapping)}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+import re
+import pandas as pd
+from tools.preprocess import (
+    get_gene_annotation,
+    get_gene_mapping,
+    apply_gene_mapping,
+    normalize_gene_symbols_in_index,
+    extract_human_gene_symbols
+)
+
+# Preserve the original probe-level expression matrix from Step 3
+expr_df = gene_data.copy()
+
+def _normalize_str_series(s: pd.Series) -> pd.Series:
+    s = s.astype(str).str.strip()
+    s = s.str.replace(r'\.0$', '', regex=True)
+    return s
+
+# Prefer platform (GPL) SOFT file if available to obtain richer annotation (including gene symbols)
+try:
+    files = os.listdir(in_cohort_dir)
+    gpl_softs = [f for f in files if ('soft' in f.lower()) and ('gpl' in f.lower())]
+    soft_ann = os.path.join(in_cohort_dir, gpl_softs[0]) if len(gpl_softs) > 0 else soft_file
+except Exception:
+    soft_ann = soft_file
+
+# Load platform annotation
+ga = get_gene_annotation(soft_ann).copy()
+
+# Determine identifier column by maximizing unique overlap with expression IDs
+expr_ids = pd.Index(expr_df.index.astype(str)).str.replace(r'\.0$', '', regex=True)
+expr_ids_set = set(expr_ids)
+
+best_id_col = None
+best_overlap_unique = -1
+
+for col in ga.columns:
+    col_vals = _normalize_str_series(ga[col])
+    overlap_unique = col_vals[col_vals.isin(expr_ids)].nunique()
+    if overlap_unique > best_overlap_unique:
+        best_overlap_unique = overlap_unique
+        best_id_col = col
+
+# Fallback if none detected
+if best_id_col is None:
+    for c in ['ID', 'SPOT_ID', 'PROBE_ID']:
+        if c in ga.columns:
+            best_id_col = c
+            break
+
+print(f"Selected identifier column: {best_id_col} with unique overlap {best_overlap_unique} against {len(expr_ids_set)} probes")
+
+# Try to find a gene symbol column
+symbol_col_candidates = [
+    'Gene Symbol', 'GENE_SYMBOL', 'GENE SYMBOL', 'Gene symbol', 'SYMBOL',
+    'GENE_SYMBOLS', 'GeneSymbol', 'Gene.symbol', 'Gene Symbol(s)', 'GeneSymbol(s)',
+    'GENE_NAME', 'GENE', 'Symbol', 'GENE NAME'
+]
+gene_symbol_col = None
+for c in symbol_col_candidates:
+    if c in ga.columns:
+        gene_symbol_col = c
+        break
+
+# If explicit symbol column not present, attempt inference from any non-ID column by extracting symbol-like tokens
+if gene_symbol_col is None:
+    min_rows = max(20, int(0.02 * len(ga)))  # at least 2% of rows or 20 rows carry symbols
+    inferred_col = None
+    inferred_support = 0
+    for col in ga.columns:
+        if col == best_id_col:
+            continue
+        series = ga[col].astype(str).map(extract_human_gene_symbols)
+        count_nonempty = series.apply(lambda x: len(x) if isinstance(x, list) else 0).gt(0).sum()
+        if count_nonempty > inferred_support:
+            inferred_support = count_nonempty
+            inferred_col = col
+    if inferred_col is not None and inferred_support >= min_rows:
+        gene_symbol_col = inferred_col
+        print(f"Inferred gene symbol source column: {gene_symbol_col} (rows with symbol-like tokens: {inferred_support})")
+
+# Build mapping and convert to gene-level with gene symbols when possible
+def _build_symbol_mapping_and_apply(ga: pd.DataFrame, id_col: str, sym_col: str) -> pd.DataFrame:
+    m = get_gene_mapping(annotation=ga, prob_col=id_col, gene_col=sym_col)
+    # Normalize ID strings and filter to measured probes
+    m['ID'] = _normalize_str_series(m['ID'])
+    m = m[m['ID'].isin(expr_ids_set)]
+    # Remove exact duplicate (ID, Gene) pairs to avoid expansion
+    m = m.drop_duplicates(subset=['ID', 'Gene'])
+    # Apply mapping with the library helper (extracts human symbols internally, splits multi-gene probes, sums)
+    gd = apply_gene_mapping(expression_df=expr_df, mapping_df=m)
+    # Canonicalize gene symbols using synonym table
+    try:
+        gd = normalize_gene_symbols_in_index(gd)
+    except Exception as e:
+        print(f"INFO: Gene symbol normalization skipped or partially applied: {e}")
+    return gd
+
+# Fallback: build Entrez-indexed matrix, and optionally convert Entrez -> symbol using annotation if possible
+def _build_entrez_mapping_and_apply(ga: pd.DataFrame, id_col: str, optional_sym_col: str = None) -> pd.DataFrame:
+    if 'ENTREZ_GENE_ID' not in ga.columns:
+        raise RuntimeError("No gene symbol and no ENTREZ_GENE_ID available in annotation; cannot proceed.")
+
+    m = get_gene_mapping(annotation=ga, prob_col=id_col, gene_col='ENTREZ_GENE_ID')
+    m['ID'] = _normalize_str_series(m['ID'])
+    m = m[m['ID'].isin(expr_ids_set)]
+    # Split mapping values; keep only numeric Entrez IDs
+    def split_keep_numeric(x: str):
+        if pd.isna(x):
+            return []
+        s = str(x)
+        nums = re.findall(r'\d+', s)
+        # cap to avoid pathological many-to-many expansion
+        if len(nums) > 5:
+            nums = nums[:5]
+        return nums
+    m['Gene'] = m['Gene'].astype(str).map(split_keep_numeric)
+    m = m.explode('Gene').dropna(subset=['Gene'])
+    m = m[m['Gene'].astype(str).str.strip() != '']
+    m = m.drop_duplicates(subset=['ID', 'Gene'])
+
+    merged = m.set_index('ID').join(expr_df, how='inner')
+    expr_cols = [c for c in merged.columns if c not in ['Gene']]
+    # If a probe maps to multiple Entrez IDs, divide equally
+    counts_per_id = m.groupby('ID')['Gene'].nunique()
+    merged = merged.join(counts_per_id.rename('num_genes'), on='ID')
+    merged['num_genes'] = merged['num_genes'].fillna(1)
+    merged[expr_cols] = merged[expr_cols].div(merged['num_genes'], axis=0)
+    gd_entrez = merged.groupby('Gene')[expr_cols].sum()
+
+    # Optional: convert Entrez -> symbols using annotation if an appropriate column exists
+    if optional_sym_col and optional_sym_col in ga.columns:
+        entrez_to_sym = ga[['ENTREZ_GENE_ID', optional_sym_col]].dropna()
+        entrez_to_sym['ENTREZ_GENE_ID'] = entrez_to_sym['ENTREZ_GENE_ID'].astype(str).str.extract(r'(\d+)')
+        entrez_to_sym = entrez_to_sym.dropna(subset=['ENTREZ_GENE_ID'])
+        # Extract a single canonical symbol from the optional_sym_col
+        entrez_to_sym['SYMBOL_EXTRACT'] = entrez_to_sym[optional_sym_col].astype(str).map(lambda t: (extract_human_gene_symbols(t) or [None])[0])
+        entrez_to_sym = entrez_to_sym.dropna(subset=['SYMBOL_EXTRACT'])
+        # Build mapping dict
+        e2s = entrez_to_sym.drop_duplicates(subset=['ENTREZ_GENE_ID']).set_index('ENTREZ_GENE_ID')['SYMBOL_EXTRACT'].to_dict()
+        # Map index and aggregate
+        idx = gd_entrez.index.astype(str)
+        sym_index = idx.map(lambda x: e2s.get(re.findall(r'\d+', x)[0], None) if re.findall(r'\d+', x) else None)
+        gd_sym = gd_entrez.copy()
+        gd_sym.index = sym_index
+        gd_sym = gd_sym[gd_sym.index.notnull()]
+        if len(gd_sym) > 0:
+            try:
+                gd_sym = normalize_gene_symbols_in_index(gd_sym)
+            except Exception as e:
+                print(f"INFO: Gene symbol normalization skipped or partially applied: {e}")
+            return gd_sym
+
+    # Return Entrez-indexed if symbol conversion not possible
+    return gd_entrez
+
+gene_data_mapped = None
+used_symbol_col = None
+
+# First attempt: use a symbol-bearing column (explicit or inferred)
+if gene_symbol_col is not None:
+    try:
+        gene_data_mapped = _build_symbol_mapping_and_apply(ga, best_id_col, gene_symbol_col)
+        used_symbol_col = gene_symbol_col
+        print(f"Gene-level matrix constructed using symbol column '{gene_symbol_col}'. Shape: {gene_data_mapped.shape}")
+    except Exception as e:
+        print(f"WARNING: Symbol-based mapping failed with error: {e}")
+
+# Sanity checks; if failed or implausible, fallback to Entrez-based mapping
+def _is_implausible(df: pd.DataFrame) -> bool:
+    if df is None or len(df) == 0:
+        return True
+    n_genes = df.shape[0]
+    return (n_genes < 500) or (n_genes > 100000)
+
+if _is_implausible(gene_data_mapped):
+    print("INFO: Symbol mapping implausible or unavailable; falling back to Entrez-based mapping.")
+    gene_data_mapped = _build_entrez_mapping_and_apply(ga, best_id_col, optional_sym_col=used_symbol_col)
+    # If still too many genes, try a light filter: retain genes with finite variance and drop any empty rows (should be none)
+    if gene_data_mapped.shape[0] > 120000:
+        print(f"WARNING: Entrez-based mapping produced {gene_data_mapped.shape[0]} genes; attempting de-duplication and sanity reduction.")
+        gene_data_mapped = gene_data_mapped.groupby(gene_data_mapped.index).sum()
+        # Remove rows that are all zeros (unlikely but safe)
+        nonzero_mask = (gene_data_mapped.abs().sum(axis=1) > 0)
+        gene_data_mapped = gene_data_mapped.loc[nonzero_mask]
+        print(f"Post-filter gene count: {gene_data_mapped.shape[0]}")
+
+# Final assignment
+gene_data = gene_data_mapped
+print(f"Final gene-level data shape: {gene_data.shape}")
+
+# Persist gene-level data
+try:
+    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+    gene_data.to_csv(out_gene_data_file)
+    print(f"Saved gene-level data to {out_gene_data_file}")
+except Exception as e:
+    print(f"INFO: Could not save gene-level data to file: {e}")
+
+# Step 7: Gene Identifier Mapping
+import os
+import re
+import pandas as pd
+
+# Re-load the original probe-level expression matrix to avoid propagating any prior incorrect mappings
+expr_df = get_genetic_data(matrix_file)
+
+def _normalize_str_series(s: pd.Series) -> pd.Series:
+    return s.astype(str).str.strip().str.replace(r'\.0$', '', regex=True)
+
+# Prefer a platform (GPL) SOFT if present; otherwise use the existing soft_file
+try:
+    files = os.listdir(in_cohort_dir)
+    gpl_softs = [f for f in files if ('soft' in f.lower()) and ('gpl' in f.lower())]
+    soft_ann = os.path.join(in_cohort_dir, gpl_softs[0]) if len(gpl_softs) > 0 else soft_file
+except Exception:
+    soft_ann = soft_file
+
+ga = get_gene_annotation(soft_ann).copy()
+
+# Determine which annotation column matches the expression IDs (max overlap)
+expr_ids = pd.Index(expr_df.index.astype(str)).str.replace(r'\.0$', '', regex=True)
+expr_ids_set = set(expr_ids)
+best_id_col, best_overlap = None, -1
+for col in ga.columns:
+    col_vals = _normalize_str_series(ga[col])
+    overlap = col_vals[col_vals.isin(expr_ids)].nunique()
+    if overlap > best_overlap:
+        best_overlap = overlap
+        best_id_col = col
+
+if best_id_col is None:
+    # Fallback to common identifier columns if overlap heuristic fails
+    for c in ['ID', 'SPOT_ID', 'PROBE_ID']:
+        if c in ga.columns:
+            best_id_col = c
+            break
+if best_id_col is None:
+    raise RuntimeError("Could not determine an annotation identifier column matching expression IDs.")
+
+# Try to find a gene symbol column
+symbol_col_candidates = [
+    'Gene Symbol', 'GENE_SYMBOL', 'GENE SYMBOL', 'Gene symbol', 'SYMBOL',
+    'GENE_SYMBOLS', 'GeneSymbol', 'Gene.symbol', 'Gene Symbol(s)', 'GeneSymbol(s)',
+    'GENE_NAME', 'GENE', 'Symbol', 'GENE NAME'
+]
+gene_symbol_col = None
+for c in symbol_col_candidates:
+    if c in ga.columns:
+        gene_symbol_col = c
+        break
+
+# If not found, try to infer a column that contains human gene symbols
+if gene_symbol_col is None:
+    inferred_col = None
+    inferred_support = 0
+    for col in ga.columns:
+        if col == best_id_col:
+            continue
+        series = ga[col].astype(str).map(extract_human_gene_symbols)
+        count_nonempty = series.apply(lambda x: len(x) if isinstance(x, list) else 0).gt(0).sum()
+        if count_nonempty > inferred_support:
+            inferred_support = count_nonempty
+            inferred_col = col
+    # Require at least some reasonable support (>1% rows) to accept inference
+    if inferred_col is not None and inferred_support >= max(20, int(0.01 * len(ga))):
+        gene_symbol_col = inferred_col
+
+# Mapping and aggregation
+def _apply_symbol_mapping(ga: pd.DataFrame, id_col: str, sym_col: str) -> pd.DataFrame:
+    m = get_gene_mapping(annotation=ga, prob_col=id_col, gene_col=sym_col)
+    m['ID'] = _normalize_str_series(m['ID'])
+    # Keep only mappings for probes present in expression matrix
+    m = m[m['ID'].isin(expr_ids_set)].drop_duplicates(subset=['ID', 'Gene'])
+    gd = apply_gene_mapping(expression_df=expr_df, mapping_df=m)
+    # Normalize symbols if possible
+    try:
+        gd = normalize_gene_symbols_in_index(gd)
+    except Exception:
+        pass
+    return gd
+
+def _parse_entrez_list(val: str) -> list:
+    # Conservative parser: split on common separators; keep only pure integer tokens
+    if pd.isna(val):
+        return []
+    s = str(val).strip()
+    if not s:
+        return []
+    # Replace typical multi-ID separators with semicolon
+    s = re.sub(r'\s*//+\s*', ';', s)  # '//' or '///'
+    s = re.sub(r'[,\s]+', ';', s)     # commas and whitespace to ';'
+    toks = [t for t in s.split(';') if t.strip().isdigit()]
+    # Deduplicate, preserve order
+    seen = set()
+    out = []
+    for t in toks:
+        if t not in seen:
+            seen.add(t)
+            out.append(t)
+    return out
+
+def _apply_entrez_mapping(ga: pd.DataFrame, id_col: str) -> pd.DataFrame:
+    if 'ENTREZ_GENE_ID' not in ga.columns:
+        raise RuntimeError("ENTREZ_GENE_ID not found in annotation; cannot proceed without gene symbols.")
+    m = ga[[id_col, 'ENTREZ_GENE_ID']].dropna().copy()
+    m[id_col] = _normalize_str_series(m[id_col])
+    m = m[m[id_col].isin(expr_ids_set)]
+    # Strictly parse Entrez IDs to avoid extracting arbitrary numbers
+    m['Gene'] = m['ENTREZ_GENE_ID'].map(_parse_entrez_list)
+    m = m.explode('Gene').dropna(subset=['Gene'])
+    m = m[m['Gene'].astype(str).str.strip() != '']
+    m = m.drop_duplicates(subset=[id_col, 'Gene'])
+    if m.empty:
+        # As a last resort, if the ID itself is an Entrez ID (all digits), map ID->ID one-to-one
+        id_is_numeric = pd.Index(expr_df.index).str.fullmatch(r'\d+').all()
+        if id_is_numeric:
+            gd = expr_df.copy()
+            gd.index.name = 'Gene'
+            return gd
+        else:
+            raise RuntimeError("No valid Entrez mappings could be derived.")
+    # Equal-split across multiple genes per probe
+    counts_per_id = m.groupby(id_col)['Gene'].nunique().rename('num_genes')
+    merged = m.set_index(id_col).join(expr_df, how='inner')
+    expr_cols = [c for c in merged.columns if c not in ['Gene']]
+    merged = merged.join(counts_per_id, on=id_col)
+    merged['num_genes'] = merged['num_genes'].fillna(1)
+    merged[expr_cols] = merged[expr_cols].div(merged['num_genes'], axis=0)
+    gd = merged.groupby('Gene')[expr_cols].sum()
+    gd.index.name = 'Gene'
+    return gd
+
+# Choose mapping path
+if gene_symbol_col is not None:
+    try:
+        gene_data = _apply_symbol_mapping(ga, best_id_col, gene_symbol_col)
+        # Sanity check: expect a reasonable number of genes
+        if gene_data.shape[0] < 500 or gene_data.shape[0] > 100000:
+            raise RuntimeError("Implausible gene count after symbol mapping.")
+    except Exception:
+        gene_data = _apply_entrez_mapping(ga, best_id_col)
+else:
+    gene_data = _apply_entrez_mapping(ga, best_id_col)
+
+# Persist gene-level data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)

