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output/preprocess/Alopecia/code/GSE18876.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE18876"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE18876"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alopecia/GSE18876.csv"
+out_gene_data_file = "./output/z1/preprocess/Alopecia/gene_data/GSE18876.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alopecia/clinical_data/GSE18876.csv"
+json_path = "./output/z1/preprocess/Alopecia/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
+is_gene_available = True  # Affymetrix Exon 1.0 ST array indicates mRNA gene expression
+
+# Step 2: Variable availability and conversion functions
+
+# Availability based on provided sample characteristics
+trait_row = None          # Alopecia status not provided per sample
+age_row = 0               # 'age: <value>' is present at row 0
+gender_row = None         # All healthy males per background => constant, thus not useful
+
+# Conversion functions
+def _extract_value_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1].strip() if len(parts) > 1 else x.strip()
+        return val
+    return x
+
+def convert_trait(x):
+    # Binary: 1 = alopecia present/AGA/AA; 0 = control/healthy/no alopecia
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in {"na", "n/a", "nan", "", "unknown", "not available"}:
+        return None
+    # Positive indicators
+    positive_terms = [
+        "alopecia", "androgenetic alopecia", "aga", "aa",
+        "pattern hair loss", "male pattern baldness", "baldness",
+        "case", "patient", "disease", "affected", "yes", "present"
+    ]
+    # Negative indicators
+    negative_terms = [
+        "control", "healthy", "normal", "no alopecia", "none",
+        "unaffected", "absent", "no", "non-diseased"
+    ]
+    for t in positive_terms:
+        if t in s:
+            return 1
+    for t in negative_terms:
+        if t in s:
+            return 0
+    # Heuristic: severity implies presence
+    if any(t in s for t in ["mild", "moderate", "severe", "grade", "stage"]):
+        return 1
+    return None
+
+def convert_age(x):
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in {"na", "n/a", "nan", "", "unknown", "not available"}:
+        return None
+    # Extract first integer/float in the string
+    num = ""
+    dot_seen = False
+    for ch in s:
+        if ch.isdigit():
+            num += ch
+        elif ch == "." and not dot_seen:
+            num += ch
+            dot_seen = True
+        elif num:
+            break
+    if num == "" or num == ".":
+        return None
+    try:
+        v = float(num)
+        return int(v) if abs(v - int(v)) < 1e-6 else v
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in {"na", "n/a", "nan", "", "unknown", "not available"}:
+        return None
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    if s in {"female", "f", "woman", "women"}:
+        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_row is None)
+if trait_row is not None and 'clinical_data' in globals():
+    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
+# Decide which columns to use for mapping based on observed identifiers
+probe_col = 'ID'  # Probe IDs match the 'ID' column in annotation
+
+# Prefer 'gene_assignment' for gene symbols; fallback to 'mrna_assignment' or any column containing 'gene'
+if 'gene_assignment' in gene_annotation.columns:
+    gene_col = 'gene_assignment'
+elif 'mrna_assignment' in gene_annotation.columns:
+    gene_col = 'mrna_assignment'
+else:
+    gene_col = next((c for c in gene_annotation.columns if 'gene' in c.lower()), None)
+    if gene_col is None:
+        raise ValueError("No suitable gene symbol column found in the annotation.")
+
+# Build mapping dataframe (ID -> Gene text)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_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
+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)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2–6. Proceed only if clinical features were extracted earlier; otherwise, skip linking and record metadata accordingly.
+selected_clinical_data_obj = globals().get('selected_clinical_data', None)
+has_clinical = isinstance(selected_clinical_data_obj, pd.DataFrame) and (trait in selected_clinical_data_obj.