output/preprocess/Atherosclerosis/code/GSE123088.py

@@ -0,0 +1,268 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE123088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE123088"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE123088.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE123088.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE123088.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine data availability based on provided sample characteristics summary
+is_gene_available = True  # Single-cell study on CD4+ T cells; likely gene expression, not miRNA/methylation.
+
+# Identify rows for variables based on the Sample Characteristics Dictionary provided above
+trait_row = 1     # primary diagnosis (includes ATHEROSCLEROSIS and controls)
+age_row = 3       # majority of 'age' values appear here
+gender_row = 2    # contains both 'Sex: Female' and 'Sex: Male' (non-constant)
+
+# Converters
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    try:
+        s = str(x).strip()
+    except Exception:
+        return None
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    # Map ATHEROSCLEROSIS to 1; Control/Healthy to 0; other diseases to None
+    v = _extract_value(x)
+    if v is None:
+        return None
+    norm = str(v).strip().upper().replace('-', '_').replace(' ', '_')
+    # Controls
+    if norm in {'CONTROL', 'HEALTHY', 'HEALTHY_CONTROL', 'NORMAL'}:
+        return 0
+    # Case
+    if norm == 'ATHEROSCLEROSIS':
+        return 1
+    # Other diseases -> unknown for this trait context
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    sv = str(v).strip()
+    if sv == '' or sv.upper() in {'NA', 'N/A', 'UNKNOWN', 'NULL'}:
+        return None
+    # keep only number and decimal point
+    try:
+        return float(sv)
+    except Exception:
+        # attempt to parse with non-digit characters removed
+        import re
+        nums = re.findall(r'[-+]?\d*\.?\d+', sv)
+        if nums:
+            try:
+                return float(nums[0])
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    norm = str(v).strip().upper()
+    if norm in {'MALE', 'M'}:
+        return 1
+    if norm in {'FEMALE', 'F'}:
+        return 0
+    return None
+
+# Initial filtering metadata save
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview
+    print(preview_df(selected_clinical_df))
+
+    # Save clinical features
+    import os
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Attempt to map probe IDs to human gene symbols if available; otherwise, fallback to using probe IDs as gene identifiers.
+
+import pandas as pd
+
+# Columns
+id_col = 'ID'
+candidate_symbol_cols = [
+    'Gene Symbol', 'GENE_SYMBOL', 'Gene symbol', 'SYMBOL', 'Symbol', 'Gene', 'GENE',
+    'gene_assignment', 'GENE_SYMBOLS', 'GENE_NAME', 'GeneName', 'Gene_Name'
+]
+symbol_col = next((c for c in candidate_symbol_cols if c in gene_annotation.columns), None)
+
+print(f"Identifier column selected: {id_col}")
+print(f"Gene symbol column selected: {symbol_col}")
+
+if symbol_col is not None:
+    try:
+        mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=symbol_col)
+        mapped_gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+        # Use mapped data only if non-empty
+        if mapped_gene_data.shape[0] > 0:
+            gene_data = mapped_gene_data
+            gene_data.index.name = 'Gene'
+        else:
+            # Fallback: keep probe IDs
+            gene_data = gene_data.copy()
+            gene_data.index.name = 'Gene'
+    except Exception as e:
+        # Robust fallback on any failure
+        print(f"Mapping to gene symbols failed with error: {e}. Falling back to probe IDs as genes.")
+        gene_data = gene_data.copy()
+        gene_data.index.name = 'Gene'
+else:
+    # No gene symbol column available; fallback to using probe IDs as gene identifiers
+    gene_data = gene_data.copy()
+    gene_data.index.name = 'Gene'
+
+# Ensure expression columns are numeric
+gene_data = gene_data.apply(pd.to_numeric, errors='coerce')
+
+# Step 7: Data Normalization and Linking
+import os
+import re
+
+# Helper to detect if index entries look like human gene symbols
+def _looks_like_gene_symbol(idx, sample_size=500, threshold=0.5):
+    if len(idx) == 0:
+        return False
+    # Sample to speed up
+    sample = list(idx[:sample_size]) if len(idx) > sample_size else list(idx)
+    basic_pat = re.compile(r"^[A-Z][A-Z0-9-]{1,9}$")
+    orf_pat = re.compile(r"^C\d+orf\d+$")
+    exclude_prefixes = ("NR_", "XR_", "LOC", "LINC")
+    matches = 0
+    for s in sample:
+        if not isinstance(s, str):
+            s = str(s)
+        if any(s.upper().startswith(p) for p in exclude_prefixes):
+            continue
+        if basic_pat.match(s) or orf_pat.match(s):
+            matches += 1
+    return (matches / len(sample)) >= threshold
+
+note_parts = []
+
+# 1. Normalize gene symbols when feasible; otherwise, keep identifiers as-is
+apply_normalization = _looks_like_gene_symbol(gene_data.index)
+final_gene_data = gene_data
+if apply_normalization:
+    before_rows = gene_data.shape[0]
+    try:
+        normalized_gene_data = normalize_gene_symbols_in_index(gene_data.copy())
+        if normalized_gene_data.shape[0] > 0:
+            final_gene_data = normalized_gene_data
+            note_parts.append(f"INFO: Gene symbols normalized using synonym table (rows {before_rows} -> {normalized_gene_data.shape[0]}).")
+        else:
+            note_parts.append("WARNING: Normalization yielded 0 rows; falling back to original identifiers (likely probes).")
+    except Exception as e:
+        note_parts.append(f"WARNING: Normalization failed with error: {e}; falling back to original identifiers.")
+else:
+    note_parts.append("INFO: Index does not resemble human gene symbols; normalization skipped and probe-level identifiers retained.")
+
+# Ensure output directory exists and save gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+final_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, final_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort information
+notes = " | ".join(note_parts) if note_parts else "INFO: No special notes."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=notes
+)
+
+# 6. Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Atherosclerosis/code/GSE125771.py

@@ -0,0 +1,223 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE125771"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE125771"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE125771.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE125771.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE125771.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability
+is_gene_available = True  # RNA expression microarray data (mRNA), not miRNA/methylation
+
+# 2) Variable availability and conversion functions
+
+# Trait (Atherosclerosis): Only carotid-atherosclerotic-plaque samples; no variation -> not available
+trait_row = None
+
+def convert_trait(x: str):
+    # Not used since trait_row is None
+    return None
+
+# Age
+age_row = 3  # 'age: <number>'
+def convert_age(x: str):
+    if x is None:
+        return None
+    try:
+        # Extract part after colon
+        val = x.split(":", 1)[1].strip()
+    except Exception:
+        val = x.strip()
+    if val == "" or val.lower() in {"na", "n/a", "nan", "null", "unknown"}:
+        return None
+    # Extract numeric
+    m = re.search(r"-?\d+\.?\d*", val)
+    if not m:
+        return None
+    try:
+        num = float(m.group())
+        # return integer if it's whole number
+        return int(num) if num.is_integer() else num
+    except Exception:
+        return None
+
+# Gender
+gender_row = 2  # 'Sex: Male' / 'Sex: Female'
+def convert_gender(x: str):
+    if x is None:
+        return None
+    try:
+        val = x.split(":", 1)[1].strip().lower()
+    except Exception:
+        val = x.strip().lower()
+    if val in {"male", "m"}:
+        return 1
+    if val in {"female", "f"}:
+        return 0
+    if val in {"unknown", "na", "n/a", "nan", ""}:
+        return None
+    # Heuristic for unexpected strings
+    if "male" in val:
+        return 1
+    if "female" in val:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction
+# Skip because trait_row is None (no varying trait label available)
+# If trait_row were available:
+# selected_clinical = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected_clinical.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for probe IDs and gene symbols from the annotation
+id_col = 'ID' if 'ID' in gene_annotation.columns else 'probeset_id'
+gene_symbol_col = 'gene_assignment'  # contains gene symbols within descriptive strings
+
+# 2. Get mapping dataframe between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine trait availability from previous steps
+is_trait_available = (trait_row is not None)
+
+# 2-6. Handle two branches based on trait availability
+if is_trait_available:
+    # Attempt to find a clinical dataframe variable created earlier
+    selected_clinical_var = None
+    if 'selected_clinical_data' in locals():
+        selected_clinical_var = selected_clinical_data
+    elif 'selected_clinical' in locals():
+        selected_clinical_var = selected_clinical
+
+    if selected_clinical_var is None:
+        # Fallback: record metadata as trait unavailable due to missing clinical dataframe
+        is_usable = validate_and_save_cohort_info(
+            is_final=True,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=True,
+            is_trait_available=False,
+            is_biased=False,
+            df=normalized_gene_data.T,
+            note="INFO: Clinical feature dataframe not found; skipping linking and marking trait as unavailable."
+        )
+    else:
+        # Link clinical and genetic data
+        linked_data = geo_link_clinical_genetic_data(selected_clinical_var, normalized_gene_data)
+
+        # Handle missing values
+        linked_data = handle_missing_values(linked_data, trait)
+
+        # Assess bias and remove biased demographic features
+        is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+        # Final validation and metadata saving
+        is_usable = validate_and_save_cohort_info(
+            is_final=True,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=True,
+            is_trait_available=True,
+            is_biased=is_trait_biased,
+            df=unbiased_linked_data,
+            note="INFO: Linked clinical and gene expression data with standard preprocessing."
+        )
+
+        # Save linked data only if usable
+        if is_usable:
+            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+            unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait not available in this cohort; skip linking and record metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=normalized_gene_data.T,
+        note="INFO: Trait unavailable (all samples are carotid atherosclerotic plaques; no variation). Gene data saved; linking skipped."
+    )

output/preprocess/Atherosclerosis/code/GSE133601.py

@@ -0,0 +1,259 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE133601"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE133601"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE133601.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE133601.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE133601.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step: Dataset Analysis and Clinical Feature Extraction for cohort GSE133601
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # PBMC transcriptome profiling (pre/post CPAP) suggests gene expression data are available.
+
+# 2) Variable Availability and Data Type Conversion
+
+# From the provided Sample Characteristics Dictionary:
+# {0: ['tissue: peripheral blood mononuclear cells'],
+#  1: ['subject: ...'],
+#  2: ['timepoint: pre-CPAP', 'timepoint: post-CPAP']}
+# There is no explicit or inferable Atherosclerosis status, age, or gender info.
+
+trait_row = None   # Atherosclerosis not available in this dataset
+age_row = None     # Age not available
+gender_row = None  # Gender not available
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    if s == '' or s.lower() in {'na', 'n/a', 'null', 'none', 'missing', 'unknown', 'undef', 'undetermined'}:
+        return None
+    return s
+
+# Define converters (even if not used) following the required interface.
+def convert_trait(x):
+    # Binary mapping for Atherosclerosis if any value is present in other datasets:
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    positive = {'atherosclerosis', 'yes', 'y', 'case', 'present', 'disease', 'true', '1', 'positive', 'pos'}
+    negative = {'no atherosclerosis', 'no', 'n', 'control', 'absent', 'healthy', 'false', '0', 'negative', 'neg'}
+    if vl in positive:
+        return 1
+    if vl in negative:
+        return 0
+    # Heuristics: mention of plaque, stenosis, CAD, ASCVD implies presence
+    if any(k in vl for k in ['plaque', 'stenosis', 'cad', 'ascvd', 'atheroma', 'carotid athero', 'mi history']):
+        return 1
+    if any(k in vl for k in ['no plaque', 'no cad', 'no ascvd']):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v = v.replace('years', '').replace('year', '').replace('yrs', '').replace('yr', '').replace('y', '').strip()
+    try:
+        return float(v)
+    except Exception:
+        # Try to extract leading numeric token
+        import re
+        m = re.search(r'[-+]?\d+(\.\d+)?', v)
+        if m:
+            try:
+                return float(m.group())
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Female -> 0, Male -> 1
+    if vl in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if vl in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    # Handle shorthand
+    if vl.startswith('fem'):
+        return 0
+    if vl.startswith('mal'):
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+selected_clinical_df = None
+clinical_preview = None
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# The identifiers like '10000_at' are Affymetrix probe set IDs, not HGNC gene symbols.
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the probe ID column in the annotation by maximizing overlap with expression probe IDs
+expr_probe_ids = set(gene_data.index.astype(str))
+
+best_id_col = None
+best_overlap = -1
+for col in gene_annotation.columns:
+    ann_ids = set(gene_annotation[col].astype(str).str.strip())
+    overlap = len(expr_probe_ids.intersection(ann_ids))
+    if overlap > best_overlap:
+        best_overlap = overlap
+        best_id_col = col
+
+# Determine the gene symbol column with a priority order
+def pick_gene_symbol_col(df: pd.DataFrame) -> str:
+    cols = list(df.columns)
+    # Priority 1: exact/close matches to gene symbol naming
+    for pat in [r'gene.?symbol', r'\bsymbol\b']:
+        for c in cols:
+            if re.search(pat, c, flags=re.IGNORECASE):
+                return c
+    # Priority 2: gene title/name/description as fallbacks
+    for pat in [r'gene.?title', r'gene.?name', r'description', r'title', r'name']:
+        for c in cols:
+            if re.search(pat, c, flags=re.IGNORECASE):
+                return c
+    # Fallback to the last column if nothing else (unlikely)
+    return cols[-1]
+
+gene_symbol_col = pick_gene_symbol_col(gene_annotation)
+
+# Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=best_id_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save to file
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Proceed only if clinical data (trait) is available; otherwise, finalize metadata without linking
+clinical_available = (
+    ('selected_clinical_df' in globals()) and
+    (selected_clinical_df is not None) and
+    isinstance(selected_clinical_df, pd.DataFrame) and
+    (trait in getattr(selected_clinical_df, 'index', []))
+)
+
+if clinical_available:
+    # 2. Link the clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine bias and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    note = "INFO: Clinical trait data available; linked data processed with QC."
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait is not available in this cohort; finalize metadata accordingly and do not save linked data
+    dummy_df = pd.DataFrame({'A': [1], 'B': [2], 'C': [3], 'D': [4], 'E': [5]})  # avoid abnormality override
+    note = "INFO: Atherosclerosis trait not available in sample characteristics; only gene expression data was saved."
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=dummy_df,
+        note=note
+    )

output/preprocess/Atherosclerosis/code/GSE154851.py

@@ -0,0 +1,258 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE154851"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE154851"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE154851.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE154851.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE154851.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Agilent Human Gene Expression microarray -> gene expression data present
+
+# 2) Variable availability based on Sample Characteristics Dictionary provided
+trait_row = None  # No explicit atherosclerosis status field found; cannot infer reliably
+age_row = 2
+gender_row = 1
+
+# 2.2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    parts = str(x).split(":", 1)
+    if len(parts) == 2:
+        return parts[1].strip()
+    return str(x).strip()
+
+def convert_trait(x):
+    # Generic binary converter for atherosclerosis if such a field exists; not used since trait_row is None
+    val = _after_colon(x)
+    if val is None:
+        return None
+    s = val.lower().strip()
+    # Positive indicators
+    pos_terms = {
+        "atherosclerosis", "has atherosclerosis", "plaque", "plaques", "cad", "coronary artery disease",
+        "case", "yes", "positive", "as", "athero", "with atherosclerosis"
+    }
+    neg_terms = {"no atherosclerosis", "no", "negative", "control", "healthy", "without atherosclerosis"}
+    if s in pos_terms:
+        return 1
+    if s in neg_terms:
+        return 0
+    # Heuristics
+    if "atherosclerosis" in s or "plaque" in s or "cad" in s:
+        return 1
+    if "no " in s and ("atherosclerosis" in s or "plaque" in s or "cad" in s):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    # Extract first number (e.g., "23y", "23 years")
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        # Filter unreasonable ages
+        if 0 < age < 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    s = val.lower().strip()
+    if s in {"female", "f", "woman", "women"}:
+        return 0
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Save metadata using initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row becomes available in future adjustments, uncomment and use:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+import os
+import re
+import pandas as pd
+
+def load_index():
+    # Prefer in-memory object if available
+    if 'gene_data_df' in globals() and isinstance(globals()['gene_data_df'], pd.DataFrame):
+        return globals()['gene_data_df'].index.astype(str)
+
+    # Try loading from saved file
+    try:
+        if os.path.exists(out_gene_data_file):
+            df = pd.read_csv(out_gene_data_file, index_col=0)
+            return df.index.astype(str)
+    except Exception:
+        pass
+
+    # Fallback to the provided preview index
+    return pd.Index([str(i) for i in range(1, 21)], name='ID')
+
+idx = load_index()
+
+def needs_mapping(index: pd.Index) -> bool:
+    vals = pd.Series(index.astype(str)).dropna().astype(str)
+    if len(vals) == 0:
+        return True
+
+    # Patterns for non-symbol identifiers
+    ens_pat = re.compile(r'^(ENSG|ENST|ENSMUSG|ENSMUST)\d+(\.\d+)?$')
+    refseq_pat = re.compile(r'^(NM_|NR_|XM_|XR_)\d+(\.\d+)?$')
+    affy_pat = re.compile(r'^\d+(_at|_st|_x_at|_s_at)$', re.IGNORECASE)
+    numeric_pat = re.compile(r'^\d+(\.\d+)?$')
+
+    def is_symbol(s: str) -> bool:
+        if ens_pat.match(s) or refseq_pat.match(s) or affy_pat.match(s) or numeric_pat.match(s):
+            return False
+        # Typical gene symbols are not purely numeric and contain letters; allow '-', numbers inside.
+        # Require at least one alphabetic character.
+        return any(c.isalpha() for c in s)
+
+    # If majority are not symbols => needs mapping
+    is_sym = vals.apply(is_symbol)
+    frac_symbol = is_sym.mean()
+    return frac_symbol < 0.5
+
+requires_gene_mapping = needs_mapping(idx)
+print(f"requires_gene_mapping = {str(requires_gene_mapping)}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping: expression uses numeric 'ID' matching annotation 'ID'; gene symbols are in 'GENE_SYMBOL'
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# 2. Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine if clinical data (with trait) is available
+has_clinical = ('selected_clinical_data' in globals() and 
+                isinstance(selected_clinical_data, pd.DataFrame))
+
+if has_clinical:
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Judge bias and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked and processed when clinical data was available."
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Clinical trait not available -> skip linking and record metadata appropriately
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Atherosclerosis/code/GSE57691.py

@@ -0,0 +1,216 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE57691"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE57691"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE57691.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE57691.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE57691.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import pandas as pd
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Genome-wide expression analysis indicates mRNA expression data
+
+# 2) Variable availability
+trait_row = 0
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    s = parts[1] if len(parts) > 1 else parts[0]
+    return s.strip().strip('"').strip("'")
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Positive (atherosclerosis): AOD (aortic occlusive disease)
+    if "aod" in v_low or "aortic occlusive" in v_low or "atherosclerosis" in v_low:
+        return 1
+    # Controls
+    if "control" in v_low or "donor" in v_low:
+        return 0
+    # AAA groups -> not the target trait; exclude
+    if "aaa" in v_low or "aneurysm" in v_low:
+        return None
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    # Extract first numeric token as age in years
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        # Basic plausibility check
+        if 0 < age < 120:
+            return age
+    except Exception:
+        return None
+    return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if "female" in v_low or v_low == "f":
+        return 0
+    if "male" in v_low or v_low == "m":
+        return 1
+    return None
+
+# 3) Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait_row is available)
+if trait_row is not None and 'clinical_data' in globals() and isinstance(globals()['clinical_data'], pd.DataFrame):
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+    # Save
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# ILMN_* identifiers are Illumina probe IDs, not human gene symbols
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for probe IDs and gene symbols from the annotation
+id_col = 'ID' if 'ID' in gene_annotation.columns else None
+gene_symbol_candidates = ['Symbol', 'ILMN_Gene', 'Gene Symbol', 'GeneSymbol']
+gene_col = next((c for c in gene_symbol_candidates if c in gene_annotation.columns), None)
+
+if id_col is None or gene_col is None:
+    raise ValueError(f"Could not find required columns in gene annotation. Found ID: {id_col}, Gene: {gene_col}")
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level expression to gene-level expression
+probe_data = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save gene expression matrix
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Ensure clinical data is available in memory; fallback to file if needed
+if 'selected_clinical_df' not in globals() or not isinstance(globals().get('selected_clinical_df'), pd.DataFrame):
+    try:
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    except FileNotFoundError as e:
+        raise FileNotFoundError(
+            f"Clinical data not found in memory and file not found at {out_clinical_data_file}."
+        ) from e
+
+# Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Determine whether the trait and demographics are biased; drop biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Conduct final quality validation and save cohort info with native Python bools
+is_gene_available_flag = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_flag = bool((trait in linked_data.columns) and linked_data[trait].notna().any())
+is_trait_biased_bool = bool(is_trait_biased)
+
+note = ("INFO: Trait derived from 'disease state': AOD mapped to atherosclerosis=1 and controls=0; "
+        "AAA/aneurysm samples were treated as missing and removed. Age/Gender not provided in the series matrix. "
+        "Illumina probes mapped to gene symbols with split-sum handling for multi-mapping.")
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased_bool,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Atherosclerosis/code/GSE83500.py

@@ -0,0 +1,129 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE83500"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE83500"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE83500.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE83500.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE83500.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Gene expression data availability
+is_gene_available = True  # Microarray mRNA expression per background info
+
+# 2) Variable availability and conversion functions
+
+# From the provided sample characteristics dictionary:
+# 0: ['individual: non-MI patient', 'individual: MI patient'] -> MI status, not our trait (Atherosclerosis)
+# 1: ['age: ...'] -> age available
+# 2: ['Sex: Male', 'Sex: Female'] -> gender available
+# There is no varying label indicating presence/absence of atherosclerosis in this cohort (all have CAD/atherosclerosis).
+trait_row = None
+age_row = 1
+gender_row = 2
+
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        val = val.strip()
+        return val if val not in {"", "NA", "N/A", "na", "n/a", "null", "None", "unknown", "Unknown"} else None
+    return x
+
+# Although trait is not available for this dataset (no controls without atherosclerosis),
+# we still define a robust converter for presence of atherosclerosis when possible.
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Heuristic: terms strongly implying atherosclerosis/CAD presence -> 1
+    pos_markers = ["atherosclerosis", "cad", "coronary artery disease", "ihd", "ischemic heart disease",
+                   "mi", "myocardial infarction", "stemi", "nstemi", "ua", "unstable angina", "stable"]
+    if any(m in v for m in pos_markers):
+        return 1
+    # Terms implying absence/healthy -> 0
+    neg_markers = ["control", "healthy", "no cad", "no atherosclerosis", "normal"]
+    if any(m in v for m in neg_markers):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    # Keep as continuous numeric
+    try:
+        # Remove potential units
+        val_clean = ''.join(ch for ch in val if ch.isdigit() or ch in ".-")
+        return float(val_clean) if val_clean not in {"", ".", "-", "--"} else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # female -> 0, male -> 1
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None for this dataset)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Atherosclerosis/code/GSE87005.py

@@ -0,0 +1,222 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE87005"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE87005"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE87005.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE87005.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE87005.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Transcriptomic profile in PBMCs indicates mRNA expression microarray data
+
+# 2) Variable availability and data type conversion
+# From the provided Sample Characteristics Dictionary:
+# {0: ['cell type: PBMC', 'group: Low HOMA', 'group: High HOMA'],
+#  1: ['cell type: PBMC', 'group: Low HOMA', 'group: High HOMA']}
+# No explicit atherosclerosis status, age, or gender fields are available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Conversion functions
+def _extract_value_after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    # Binary: Atherosclerosis present (1) vs absent (0)
+    v = _extract_value_after_colon(x).lower()
+    if v in ("", "na", "n/a", "nan", "none", "unknown", "not available"):
+        return None
+    # Positive indicators
+    pos_terms = {"atherosclerosis", "cad", "coronary artery disease", "plaque", "carotid atherosclerosis"}
+    # Negative/control indicators
+    neg_terms = {"control", "healthy", "normal", "no atherosclerosis", "without atherosclerosis", "absent"}
+    # Direct boolean-ish
+    if v in ("yes", "present", "positive", "pos", "case"):
+        return 1
+    if v in ("no", "absent", "negative", "neg", "ctrl", "control", "healthy"):
+        return 0
+    # Keyword inference
+    for t in pos_terms:
+        if t in v:
+            return 1
+    for t in neg_terms:
+        if t in v:
+            return 0
+    return None
+
+def convert_age(x):
+    # Continuous: age in years
+    v = _extract_value_after_colon(x).lower()
+    if v in ("", "na", "n/a", "nan", "none", "unknown", "not available"):
+        return None
+    # Extract first numeric token (handles formats like "45", "45 years", "45.0 yrs")
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    v = _extract_value_after_colon(x).lower()
+    if v in ("", "na", "n/a", "nan", "none", "unknown", "not available"):
+        return None
+    if v in ("male", "m", "man", "men", "1"):
+        return 1
+    if v in ("female", "f", "woman", "women", "0", "2"):
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction: skipped because trait_row is None (no clinical trait data available for atherosclerosis)
+# If in future a trait_row is identified, use the following template:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping: probe IDs ('ID') and gene symbols ('GENE_SYMBOL')
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Preserve probe-level data, then map to gene-level and overwrite gene_data as required
+probe_level_df = gene_data
+gene_data = apply_gene_mapping(probe_level_df, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1) Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Determine clinical trait availability and guard linking/validation accordingly
+try:
+    trait_available = (trait_row is not None)
+except NameError:
+    trait_available = False
+
+linked_data = None  # default when no clinical data is available
+
+if trait_available:
+    # Recompute clinical features to ensure availability in this step
+    selected_clinical_data = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # 2) Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Bias check and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final validation and cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data
+    )
+
+    # 6) Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # No trait data available: skip linking and final validation; ensure metadata correctly reflects unavailability
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Atherosclerosis/code/GSE90074.py

@@ -0,0 +1,238 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE90074"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE90074"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/GSE90074.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/GSE90074.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/GSE90074.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1. Gene Expression Data Availability
+# Based on the background information (Agilent Whole Human Genome arrays on PBMC RNA), gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Sample Characteristics Dictionary shows only one constant, non-informative entry, so no usable human trait/age/gender fields are available.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _extract_value(x: str) -> str:
+    if x is None:
+        return None
+    # Extract part after the first colon if present
+    parts = str(x).split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    val = val.strip()
+    return val if val != "" else None
+
+def convert_trait(x):
+    """
+    Convert trait (Atherosclerosis/CAD) to a binary indicator when possible.
+    Heuristics:
+      - map case/yes/present/disease to 1; control/no/absent to 0
+      - extract numeric severity if provided; if numeric, return as 1 if >0 else 0
+    Unknown -> None
+    """
+    val = _extract_value(x)
+    if val is None:
+        return None
+    low = val.lower()
+    # Explicit binary cues
+    if low in {"case", "cad", "atherosclerosis", "disease", "yes", "y", "present", "true", "positive", "pos"}:
+        return 1
+    if low in {"control", "no disease", "no", "n", "absent", "false", "negative", "neg", "healthy"}:
+        return 0
+    # Look for typical tokens
+    if any(tok in low for tok in ["case", "patient", "cad", "atheroscler", "stenosis", "plaque"]):
+        # If it sounds disease-like, lean towards case
+        return 1
+    if any(tok in low for tok in ["control", "healthy", "no cad", "no atheroscler"]):
+        return 0
+    # Try numeric
+    try:
+        num = float(re.findall(r"[-+]?\d*\.?\d+", low)[0])
+        # If a numeric severity/score exists, binarize >0 as diseased
+        return 1 if num > 0 else 0
+    except Exception:
+        return None
+
+def convert_age(x):
+    """
+    Convert age to continuous years. Extract first numeric token.
+    """
+    val = _extract_value(x)
+    if val is None:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group(0))
+        # Filter unreasonable ages
+        if 0 <= age_val <= 120:
+            return age_val
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Convert gender to binary: female->0, male->1. Unknown -> None.
+    """
+    val = _extract_value(x)
+    if val is None:
+        return None
+    low = val.lower()
+    if low in {"male", "m"}:
+        return 1
+    if low in {"female", "f"}:
+        return 0
+    # Handle common encodings
+    if "male" in low:
+        return 1
+    if "female" in low:
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (skip because trait_row is None)
+# If trait_row were available:
+if (trait_row is not None) and ('clinical_data' in globals()):
+    selected = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for mapping
+probe_id_col = 'ID'           # Matches probe identifiers in gene_data (e.g., A_23_P100001)
+gene_symbol_col = 'GENE_SYMBOL'  # Contains human gene symbols
+
+# 2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+probe_level_data = gene_data.copy()
+gene_data = apply_gene_mapping(expression_df=probe_level_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine if clinical data is available (from in-memory 'selected' or saved CSV)
+clinical_df = None
+if 'selected' in globals() and isinstance(selected, pd.DataFrame):
+    clinical_df = selected
+elif os.path.exists(out_clinical_data_file):
+    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# If no clinical data (trait) is available, skip linking/processing and record initial filtering result
+if clinical_df is None:
+    linked_data = None
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+else:
+    # 2. Link the clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait and demographic features are severely biased, and remove biased features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Conduct final quality validation and save the cohort information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked gene expression with available clinical features; applied QC and imputation."
+    )
+
+    # 6. If the linked data is usable, save it
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Atherosclerosis/code/TCGA.py

@@ -0,0 +1,60 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atherosclerosis/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Atherosclerosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atherosclerosis/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Atherosclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Find the most relevant TCGA cohort directory for Atherosclerosis (unlikely in TCGA; attempt matching)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_synonyms = [
+    "atherosclerosis", "atheroma", "artery", "arterial", "coronary", "cad",
+    "ischemic", "ischemia", "heart", "cardiac", "cardiovascular", "vascular"
+]
+def relevance_score(name: str) -> int:
+    lname = name.lower()
+    return sum(1 for s in trait_synonyms if s in lname)
+
+scored = [(d, relevance_score(d)) for d in subdirs]
+scored.sort(key=lambda x: x[1], reverse=True)
+selected_dir = scored[0][0] if scored and scored[0][1] > 0 else None
+
+if selected_dir is None:
+    print("No suitable TCGA cohort directory found for the trait 'Atherosclerosis'. Skipping.")
+    # Record as unavailable and exit early for this trait
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    clinical_df = pd.DataFrame()
+    genetic_df = pd.DataFrame()
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    # Step 2: Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+    print(f"Selected cohort: {selected_dir}")
+    print(f"Clinical file: {clinical_file_path}")
+    print(f"Genetic file: {genetic_file_path}")
+
+    # Step 3: Load files
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # Step 4: Print clinical data columns
+    print(list(clinical_df.columns))

output/preprocess/Atherosclerosis/cohort_info.json

@@ -1,82 +1 @@
-{
-  "GSE90074": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 143
-  },
-  "GSE87005": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 40
-  },
-  "GSE83500": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 37
-  },
-  "GSE57691": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 68
-  },
-  "GSE154851": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE133601": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE109048": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 57
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  }
-}
+{"GSE90074": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE87005": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE83500": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE57691": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 19, "note": "INFO: Trait derived from 'disease state': AOD mapped to atherosclerosis=1 and controls=0; AAA/aneurysm samples were treated as missing and removed. Age/Gender not provided in the series matrix. Illumina probes mapped to gene symbols with split-sum handling for multi-mapping."}, "GSE154851": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE133601": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Atherosclerosis trait not available in sample characteristics; only gene expression data was saved."}, "GSE125771": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait unavailable (all samples are carotid atherosclerotic plaques; no variation). Gene data saved; linking skipped."}, "GSE123088": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 63, "note": "INFO: Index does not resemble human gene symbols; normalization skipped and probe-level identifiers retained."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE162635.csv

@@ -1,2 +1,2 @@
-,GSM1,GSM2,GSM3,GSM4,GSM5,GSM6,GSM7,GSM8,GSM9,GSM10,GSM11,GSM12,GSM13,GSM14,GSM15,GSM16,GSM17,GSM18,GSM19,GSM20,GSM21,GSM22,GSM23,GSM24,GSM25,GSM26,GSM27,GSM28,GSM29,GSM30
-Chronic_obstructive_pulmonary_disease_(COPD),1.0,1.0,1.0,1.0,0.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+,GSM4955871,GSM4955872,GSM4955873,GSM4955874,GSM4955875,GSM4955876,GSM4955877,GSM4955878,GSM4955879,GSM4955880,GSM4955881,GSM4955882,GSM4955883,GSM4955884,GSM4955885,GSM4955886,GSM4955887,GSM4955888,GSM4955889,GSM4955890,GSM4955891,GSM4955892,GSM4955893,GSM4955894,GSM4955895,GSM4955896,GSM4955897,GSM4955898,GSM4955899,GSM4955900,GSM4955901,GSM4955902,GSM4955903,GSM4955904,GSM4955905,GSM4955906,GSM4955907,GSM4955908,GSM4955909,GSM4955910,GSM4955911,GSM4955912,GSM4955913,GSM4955914,GSM4955915,GSM4955916,GSM4955917,GSM4955918,GSM4955919,GSM4955920,GSM4955921,GSM4955922,GSM4955923,GSM4955924,GSM4955925,GSM4955926,GSM4955927,GSM4955928,GSM4955929,GSM4955930,GSM4955931,GSM4955932,GSM4955933,GSM4955934,GSM4955935,GSM4955936,GSM4955937,GSM4955938,GSM4955939,GSM4955940,GSM4955941,GSM4955942,GSM4955943,GSM4955944,GSM4955945,GSM4955946,GSM4955947,GSM4955948,GSM4955949,GSM4955950,GSM4955951,GSM4955952,GSM4955953,GSM4955954,GSM4955955,GSM4955956,GSM4955957,GSM4955958,GSM4955959,GSM4955960,GSM4955961,GSM4955962,GSM4955963,GSM4955964,GSM4955965,GSM4955966,GSM4955967,GSM4955968,GSM4955969,GSM4955970,GSM4955971,GSM4955972,GSM4955973,GSM4955974,GSM4955975,GSM4955976,GSM4955977,GSM4955978,GSM4955979,GSM4955980,GSM4955981,GSM4955982,GSM4955983,GSM4955984,GSM4955985,GSM4955986,GSM4955987,GSM4955988,GSM4955989,GSM4955990,GSM4955991,GSM4955992,GSM4955993,GSM4955994,GSM4955995,GSM4955996,GSM4955997,GSM4955998,GSM4955999,GSM4956000,GSM4956001,GSM4956002,GSM4956003,GSM4956004,GSM4956005,GSM4956006,GSM4956007,GSM4956008,GSM4956009,GSM4956010,GSM4956011,GSM4956012,GSM4956013,GSM4956014,GSM4956015,GSM4956016,GSM4956017,GSM4956018,GSM4956019,GSM4956020,GSM4956021,GSM4956022,GSM4956023,GSM4956024,GSM4956025,GSM4956026,GSM4956027,GSM4956028,GSM4956029,GSM4956030,GSM4956031,GSM4956032,GSM4956033,GSM4956034,GSM4956035,GSM4956036,GSM4956037,GSM4956038,GSM4956039,GSM4956040,GSM4956041,GSM4956042,GSM4956043,GSM4956044,GSM4956045,GSM4956046,GSM4956047,GSM4956048,GSM4956049,GSM4956050,GSM4956051,GSM4956052,GSM4956053,GSM4956054,GSM4956055,GSM4956056,GSM4956057,GSM4956058,GSM4956059,GSM4956060,GSM4956061,GSM4956062,GSM4956063,GSM4956064,GSM4956065,GSM4956066,GSM4956067,GSM4956068,GSM4956069,GSM4956070,GSM4956071,GSM4956072,GSM4956073,GSM4956074,GSM4956075
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output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE208662.csv

@@ -0,0 +1,2 @@
+,GSM6360778,GSM6360779,GSM6360780,GSM6360781,GSM6360782,GSM6360783,GSM6360784,GSM6360785,GSM6360786,GSM6360787,GSM6360788,GSM6360789,GSM6360790,GSM6360791,GSM6360792,GSM6360793,GSM6360794,GSM6360795,GSM6360796,GSM6360797,GSM6360798,GSM6360799,GSM6360800,GSM6360801,GSM6360802,GSM6360803,GSM6360804,GSM6360805,GSM6360806,GSM6360807,GSM6360808,GSM6360809
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output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE212331.csv

@@ -1,4 +1,4 @@
-0
-1.0
-75.0
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output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE21359.csv

@@ -1,4 +1,4 @@
-0,1,2,3
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output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE32030.csv

@@ -1,2 +1,2 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29
-1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,GSM549645,GSM549646,GSM549647,GSM549648,GSM549649,GSM549650,GSM549651,GSM549652,GSM549653,GSM549654,GSM549655,GSM549656,GSM549657,GSM549658,GSM549659,GSM549660,GSM549661,GSM549662,GSM549663,GSM549664,GSM549665,GSM549666,GSM549667,GSM549668,GSM549669,GSM549670,GSM549671,GSM549672,GSM549673,GSM549674,GSM549675,GSM549676,GSM549677,GSM549678,GSM549679,GSM549680,GSM549681,GSM549682,GSM549683,GSM549684,GSM549685,GSM549686,GSM549687,GSM549688,GSM549689,GSM549690,GSM549691,GSM549692,GSM549693,GSM549694,GSM549695,GSM549696,GSM549697,GSM549698,GSM549699,GSM549700,GSM549701,GSM549702,GSM549703,GSM549704,GSM549705,GSM549706,GSM549707,GSM549708,GSM549709,GSM549710,GSM549711,GSM549712,GSM549713,GSM549714,GSM549715,GSM549716,GSM549717,GSM549718,GSM549719,GSM549720,GSM549721,GSM549722,GSM549723,GSM549724,GSM549725,GSM549726,GSM549727,GSM549728,GSM549729,GSM549730,GSM549731,GSM549732,GSM549733,GSM549734,GSM549735,GSM549736,GSM549737,GSM549738,GSM549739,GSM549740,GSM549742,GSM549743,GSM549744,GSM549745,GSM549746,GSM549747,GSM549748,GSM549749,GSM549751,GSM549752,GSM549753,GSM549754,GSM549755,GSM549756,GSM549757,GSM549758,GSM549759,GSM549760,GSM549761,GSM549762,GSM549763,GSM549764,GSM549765,GSM549766,GSM549767,GSM549768,GSM549769,GSM549770,GSM549771,GSM549772,GSM549773,GSM549774,GSM549775,GSM549776,GSM549777,GSM549778,GSM549779,GSM549780,GSM549781,GSM549783,GSM549784,GSM549785,GSM549786,GSM549787,GSM549788,GSM549789,GSM549790,GSM549791,GSM549792,GSM549793,GSM549794,GSM549795,GSM549796,GSM549797,GSM549798,GSM549799,GSM549800,GSM549801,GSM549802,GSM549803,GSM549804,GSM549805,GSM549806,GSM549807,GSM549808,GSM549809,GSM549810,GSM549811,GSM549812,GSM549813,GSM549814,GSM549815,GSM549816,GSM549817,GSM549818,GSM549819,GSM549820,GSM549821,GSM569911,GSM569912,GSM569913,GSM569914,GSM569915,GSM569916,GSM569917,GSM569918,GSM569919,GSM569920,GSM569921,GSM569922,GSM569923,GSM569924,GSM569925,GSM569926,GSM599910,GSM599911,GSM599912,GSM599913,GSM599914,GSM599915,GSM599916,GSM599917,GSM599918,GSM599919,GSM599920,GSM599921,GSM631346,GSM631347,GSM631349,GSM631351,GSM631353,GSM631354,GSM631356,GSM631357,GSM631359,GSM631361,GSM631362,GSM631363,GSM631364,GSM631365,GSM631366,GSM631367,GSM631368,GSM631369,GSM631370,GSM631371,GSM631372,GSM631373,GSM631374,GSM631375,GSM631376,GSM631377,GSM631378,GSM631379,GSM631380,GSM631381,GSM631382,GSM631383,GSM631384,GSM631385,GSM631386,GSM631387,GSM631388,GSM631389,GSM631390,GSM631391,GSM794191,GSM794192,GSM794193,GSM794194,GSM794195,GSM794196,GSM794197,GSM794198,GSM794199,GSM794200,GSM794201,GSM794202,GSM794203,GSM794204,GSM794205,GSM794206,GSM794207,GSM794208,GSM794209,GSM794210,GSM794211,GSM794212,GSM794214,GSM794215,GSM794216,GSM794217,GSM794218,GSM794219
+Chronic_obstructive_pulmonary_disease_(COPD),1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE162635.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE162635"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE162635.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE162635.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression data availability (expression profiling in lung tissue)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# From the sample characteristics, GOLD stages are present under key 2 (gold.1)
+trait_row = 2  # 'gold.1' with values including healthy, O(0), I, II, III, IV
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    # Extract the portion after colon if present
+    val = s.split(':', 1)[1] if ':' in s else s
+    v = val.strip().lower()
+    # Normalize some possible variants
+    v = v.replace('stage', '').strip()
+    # Map GOLD stages: I-IV => COPD (1); healthy/0/O => non-COPD (0)
+    if v in {'i', 'ii', 'iii', 'iv'}:
+        return 1
+    if v in {'healthy', '0', 'o', 'none', 'no', 'control'}:
+        return 0
+    # Any other ambiguous/unknown goes to None
+    return None
+
+def convert_age(x):
+    # Not available in this dataset
+    return None
+
+def convert_gender(x):
+    # Not available in this dataset
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 1-2. Build the mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+probe_level_df = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_level_df, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features if any
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Trait derived from GOLD stages (gold.1): healthy/0/O mapped to 0; "
+        "stages I–IV mapped to 1. No age or gender available.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE175616.py

@@ -0,0 +1,220 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE175616"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE175616"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE175616.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE175616.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE175616.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # RNA expression profiling of nasal epithelium; not miRNA-only or methylation-only.
+
+# 2) Variable availability (from provided Sample Characteristics Dictionary)
+trait_row = None  # COPD status not provided; not inferable from available fields.
+age_row = 6       # 'age: <number>'
+gender_row = 5    # 'Sex: male'/'Sex: female'
+
+# 2.2) Conversion functions
+def _after_colon(value: any) -> str:
+    if value is None or (isinstance(value, float) and pd.isna(value)):
+        return ""
+    s = str(value)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(value: any):
+    # Binary: COPD (1) vs non-COPD (0). Not available here; robust parser for potential future use.
+    v = _after_colon(value).lower()
+    if v in {"copd", "yes", "case", "patient", "present", "true"}:
+        return 1
+    if v in {"no copd", "no", "control", "healthy", "normal", "absent", "false"}:
+        return 0
+    if "copd" in v:
+        return 1
+    return None
+
+def convert_age(value: any):
+    # Continuous age in years
+    v = _after_colon(value)
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value: any):
+    # Binary: female=0, male=1
+    v = _after_colon(value).lower()
+    if v in {"male", "m", "man"}:
+        return 1
+    if v in {"female", "f", "woman"}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2) Build mapping from probe IDs to gene symbols using columns in annotation
+probe_col = 'ID'            # Matches probe IDs in gene_data (e.g., '10000_at')
+gene_symbol_col = 'DESCRIPTION'  # Contains gene description; symbols will be extracted from this text
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+# Keep original probe-level data; then overwrite gene_data with gene-level
+probe_level_df = gene_data
+gene_data = apply_gene_mapping(probe_level_df, mapping_df)
+
+# Step 7: Gene Identifier Mapping
+# 1-2) Decide mapping columns and build mapping dataframe
+probe_col = 'ID'               # Probe identifiers match the gene_data row IDs (e.g., '10000_at')
+gene_symbol_col = 'DESCRIPTION'  # Gene symbol/description text; symbols will be extracted from this text
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Convert probe-level measurements to gene-level expression
+# Reload probe-level data to ensure correct mapping input
+probe_level_df = get_genetic_data(matrix_file)
+gene_data = apply_gene_mapping(probe_level_df, mapping_df)
+
+# Step 8: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine trait availability from previous step (trait_row was None)
+is_gene_available = True
+is_trait_available = False  # COPD status not available in this cohort per Step 2
+
+# Prepare a placeholder for linked_data to avoid NameError downstream
+linked_data = None
+
+if is_trait_available:
+    # If trait were available, ensure clinical features are present
+    if 'selected_clinical_data' not in globals():
+        try:
+            selected_clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+        except Exception as e:
+            raise RuntimeError("Clinical features expected but not found. Aborting linking.") from e
+
+    # 2) Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3) Missing value handling
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Bias check and remove biased covariates
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Trait available; standard preprocessing completed."
+    )
+
+    # 6) Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Trait not available: skip linking and downstream steps; still record metadata for gene data
+    # Use transposed gene data as df input for validation to pass shape checks
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available,
+        is_biased=False,  # Not applicable; set to False for record completeness
+        df=normalized_gene_data.T,
+        note="INFO: Trait not available in sample characteristics; only gene data saved."
+    )
+    # Do not save out_data_file since dataset is not usable for association without trait

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE208662.py

@@ -0,0 +1,198 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE208662"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE208662"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE208662.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE208662.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE208662.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Gene expression microarray analysis is explicitly stated
+
+# 2) Variable availability based on the provided Sample Characteristics Dictionary
+trait_row = 0  # 'disease state: COPD-IV' vs 'disease state: control'
+age_row = None  # Not provided
+gender_row = None  # Not provided
+
+# 2.2) Conversion functions
+def _after_colon(value):
+    if value is None:
+        return ""
+    s = str(value)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(value):
+    v = _after_colon(value).lower()
+    if v == "":
+        return None
+    # Positive trait (COPD)
+    if "copd" in v:
+        return 1
+    # Controls / non-disease
+    if any(k in v for k in ["control", "healthy", "non-cld", "non cld", "noncld", "no cld", "no lung disease", "normal"]):
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value).lower()
+    if v in {"", "na", "n/a", "nan", "none", "unknown"}:
+        return None
+    # extract first number (integer or float)
+    m = re.search(r"(-?\d+(\.\d+)?)", v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    v = _after_colon(value).lower()
+    if v in {"", "na", "n/a", "nan", "none", "unknown"}:
+        return None
+    if v in {"female", "f", "woman", "women", "girl"}:
+        return 0
+    if v in {"male", "m", "man", "men", "boy"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction
+if trait_row is not None:
+    # Ensure clinical_data exists
+    if 'clinical_data' not in globals():
+        raise RuntimeError("clinical_data DataFrame is not available in the environment.")
+
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide which columns to use for mapping
+prob_col = 'ID' if 'ID' in gene_annotation.columns else 'probeset_id'
+gene_col = 'SPOT_ID.1'
+
+# 2. Get the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Ensure selected_clinical_df is available; if not, load from saved file
+if 'selected_clinical_df' not in globals():
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise RuntimeError("Clinical features are not available in memory and the saved file is missing.")
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Dataset includes multiple treatment arms (Printex/Zn/LPS/Sham). "
+        "Only disease state (trait) is available as clinical covariate; age and gender not provided.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE210272.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE210272"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE210272"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE210272.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE210272.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE210272.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+from typing import Any, Optional
+
+# 1) Gene expression availability (Affymetrix Human Gene 1.0 ST Arrays => mRNA expression)
+is_gene_available = True
+
+# 2) Variable availability (from Sample Characteristics Dictionary)
+trait_row = None  # No explicit COPD status field found; cannot robustly infer from FEV1% alone
+age_row = 2       # 'age: ...'
+gender_row = 1    # 'Sex: Male/Female'
+
+# 2.2) Converters
+def _extract_value(cell: Any) -> str:
+    if cell is None:
+        return ''
+    s = str(cell)
+    parts = s.split(':', 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x: Any) -> Optional[int]:
+    v = _extract_value(x).lower()
+    if v in ('', 'na', 'n/a', 'none', 'unknown', 'not available'):
+        return None
+    if any(k in v for k in ['copd', 'chronic obstructive']):
+        if any(k in v for k in ['no copd', 'without copd', 'control', 'non-copd', 'healthy', 'normal']):
+            return 0
+        return 1
+    if 'case' in v and 'control' not in v:
+        return 1
+    if 'control' in v:
+        return 0
+    return None
+
+def convert_age(x: Any) -> Optional[float]:
+    v = _extract_value(x)
+    if v.lower() in ('', 'na', 'n/a', 'none', 'unknown'):
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    try:
+        return float(m.group()) if m else None
+    except Exception:
+        return None
+
+def convert_gender(x: Any) -> Optional[int]:
+    v = _extract_value(x).strip().lower()
+    if v in ('', 'na', 'n/a', 'none', 'unknown'):
+        return None
+    if v in ('male', 'm'):
+        return 1
+    if v in ('female', 'f'):
+        return 0
+    return None
+
+# 3) Save metadata with initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import re
+import pandas as pd
+
+# Use explicit columns based on the annotation preview
+id_col = 'ID'
+gene_col = 'ORF'
+
+# Build mapping dataframe and restrict to probes present in expression data
+mapping_df = gene_annotation[[id_col, gene_col]].dropna().copy()
+mapping_df[id_col] = mapping_df[id_col].astype(str).str.strip()
+mapping_df[gene_col] = mapping_df[gene_col].astype(str).str.strip()
+mapping_df = mapping_df[mapping_df[id_col].isin(gene_data.index)]
+
+# Split possible multi-mapped entries; keep only valid Ensembl IDs
+def split_genes(s: str):
+    s = s.strip().strip('"').strip("'")
+    if s == '' or s.lower() in ('na', 'n/a', 'none', 'nan'):
+        return []
+    parts = re.split(r'\s*(?:/{2,3}|;|,|\|)\s*', s)
+    return [p for p in parts if p]
+
+mapping_df['Gene'] = mapping_df[gene_col].apply(split_genes)
+mapping_df = mapping_df.explode('Gene').dropna(subset=['Gene'])
+# Keep canonical Ensembl gene IDs
+mapping_df = mapping_df[mapping_df['Gene'].str.match(r'^ENSG\d+$')]
+
+# If nothing left after filtering, raise a clear error
+if mapping_df.empty:
+    raise ValueError("No valid Ensembl mappings found in annotation. Check annotation columns and contents.")
+
+# Count mappings per probe for equal split, then join to expression and aggregate
+mapping_df['num_genes'] = mapping_df.groupby(id_col)[id_col].transform('count')
+mapping_df = mapping_df.set_index(id_col)
+
+# Join mapping with expression data and distribute probe signal equally among mapped genes
+merged = mapping_df[['Gene', 'num_genes']].join(gene_data, how='inner')
+expr_cols = gene_data.columns.tolist()
+merged[expr_cols] = merged[expr_cols].div(merged['num_genes'].replace(0, 1), axis=0)
+
+# Aggregate to gene-level
+gene_level = merged.groupby('Gene')[expr_cols].sum()
+
+# Sanity checks to avoid corrupted indices
+if gene_level.shape[0] == 0:
+    raise ValueError("Mapped gene-level matrix is empty after aggregation.")
+if not gene_level.index.to_series().str.startswith('ENSG').all():
+    bad_examples = gene_level.index[~gene_level.index.to_series().str.startswith('ENSG')][:5].tolist()
+    raise ValueError(f"Unexpected gene identifiers detected (examples: {bad_examples}).")
+
+# Assign result and print basic info
+gene_data = gene_level
+print(f"Gene-level data shape: {gene_data.shape}")
+print(f"First 5 gene IDs: {list(gene_data.index[:5])}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE212331.py

@@ -0,0 +1,203 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE212331"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE212331.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE212331.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on series summary: gene expression profiles from induced sputum
+
+# 2. Variable Availability and Data Type Conversion
+
+# Keys from Sample Characteristics Dictionary
+trait_row = 1   # 'disease group: COPD' vs 'disease group: Healthy Control'
+age_row = 3     # 'age: <number>'
+gender_row = 4  # 'gender: Male'/'gender: Female'
+
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map COPD cases to 1, healthy controls to 0
+    if "copd" in vl:
+        return 1
+    if ("healthy" in vl and "control" in vl) or vl in {"control", "healthy", "hc", "ctrl"}:
+        return 0
+    if vl in {"case", "patient", "disease"}:
+        return 1
+    if vl in {"normal"}:
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"na", "n/a", "nan", ""}:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        num = float(m.group())
+        return num
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    if "male" in vl or vl == "m":
+        return 1
+    if "female" in vl or vl == "f":
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns in the annotation for probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'Symbol'
+
+# Build the mapping dataframe (probe -> gene symbol)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply the mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    selected_clinical_df
+except NameError:
+    # Load previously saved clinical data if not in memory
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available = (normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+is_trait_available = (trait in linked_data.columns)
+note = "INFO: Illumina probe-level data mapped to symbols; sputum samples from COPD vs healthy controls."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE21359.py

@@ -0,0 +1,197 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE21359"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE21359.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+import numpy as np
+
+# 1) Gene expression data availability (Affymetrix expression arrays per background info)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# Rows determined from the provided Sample Characteristics Dictionary
+trait_row = 3    # smoking status field contains COPD info
+age_row = 0      # age
+gender_row = 1   # sex
+
+def _after_colon(value: str) -> str:
+    if value is None or (isinstance(value, float) and np.isnan(value)):
+        return ""
+    parts = str(value).split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(value):
+    # Binary: COPD (1) vs non-COPD (0)
+    try:
+        v = _after_colon(value).lower()
+        if v == "":
+            return None
+        if "copd" in v:
+            return 1
+        # If explicitly non-smoker or smoker without COPD mentioned => non-COPD
+        if "non-smoker" in v or "nonsmoker" in v or "smoker" in v:
+            return 0
+        # Heuristic: absence of 'copd' implies non-copd for this field
+        return 0
+    except Exception:
+        return None
+
+def convert_age(value):
+    # Continuous
+    try:
+        v = _after_colon(value)
+        if v == "":
+            return None
+        # Extract first integer number
+        m = re.search(r"[-+]?\d+\.?\d*", v)
+        if m:
+            return float(m.group())
+        return None
+    except Exception:
+        return None
+
+def convert_gender(value):
+    # Binary: female=0, male=1
+    try:
+        v = _after_colon(value).strip().lower()
+        if v in ("m", "male"):
+            return 1
+        if v in ("f", "female"):
+            return 0
+        return None
+    except Exception:
+        return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if clinical data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print(clinical_preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the probe ID column and the gene symbol column in the annotation
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Map probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort metadata
+# Ensure native Python bools to avoid JSON serialization issues
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+
+note = "INFO: Affymetrix platform; probes mapped to gene symbols via GPL; COPD inferred from smoking status (COPD vs non-COPD)."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=bool(is_gene_available),
+    is_trait_available=bool(is_trait_available),
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if bool(is_usable):
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE32030.py

@@ -0,0 +1,119 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE32030"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE32030"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE32030.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE32030.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE32030.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability (microarray gene expression described in background)
+is_gene_available = True
+
+# 2) Variable availability and converters
+
+# COPD status only appears as positive ("copd status: yes") without explicit negatives,
+# making the trait effectively a constant/one-class under the available keys.
+# Per instructions, treat as not available for association analysis.
+trait_row = None
+
+# No explicit age or gender information is present in the dictionary.
+age_row = None
+gender_row = None
+
+# Converters (defined but unused since trait/age/gender rows are unavailable)
+def _extract_after_colon(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        parts = s.split(':', 1)
+        key = parts[0].strip().lower()
+        val = parts[1].strip().lower()
+        return key, val
+    else:
+        return None
+
+def convert_trait(x):
+    kv = _extract_after_colon(x)
+    if not kv:
+        return None
+    key, val = kv
+    if 'copd' in key:
+        if val in {'yes', 'y', 'positive', 'pos'}:
+            return 1
+        if val in {'no', 'n', 'negative', 'neg'}:
+            return 0
+        if val in {'healthy', 'control'}:
+            return 0
+    return None
+
+def convert_age(x):
+    kv = _extract_after_colon(x)
+    if not kv:
+        return None
+    _, val = kv
+    m = re.search(r'(\d+(\.\d+)?)', val)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    kv = _extract_after_colon(x)
+    if not kv:
+        return None
+    _, val = kv
+    v = val.strip().lower()
+    if v in {'male', 'm'}:
+        return 1
+    if v in {'female', 'f'}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction is skipped because trait_row is None (trait not available)

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE64593.py

@@ -0,0 +1,104 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE64593"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64593"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64593.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE64593.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64593.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine gene expression availability based on series description (Affymetrix expression profiling)
+is_gene_available = True
+
+# Variable availability based on Sample Characteristics Dictionary
+# {0: ['smoking status: smoker'], 1: ['disease state: HIV-', 'disease state: HIV+'], 2: ['cell type: alveolar macrophage']}
+# No explicit or inferable COPD status; no age; no gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Converters (defined for completeness; not used since corresponding rows are None)
+def _extract_value(x):
+    if x is None:
+        return None
+    try:
+        s = str(x)
+        if ':' in s:
+            s = s.split(':', 1)[1]
+        return s.strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    # Map COPD-related labels to binary if present; otherwise None
+    v = _extract_value(x)
+    if not v:
+        return None
+    vl = v.lower()
+    case_tokens = ['copd', 'emphysema', 'low dlco', 'obstructive']
+    control_tokens = ['healthy', 'normal', 'no copd', 'non-copd']
+    if any(t in vl for t in case_tokens):
+        return 1
+    if any(t in vl for t in control_tokens):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    # extract first number as age
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    vl = v.lower()
+    if vl in ['female', 'f', 'woman', 'women']:
+        return 0
+    if vl in ['male', 'm', 'man', 'men']:
+        return 1
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(False, cohort, json_path, is_gene_available, is_trait_available)
+
+# Clinical feature extraction skipped because trait_row is None

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE64599.py

@@ -0,0 +1,203 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE64599"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE64599.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64599.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression availability based on provided background
+# The series focuses on gene expression in alveolar macrophages; no indication it's miRNA-only or methylation-only.
+is_gene_available = True
+
+# Step 2: Variable availability and conversion functions
+
+# From the provided Sample Characteristics Dictionary:
+# {0: ['smoking status: smoker'], 
+#  1: ['disease state: HIV-', 'disease state: HIV+'], 
+#  2: ['cell type: alveolar macrophage']}
+# There is no COPD status, age, or gender. Smoking status is constant and not our trait. Therefore:
+trait_row = None
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[-1] if len(parts) > 1 else s
+    val = val.strip()
+    return val if val != "" else None
+
+def convert_trait(x):
+    # Binary: 1 = COPD/emphysema case, 0 = control/non-COPD
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Heuristics in case trait wording varies
+    # Positive indicators
+    pos_keywords = ["copd", "emphysema"]
+    neg_keywords = ["control", "healthy", "normal", "no copd", "non-copd", "without copd", "no evidence of copd"]
+    if any(k in v for k in pos_keywords):
+        if any(k in v for k in neg_keywords) or "no " in v or "non-" in v:
+            return 0
+        return 1
+    if v in {"case", "patient", "diseased"}:
+        return 1
+    if v in {"control", "healthy", "normal"}:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years (float)
+    val = _extract_value(x)
+    if val is None:
+        return None
+    import re
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"male", "m", "man", "boy"}:
+        return 1
+    if v in {"female", "f", "woman", "girl"}:
+        return 0
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (skip because trait_row is None)
+# If trait_row becomes available in future updates, the below block can be enabled.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Affymetrix probe set IDs (e.g., '1007_s_at') are not gene symbols and require mapping.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Decide columns and get mapping dataframe
+probe_col = 'ID'  # Matches probe IDs in gene expression data (e.g., '1007_s_at')
+gene_symbol_col = 'Gene Symbol'  # Contains human gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. If clinical data with trait is unavailable, skip linking and final validation; record metadata accordingly.
+if 'selected_clinical_data' in globals() and selected_clinical_data is not None:
+    # Proceed only if trait data exists (i.e., selected_clinical_data was created in Step 2)
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+    linked_data = handle_missing_values(linked_data, trait)
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Gene data normalized and linked with available clinical features."
+    )
+
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait unavailable in this cohort; log and do not attempt linking or saving linked data
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE84046.py

@@ -0,0 +1,120 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE84046"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE84046"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE84046.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE84046.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE84046.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine data availability based on provided background and sample characteristics
+is_gene_available = True  # Whole-genome gene expression in adipose tissue (not miRNA/methylation)
+trait_row = None  # COPD status not provided; dataset is dietary intervention, not COPD-specific
+age_row = None    # Only date of birth provided; no sampling date to compute age reliably
+gender_row = 4    # 'sexe: Male/Female' present
+
+# Converters
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) == 2 else x.strip()
+    return x
+
+def convert_trait(x):
+    # Binary: 1 = COPD, 0 = control/non-COPD
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    # Heuristics for case/control labels
+    if "copd" in v:
+        # Exclude negations like "non-copd"
+        if "non-copd" in v or "no copd" in v:
+            return 0
+        return 1
+    if any(k in v for k in ["control", "healthy", "non-copd", "normal", "no copd"]):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous. Try numeric; if date-of-birth or non-numeric, return None.
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip()
+    # If looks like a date (e.g., dd-mm-yyyy), we don't have sampling date; return None.
+    if "-" in s and any(ch.isdigit() for ch in s):
+        return None
+    try:
+        return float(s)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    if v in ["female", "f", "woman", "women"]:
+        return 0
+    if v in ["male", "m", "man", "men"]:
+        return 1
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction: skip because trait is not available
+# If trait_row becomes available in future updates, uncomment and use the following:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/TCGA.py

@@ -0,0 +1,73 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most appropriate TCGA cohort directory for COPD
+all_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+dirs_lower = {d: d.lower() for d in all_dirs}
+
+# Define strict COPD-related terms
+copd_terms = [
+    "copd",
+    "chronic_obstructive_pulmonary_disease",
+    "chronic-obstructive-pulmonary-disease",
+    "chronic obstructive pulmonary disease"
+]
+
+# Exclude clear cancer-specific cohorts even if they mention "lung"
+exclude_terms = [
+    "cancer", "carcinoma", "adenocarcinoma", "squamous", "glioma", "sarcoma", "melanoma",
+    "leukemia", "lymphoma", "tumor", "neoplasm", "blastoma"
+]
+
+selected_dir = None
+for d, dl in dirs_lower.items():
+    if any(term in dl for term in copd_terms):
+        if not any(ex in dl for ex in exclude_terms):
+            selected_dir = d
+            break
+
+# If no suitable directory found, skip this trait and mark task as completed
+if selected_dir is None:
+    # Record metadata that TCGA is not suitable for COPD
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Step 2: Identify clinical and genetic file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    clinical_kwargs = {"sep": "\t", "index_col": 0, "low_memory": False}
+    genetic_kwargs = {"sep": "\t", "index_col": 0, "low_memory": False}
+    if clinical_file_path.endswith(".gz"):
+        clinical_kwargs["compression"] = "gzip"
+    if genetic_file_path.endswith(".gz"):
+        genetic_kwargs["compression"] = "gzip"
+
+    clinical_df = pd.read_csv(clinical_file_path, **clinical_kwargs)
+    genetic_df = pd.read_csv(genetic_file_path, **genetic_kwargs)
+
+    # Step 4: Print the column names of the clinical data
+    print(list(clinical_df.columns))

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE84046": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE64599": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE64593": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE32030": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 35
-  },
-  "GSE21359": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 135
-  },
-  "GSE212331": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 87
-  },
-  "GSE210272": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE208662": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 32
-  },
-  "GSE175616": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE162635": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 1129
-  }
-}
+{"GSE84046": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE64599": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE64593": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE32030": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE21359": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 135, "note": "INFO: Affymetrix platform; probes mapped to gene symbols via GPL; COPD inferred from smoking status (COPD vs non-COPD)."}, "GSE212331": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 87, "note": "INFO: Illumina probe-level data mapped to symbols; sputum samples from COPD vs healthy controls."}, "GSE210272": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE208662": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 32, "note": "INFO: Dataset includes multiple treatment arms (Printex/Zn/LPS/Sham). Only disease state (trait) is available as clinical covariate; age and gender not provided."}, "GSE175616": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait not available in sample characteristics; only gene data saved."}, "GSE162635": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 205, "note": "INFO: Trait derived from GOLD stages (gold.1): healthy/0/O mapped to 0; stages I\u2013IV mapped to 1. No age or gender available."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE210272.csv

@@ -1 +1 @@
-ID,GSM6427241,GSM6427242,GSM6427243,GSM6427244,GSM6427245,GSM6427246,GSM6427247,GSM6427248,GSM6427249,GSM6427250,GSM6427251,GSM6427252,GSM6427253,GSM6427254,GSM6427255,GSM6427256,GSM6427257,GSM6427258,GSM6427259,GSM6427260,GSM6427261,GSM6427262,GSM6427263,GSM6427264,GSM6427265,GSM6427266,GSM6427267,GSM6427268,GSM6427269,GSM6427270,GSM6427271,GSM6427272,GSM6427273,GSM6427274,GSM6427275,GSM6427276,GSM6427277,GSM6427278,GSM6427279,GSM6427280,GSM6427281,GSM6427282,GSM6427283,GSM6427284,GSM6427285,GSM6427286,GSM6427287,GSM6427288,GSM6427289,GSM6427290,GSM6427291,GSM6427292,GSM6427293,GSM6427294,GSM6427295,GSM6427296,GSM6427297,GSM6427298,GSM6427299,GSM6427300,GSM6427301,GSM6427302,GSM6427303,GSM6427304,GSM6427305,GSM6427306,GSM6427307,GSM6427308,GSM6427309,GSM6427310,GSM6427311,GSM6427312,GSM6427313,GSM6427314,GSM6427315,GSM6427316,GSM6427317,GSM6427318,GSM6427319,GSM6427320,GSM6427321,GSM6427322,GSM6427323,GSM6427324,GSM6427325,GSM6427326,GSM6427327,GSM6427328,GSM6427329,GSM6427330,GSM6427331,GSM6427332,GSM6427333,GSM6427334,GSM6427335,GSM6427336,GSM6427337,GSM6427338,GSM6427339,GSM6427340,GSM6427341,GSM6427342,GSM6427343,GSM6427344,GSM6427345,GSM6427346,GSM6427347,GSM6427348,GSM6427349,GSM6427350,GSM6427351,GSM6427352,GSM6427353,GSM6427354,GSM6427355,GSM6427356,GSM6427357,GSM6427358,GSM6427359,GSM6427360,GSM6427361,GSM6427362,GSM6427363,GSM6427364,GSM6427365,GSM6427366,GSM6427367,GSM6427368,GSM6427369,GSM6427370,GSM6427371,GSM6427372,GSM6427373,GSM6427374,GSM6427375,GSM6427376,GSM6427377,GSM6427378,GSM6427379,GSM6427380,GSM6427381,GSM6427382,GSM6427383,GSM6427384,GSM6427385,GSM6427386,GSM6427387,GSM6427388,GSM6427389,GSM6427390,GSM6427391,GSM6427392,GSM6427393,GSM6427394,GSM6427395,GSM6427396,GSM6427397,GSM6427398,GSM6427399,GSM6427400,GSM6427401,GSM6427402,GSM6427403,GSM6427404,GSM6427405,GSM6427406,GSM6427407,GSM6427408,GSM6427409,GSM6427410,GSM6427411,GSM6427412,GSM6427413,GSM6427414,GSM6427415,GSM6427416,GSM6427417,GSM6427418,GSM6427419,GSM6427420,GSM6427421,GSM6427422,GSM6427423,GSM6427424,GSM6427425,GSM6427426,GSM6427427,GSM6427428,GSM6427429,GSM6427430,GSM6427431,GSM6427432,GSM6427433,GSM6427434,GSM6427435,GSM6427436,GSM6427437,GSM6427438,GSM6427439,GSM6427440,GSM6427441,GSM6427442,GSM6427443,GSM6427444,GSM6427445,GSM6427446,GSM6427447,GSM6427448,GSM6427449,GSM6427450,GSM6427451,GSM6427452,GSM6427453,GSM6427454,GSM6427455,GSM6427456,GSM6427457,GSM6427458,GSM6427459,GSM6427460,GSM6427461,GSM6427462,GSM6427463,GSM6427464,GSM6427465,GSM6427466,GSM6427467,GSM6427468,GSM6427469,GSM6427470,GSM6427471,GSM6427472,GSM6427473,GSM6427474,GSM6427475,GSM6427476,GSM6427477,GSM6427478
+Gene,GSM6427241,GSM6427242,GSM6427243,GSM6427244,GSM6427245,GSM6427246,GSM6427247,GSM6427248,GSM6427249,GSM6427250,GSM6427251,GSM6427252,GSM6427253,GSM6427254,GSM6427255,GSM6427256,GSM6427257,GSM6427258,GSM6427259,GSM6427260,GSM6427261,GSM6427262,GSM6427263,GSM6427264,GSM6427265,GSM6427266,GSM6427267,GSM6427268,GSM6427269,GSM6427270,GSM6427271,GSM6427272,GSM6427273,GSM6427274,GSM6427275,GSM6427276,GSM6427277,GSM6427278,GSM6427279,GSM6427280,GSM6427281,GSM6427282,GSM6427283,GSM6427284,GSM6427285,GSM6427286,GSM6427287,GSM6427288,GSM6427289,GSM6427290,GSM6427291,GSM6427292,GSM6427293,GSM6427294,GSM6427295,GSM6427296,GSM6427297,GSM6427298,GSM6427299,GSM6427300,GSM6427301,GSM6427302,GSM6427303,GSM6427304,GSM6427305,GSM6427306,GSM6427307,GSM6427308,GSM6427309,GSM6427310,GSM6427311,GSM6427312,GSM6427313,GSM6427314,GSM6427315,GSM6427316,GSM6427317,GSM6427318,GSM6427319,GSM6427320,GSM6427321,GSM6427322,GSM6427323,GSM6427324,GSM6427325,GSM6427326,GSM6427327,GSM6427328,GSM6427329,GSM6427330,GSM6427331,GSM6427332,GSM6427333,GSM6427334,GSM6427335,GSM6427336,GSM6427337,GSM6427338,GSM6427339,GSM6427340,GSM6427341,GSM6427342,GSM6427343,GSM6427344,GSM6427345,GSM6427346,GSM6427347,GSM6427348,GSM6427349,GSM6427350,GSM6427351,GSM6427352,GSM6427353,GSM6427354,GSM6427355,GSM6427356,GSM6427357,GSM6427358,GSM6427359,GSM6427360,GSM6427361,GSM6427362,GSM6427363,GSM6427364,GSM6427365,GSM6427366,GSM6427367,GSM6427368,GSM6427369,GSM6427370,GSM6427371,GSM6427372,GSM6427373,GSM6427374,GSM6427375,GSM6427376,GSM6427377,GSM6427378,GSM6427379,GSM6427380,GSM6427381,GSM6427382,GSM6427383,GSM6427384,GSM6427385,GSM6427386,GSM6427387,GSM6427388,GSM6427389,GSM6427390,GSM6427391,GSM6427392,GSM6427393,GSM6427394,GSM6427395,GSM6427396,GSM6427397,GSM6427398,GSM6427399,GSM6427400,GSM6427401,GSM6427402,GSM6427403,GSM6427404,GSM6427405,GSM6427406,GSM6427407,GSM6427408,GSM6427409,GSM6427410,GSM6427411,GSM6427412,GSM6427413,GSM6427414,GSM6427415,GSM6427416,GSM6427417,GSM6427418,GSM6427419,GSM6427420,GSM6427421,GSM6427422,GSM6427423,GSM6427424,GSM6427425,GSM6427426,GSM6427427,GSM6427428,GSM6427429,GSM6427430,GSM6427431,GSM6427432,GSM6427433,GSM6427434,GSM6427435,GSM6427436,GSM6427437,GSM6427438,GSM6427439,GSM6427440,GSM6427441,GSM6427442,GSM6427443,GSM6427444,GSM6427445,GSM6427446,GSM6427447,GSM6427448,GSM6427449,GSM6427450,GSM6427451,GSM6427452,GSM6427453,GSM6427454,GSM6427455,GSM6427456,GSM6427457,GSM6427458,GSM6427459,GSM6427460,GSM6427461,GSM6427462,GSM6427463,GSM6427464,GSM6427465,GSM6427466,GSM6427467,GSM6427468,GSM6427469,GSM6427470,GSM6427471,GSM6427472,GSM6427473,GSM6427474,GSM6427475,GSM6427476,GSM6427477,GSM6427478

output/preprocess/Fibromyalgia/code/GSE67311.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Fibromyalgia"
+cohort = "GSE67311"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Fibromyalgia"
+in_cohort_dir = "../DATA/GEO/Fibromyalgia/GSE67311"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Fibromyalgia/GSE67311.csv"
+out_gene_data_file = "./output/z3/preprocess/Fibromyalgia/gene_data/GSE67311.csv"
+out_clinical_data_file = "./output/z3/preprocess/Fibromyalgia/clinical_data/GSE67311.csv"
+json_path = "./output/z3/preprocess/Fibromyalgia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix Human Gene 1.1 ST arrays indicate gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Rows determined from the provided Sample Characteristics Dictionary
+trait_row = 0          # 'diagnosis: healthy control' / 'diagnosis: fibromyalgia'
+age_row = None         # Not available
+gender_row = None      # Not available
+
+# Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Map fibromyalgia cases to 1, controls to 0
+    if v_low in {'fibromyalgia', 'fm', 'patient', 'case'} or 'fibro' in v_low:
+        return 1
+    if v_low in {'healthy control', 'control', 'hc', 'healthy', 'normal'} or 'control' in v_low:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if v_low in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    if v_low in {'0', '1'}:
+        try:
+            return int(v_low)
+        except Exception:
+            return None
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide the columns for probe IDs and gene symbols based on the annotation preview:
+# - Probe IDs match the 'ID' column (e.g., 789xxxx).
+# - Gene symbols can be parsed from the 'gene_assignment' column.
+id_col = 'ID'
+symbol_col = 'gene_assignment'
+
+# Build mapping and apply it to convert probe-level data to gene-level expression
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=symbol_col)
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# Ensure clinical data is available in memory; reload if needed
+if 'selected_clinical_df' not in locals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check bias and remove biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Platform=Affymetrix Human Gene 1.1 ST; Age/Gender unavailable; Trait derived from 'diagnosis'."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Fibromyalgia/code/TCGA.py

@@ -0,0 +1,64 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Fibromyalgia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Fibromyalgia/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Fibromyalgia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Fibromyalgia/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Fibromyalgia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA cohort directory for Fibromyalgia (likely none)
+synonym_terms = [
+    'fibromyalgia',
+    'myalgia',
+    'chronic pain',
+    'central sensitization',
+    'musculoskeletal pain'
+]
+
+# List available subdirectories
+all_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Find matches
+matches = []
+for d in all_subdirs:
+    name_l = d.lower()
+    score = sum(term in name_l for term in synonym_terms)
+    if score > 0:
+        matches.append((score, d))
+
+if not matches:
+    # No suitable TCGA cohort for Fibromyalgia; record and skip
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Choose the most specific match (highest score, then longest matched directory name)
+    matches.sort(key=lambda x: (x[0], len(x[1])), reverse=True)
+    selected_dir = matches[0][1]
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # Step 2: Identify clinical and genetic file paths
+    clinical_fp, genetic_fp = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files
+    clinical_df = pd.read_csv(clinical_fp, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_fp, sep='\t', index_col=0, low_memory=False)
+
+    # Step 4: Print clinical column names
+    print(clinical_df.columns.tolist())

output/preprocess/Fibromyalgia/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE67311": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 142
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  }
-}
+{"GSE67311": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 142, "note": "INFO: Platform=Affymetrix Human Gene 1.1 ST; Age/Gender unavailable; Trait derived from 'diagnosis'."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Hypertension/code/GSE149256.py

@@ -0,0 +1,126 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+cohort = "GSE149256"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertension"
+in_cohort_dir = "../DATA/GEO/Hypertension/GSE149256"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/GSE149256.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/GSE149256.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/GSE149256.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Microarray gene expression in PBMCs per background info
+
+# Step 2: Identify variable availability from the sample characteristics dictionary
+# Based on the provided dictionary:
+# 0: gender, 1: ethnicity, 2: poverty status, 3: age, 4: tissue
+trait_row = None          # Hypertension is not available in this dataset
+age_row = 3               # 'age: <value>'
+gender_row = 0            # 'gender: Male/Female'
+
+# Step 2.2: Define conversion functions
+def _after_colon(value: str) -> str:
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    v = v.strip()
+    return v if v != "" else None
+
+def convert_trait(value):
+    # Robust parser for hypertension if it ever appears; returns None otherwise
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    yes_set = {"yes", "y", "1", "true", "hypertensive", "htn", "case", "with hypertension", "hypertension"}
+    no_set = {"no", "n", "0", "false", "normotensive", "control", "without hypertension", "non-hypertension"}
+    if vl in yes_set:
+        return 1
+    if vl in no_set:
+        return 0
+    # Heuristics for common phrasing
+    if "hypertens" in vl and ("no" in vl or "without" in vl or "non" in vl):
+        return 0
+    if "hypertens" in vl:
+        return 1
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    try:
+        # Some GEO ages can include units or extra text; extract leading numeric
+        return float(v)
+    except Exception:
+        # Try to parse numbers embedded in text
+        import re
+        m = re.search(r"[-+]?\d+(\.\d+)?", v)
+        return float(m.group()) if m else None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"male", "m"}:
+        return 1
+    if vl in {"female", "f"}:
+        return 0
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (skip if trait not available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Hypertension/code/GSE151158.py

@@ -0,0 +1,187 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+cohort = "GSE151158"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertension"
+in_cohort_dir = "../DATA/GEO/Hypertension/GSE151158"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/GSE151158.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/GSE151158.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/GSE151158.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+# Based on series summary describing RNA from liver tissue with expression of 594 genes.
+is_gene_available = True
+
+# 2) Variable availability
+trait_row = 7        # 'hypertension: N'/'Y'
+age_row = 1          # 'age: ...'
+gender_row = 2       # 'Sex: F'/'M'
+
+# 2) Data type conversion functions
+def _value_after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) == 2 else parts[0]
+    return v.strip()
+
+def convert_trait(x):
+    v = _value_after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.strip().lower()
+    # Map common binary representations
+    if v_low in {"y", "yes", "1", "true", "positive", "pos"}:
+        return 1
+    if v_low in {"n", "no", "0", "false", "negative", "neg"}:
+        return 0
+    # Heuristic: if it contains 'y' alone or startswith 't', consider as yes; 'n' or startswith 'f' as no
+    if v_low.startswith("y"):
+        return 1
+    if v_low.startswith("n"):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _value_after_colon(x)
+    if v is None or v == "":
+        return None
+    # Extract first numeric token (integer or float)
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        val = float(m.group(0))
+        # Age cannot be non-sensical; filter out extreme negatives
+        if val < 0:
+            return None
+        return val
+    except:
+        return None
+
+def convert_gender(x):
+    v = _value_after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.strip().lower()
+    # Female -> 0, Male -> 1
+    if v_low in {"female", "f", "woman", "women", "girl"}:
+        return 0
+    if v_low in {"male", "m", "man", "men", "boy"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Track pre-QC stats
+covariate_cols = [trait, 'Age', 'Gender']
+pre_gene_cols = [c for c in linked_data.columns if c not in covariate_cols]
+pre_samples = len(linked_data)
+pre_genes = len(pre_gene_cols)
+
+# 3. Handle missing values in the linked data
+linked_data_qc = handle_missing_values(linked_data, trait)
+
+# Post-QC stats
+post_gene_cols = [c for c in linked_data_qc.columns if c not in covariate_cols]
+post_samples = len(linked_data_qc)
+post_genes = len(post_gene_cols)
+
+# 4. Determine whether the trait and demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_qc, trait)
+
+# 5. Conduct quality check and save the cohort information.
+note = (
+    f"INFO: Linked {pre_samples} samples and {pre_genes} genes; "
+    f"after QC retained {post_samples} samples and {post_genes} genes. "
+    f"Age present after QC: {'Age' in linked_data_qc.columns}. "
+    f"Gender present after QC: {'Gender' in linked_data_qc.columns}."
+)
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note
+)
+
+# 6. If the linked data is usable, save it
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hypertension/code/GSE161533.py

@@ -0,0 +1,199 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+cohort = "GSE161533"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertension"
+in_cohort_dir = "../DATA/GEO/Hypertension/GSE161533"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/GSE161533.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/GSE161533.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/GSE161533.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix Human Genome U133 Plus 2.0 Array indicates mRNA expression microarray
+
+# 2. Variable Availability and Data Type Conversion
+
+# Identified keys from Sample Characteristics Dictionary
+trait_row = 6     # disease history, includes 'Hypertension' among other diseases
+age_row = 2       # 'age: <number>'
+gender_row = 3    # 'gender: Male/Female'
+
+def _after_colon(value):
+    if value is None:
+        return None
+    if not isinstance(value, str):
+        value = str(value)
+    parts = value.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    v_low = v.lower()
+    # Handle unknowns
+    if v_low in {"na", "n/a", "unknown", "undef", "missing"}:
+        return None
+    # Binary: 1 if hypertension is present, else 0
+    return 1 if "hypertension" in v_low else 0
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    v = v.strip()
+    # Extract first integer or float in the string
+    m = re.search(r"[-+]?\d+\.?\d*", v)
+    if not m:
+        return None
+    try:
+        num = float(m.group())
+        # Age should be within a reasonable human range
+        if num <= 0 or num > 120:
+            return None
+        return num
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    v_low = v.lower()
+    if v_low.startswith("male"):
+        return 1
+    if v_low.startswith("female"):
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if clinical data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Affymetrix probe set IDs (e.g., '1007_s_at') are not human gene symbols and need mapping.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping: probe IDs and gene symbols
+probe_id_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Ensure clinical data is loaded, then link clinical and genetic data
+if 'selected_clinical_df' not in globals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Determine availability flags from actual data
+is_gene_available = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available = trait in linked_data.columns
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata save
+note = "INFO: Affymetrix U133 Plus 2.0 microarray; trait derived from 'disease history' indicating Hypertension presence."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save final linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hypertension/code/GSE181339.py

@@ -0,0 +1,141 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+cohort = "GSE181339"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertension"
+in_cohort_dir = "../DATA/GEO/Hypertension/GSE181339"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/GSE181339.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/GSE181339.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/GSE181339.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import json
+import os
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # PBMC microarray gene expression per background info
+
+# 2. Variable Availability and Data Type Conversion
+
+# Keys inferred from the sample characteristics dictionary
+trait_row = None  # Hypertension not explicitly available and cannot be reliably inferred
+age_row = None    # The 'age' field (key 2) shows implausible small integers (e.g., 1–15), conflicting with 18–45 study range
+gender_row = 0    # 'gender: Man/Woman' present
+
+def _after_colon(value):
+    if value is None:
+        return None
+    if isinstance(value, str):
+        parts = value.split(":", 1)
+        v = parts[1] if len(parts) > 1 else parts[0]
+        return v.strip()
+    return value
+
+def convert_trait(x):
+    """
+    Convert hypertension-like information to binary if explicitly available.
+    Returns:
+        1 for hypertensive/yes; 0 for normotensive/no; None if unknown/irrelevant.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    pos = {"yes", "y", "true", "1", "hypertension", "htn", "hypertensive", "high blood pressure", "hypertensive patient"}
+    neg = {"no", "n", "false", "0", "normotensive", "none", "normal", "without hypertension", "no hypertension"}
+    if s in pos:
+        return 1
+    if s in neg:
+        return 0
+    if any(k in s for k in ["hypertension", "hypertens"]):
+        if any(k in s for k in ["no ", "not ", "without", "non-"]):
+            return 0
+        return 1
+    return None
+
+def convert_age(x):
+    """
+    Convert age to a continuous numeric value (years). Unknown -> None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    m = re.search(r"[-+]?\d*\.?\d+", s)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+    except Exception:
+        return None
+    if 0 <= age <= 120:
+        return age
+    return None
+
+def convert_gender(x):
+    """
+    Convert gender to binary: female=0, male=1. Unknown -> None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {"female", "woman", "women", "girl", "f"}:
+        return 0
+    if s in {"male", "man", "men", "boy", "m"}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Hypertension/code/GSE256539.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+cohort = "GSE256539"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertension"
+in_cohort_dir = "../DATA/GEO/Hypertension/GSE256539"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/GSE256539.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/GSE256539.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/GSE256539.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability (GeoMx DSP whole transcriptome => gene expression)
+is_gene_available = True
+
+# 2) Variable availability based on provided Sample Characteristics Dictionary
+# Only 'individuial' (subject ID) and 'batch' are present; no trait/age/gender fields with variable information.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Define converters (robust, though they won't be used since rows are None)
+def _extract_value_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    # Map pulmonary arterial hypertension as "Hypertension" present; controls as absent
+    positive = {"ipah", "pah", "pulmonary arterial hypertension", "hypertension", "htn"}
+    negative = {"control", "normal", "healthy", "donor", "non-hypertensive", "no"}
+    if any(p in s for p in positive):
+        return 1
+    if any(n in s for n in negative):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    s = str(v).lower()
+    nums = re.findall(r"[\d\.]+", s)
+    if not nums:
+        return None
+    try:
+        return float(nums[0])
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    # female -> 0, male -> 1
+    if s in {"female", "f", "woman", "women"}:
+        return 0
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+    print(preview)

output/preprocess/Hypertension/code/GSE71994.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+cohort = "GSE71994"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertension"
+in_cohort_dir = "../DATA/GEO/Hypertension/GSE71994"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/GSE71994.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/GSE71994.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/GSE71994.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import math
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # PBMC genome-wide gene expression analysis per background info
+
+# 2. Variable Availability and Data Type Conversion
+
+# Keys from Sample Characteristics Dictionary:
+# trait candidate (infer uncontrolled hypertension from systolic BP "in a sample")
+trait_row = 6  # 'sistolic blood pressure in a sample'
+age_row = 3    # 'age'
+gender_row = 1 # 'gender'
+
+# 2.2 Converters
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    if isinstance(value, str):
+        parts = value.split(":", 1)
+        return parts[1].strip() if len(parts) > 1 else value.strip()
+    return str(value).strip()
+
+def convert_trait(value):
+    # Map systolic BP to uncontrolled hypertension (1) if systolic >= 140, else controlled (0)
+    try:
+        v = _after_colon(value)
+        if v == "" or v.lower() in {"na", "n/a", "nan", "none", "missing", "unknown"}:
+            return None
+        sbp = float(v)
+        # Basic sanity check
+        if sbp <= 0 or sbp > 300:
+            return None
+        return 1 if sbp >= 140 else 0
+    except Exception:
+        return None
+
+def convert_age(value):
+    try:
+        v = _after_colon(value)
+        if v == "" or v.lower() in {"na", "n/a", "nan", "none", "missing", "unknown"}:
+            return None
+        age = float(v)
+        if not (0 < age < 120):
+            return None
+        return age
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value).strip().lower()
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Based on the provided index values (e.g., '7896746', '7896756'), these are platform probe IDs, not human gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Decide identifier and gene symbol columns and build mapping dataframe
+# Expression data uses probe IDs matching the 'ID' column in annotation.
+# Gene symbols are embedded in the 'gene_assignment' column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+# Preserve the original probe-level data if needed
+probe_data = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+try:
+    clinical_df_use = selected_clinical_df
+except NameError:
+    clinical_df_use = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(clinical_df_use, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hypertension/code/GSE74144.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+cohort = "GSE74144"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertension"
+in_cohort_dir = "../DATA/GEO/Hypertension/GSE74144"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/GSE74144.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/GSE74144.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/GSE74144.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Transcriptomic gene expression profiling indicated in series information
+
+# Step 2: Variable Availability and Converters
+# Based on Sample Characteristics Dictionary, 'subject status' is at key 0
+trait_row = 0  # Maps to 'subject status'
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    # Extract value after colon if present
+    if x is None:
+        return None
+    try:
+        val = x.split(":", 1)[1].strip().lower()
+    except Exception:
+        val = str(x).strip().lower()
+    if "hypertens" in val:
+        return 1
+    if "control" in val:
+        return 0
+    return None
+
+# Age and Gender are not available
+convert_age = None
+convert_gender = None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical Feature Extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for probe IDs and gene symbols by checking overlaps and availability
+# 1) Select the probe identifier column that best matches the expression data index
+id_match_counts = {}
+probe_index_set = set(gene_data.index.astype(str))
+for col in gene_annotation.columns:
+    try:
+        col_series = gene_annotation[col].astype(str).str.strip()
+        id_match_counts[col] = col_series.isin(probe_index_set).sum()
+    except Exception:
+        continue
+
+# Fallback to 'ID' if no matches found
+probe_col = max(id_match_counts, key=id_match_counts.get) if id_match_counts else 'ID'
+if id_match_counts and id_match_counts[probe_col] == 0 and 'ID' in gene_annotation.columns:
+    probe_col = 'ID'
+
+# 2) Choose a gene symbol column, preferring canonical symbol fields
+symbol_candidates = ['GENE_SYMBOL', 'GENE', 'SYMBOL', 'Gene Symbol', 'Symbol', 'GENE_NAME']
+symbol_col = None
+for cand in symbol_candidates:
+    if cand in gene_annotation.columns and gene_annotation[cand].notna().sum() > 0:
+        symbol_col = cand
+        break
+# As a final fallback (unlikely to be ideal), use any non-empty textual column
+if symbol_col is None:
+    non_empty_cols = [c for c in gene_annotation.columns if gene_annotation[c].notna().sum() > 0]
+    symbol_col = non_empty_cols[0] if non_empty_cols else 'GENE_SYMBOL'
+
+# Build mapping dataframe and apply mapping to convert probe-level data to gene-level
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and drop biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Only trait is available; Age and Gender are not provided in this series."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hypertension/code/GSE77627.py

@@ -0,0 +1,202 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+cohort = "GSE77627"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertension"
+in_cohort_dir = "../DATA/GEO/Hypertension/GSE77627"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/GSE77627.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/GSE77627.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/GSE77627.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Determine gene expression availability
+is_gene_available = True  # Illumina Whole-Genome DASL HT BeadChip arrays indicate mRNA expression data
+
+# 2) Variable availability and conversion functions
+# From the provided Sample Characteristics Dictionary:
+# {0: ['liver group: LC', 'liver group: INCPH', 'liver group: HNL'], 1: ['tissue: liver']}
+trait_row = 0   # 'liver group' distinguishes INCPH cases from LC/HNL controls
+age_row = None  # Not available
+gender_row = None  # Not available
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip().strip('"').strip("'")
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    """
+    Convert 'liver group' to binary Hypertension label aligned to study design:
+    - INCPH (idiopathic non-cirrhotic portal hypertension) -> 1 (case)
+    - LC (cirrhosis) and HNL (histologically normal liver) -> 0 (controls)
+    Unknown or other -> None
+    """
+    v = _extract_value(x)
+    if not v:
+        return None
+    v_low = v.lower()
+    if v_low == 'incph':
+        return 1
+    if v_low in {'lc', 'hnl'}:
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Generic age parser: extract numeric years if present; else None.
+    """
+    v = _extract_value(x)
+    if not v:
+        return None
+    import re
+    m = re.search(r'(\d+(?:\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Map gender to binary: female->0, male->1.
+    """
+    v = _extract_value(x)
+    if not v:
+        return None
+    v_low = v.lower()
+    if v_low in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if v_low in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if clinical data available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print(clinical_preview)
+    # Ensure output directory exists and save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide the columns for probe IDs and gene symbols based on annotation preview
+probe_col = 'ID'       # Matches ILMN_* probe identifiers seen in gene_data
+gene_symbol_col = 'Symbol'  # Contains gene symbols
+
+# 2. Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+if not normalized_gene_data.empty:
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Load clinical data from disk and link with genetic data
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove biased demographic features if necessary
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and save cohort info
+is_gene_available = bool(normalized_gene_data.shape[0] > 0)
+is_trait_available = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+note = "INFO: Trait defined as INCPH (case) vs LC/HNL (controls); Age and Gender not available."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hypertension/code/TCGA.py

@@ -0,0 +1,65 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertension"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertension/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertension/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertension/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Hypertension/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select a TCGA subdirectory relevant to the trait "Hypertension"
+dirnames = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_lower = trait.lower()
+
+# Define synonyms/related terms for hypertension
+hypertension_keywords = [
+    "hypertension", "hypertensive", "blood pressure", "blood-pressure", "blood_pressure",
+    "high blood pressure", "bp", "cardiovascular", "cardiac", "heart"
+]
+
+def is_relevant(dirname: str, keywords):
+    name = dirname.lower()
+    return any(k in name for k in keywords)
+
+candidate_dirs = [d for d in dirnames if is_relevant(d, hypertension_keywords)]
+
+if not candidate_dirs:
+    print(f"No suitable TCGA cohort directory found for trait '{trait}'. Skipping this trait.")
+    # Record metadata as unavailable
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    clinical_df = None
+    genetic_df = None
+else:
+    # If multiple candidates, choose the most specific (longest name as a proxy)
+    selected_dir = sorted(candidate_dirs, key=lambda x: len(x), reverse=True)[0]
+    print(f"Selected TCGA cohort directory: {selected_dir}")
+
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+    print(f"Clinical file: {clinical_file_path}")
+    print(f"Genetic file: {genetic_file_path}")
+
+    # Step 3: Load both files
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # Step 4: Print clinical column names
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))

output/preprocess/Hypertrophic_Cardiomyopathy/code/GSE36961.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertrophic_Cardiomyopathy"
+cohort = "GSE36961"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy"
+in_cohort_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy/GSE36961"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertrophic_Cardiomyopathy/GSE36961.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv"
+json_path = "./output/z3/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # mRNA transcriptome per background info
+
+# 2) Variable availability and conversion functions
+trait_row = 3   # contains 'disease state: hypertrophic cardiomyopathy (HCM)' and 'sample type: control'
+age_row = 1     # contains 'age (yrs): ...' (also has 'tissue: cardiac' which will be ignored by converter)
+gender_row = 0  # contains 'Sex: male' / 'Sex: female'
+
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # Map known case indicators
+    if 'hypertrophic cardiomyopathy' in v or v == 'hcm' or 'hcm' in v or 'case' in v or 'patient' in v:
+        return 1
+    # Map known control indicators
+    if 'control' in v or 'normal' in v or 'healthy' in v or 'donor' in v:
+        return 0
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    # Extract first numeric token
+    m = re.search(r'[-+]?\d+\.?\d*', val)
+    if m:
+        try:
+            num = float(m.group(0))
+            # If age looks like an integer, keep as int; otherwise, float
+            return int(num) if num.is_integer() else num
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v.startswith('female') or v == 'f':
+        return 0
+    if v.startswith('male') or v == 'm':
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Clinical feature preview:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Based on the observed identifiers (e.g., A1BG, A1CF, A2M), these are human gene symbols.
+requires_gene_mapping = False
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Data Normalization and Linking
+import os
+import pandas as pd
+
+# Fallbacks for variables from previous steps
+if 'matrix_file' not in globals():
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+if 'gene_data' not in globals():
+    gene_data = get_genetic_data(matrix_file)
+
+if 'selected_clinical_df' not in globals():
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise FileNotFoundError("Clinical data not found in memory or on disk. Expected at: " + out_clinical_data_file)
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# For notes: capture counts before missing handling
+covariate_cols = [trait, 'Age', 'Gender']
+samples_before = len(linked_data)
+genes_before = len([c for c in linked_data.columns if c not in covariate_cols])
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Counts after missing handling (before bias removal)
+samples_after = len(linked_data)
+genes_after = len([c for c in linked_data.columns if c not in covariate_cols])
+
+# 4) Bias check and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Final quality validation and metadata saving
+note = (
+    f"INFO: Samples before/after missing handling: {samples_before}/{samples_after}; "
+    f"Genes before/after missing handling: {genes_before}/{genes_after}."
+)
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hypertrophic_Cardiomyopathy/code/TCGA.py

@@ -0,0 +1,60 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertrophic_Cardiomyopathy"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hypertrophic_Cardiomyopathy/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Hypertrophic_Cardiomyopathy/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Find the most relevant TCGA cohort directory for Hypertrophic Cardiomyopathy (unlikely in TCGA cancer cohorts)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+keywords = ['hypertrophic', 'cardiomyopathy', 'heart', 'cardio', 'hcm', 'myocard', 'ventric']
+selected_dir = None
+
+def score_dir(name: str) -> int:
+    lname = name.lower()
+    return sum(1 for k in keywords if k in lname)
+
+scored = [(d, score_dir(d)) for d in subdirs]
+scored.sort(key=lambda x: x[1], reverse=True)
+if scored and scored[0][1] > 0:
+    selected_dir = scored[0][0]
+
+if selected_dir is None:
+    print("No suitable TCGA cohort directory found for Hypertrophic Cardiomyopathy. Skipping this trait.")
+    # Record metadata and mark as completed for this trait with no available data
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    # Prepare placeholders for potential downstream references
+    cohort_dir = None
+    clinical_df = None
+    genetic_df = None
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # Step 2: Identify clinical and genetic data files
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # Step 4: Print clinical column names
+    print(clinical_df.columns.tolist())

output/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE36961": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 142
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  }
-}
+{"GSE36961": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 142, "note": "INFO: Samples before/after missing handling: 145/142; Genes before/after missing handling: 18660/18660."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}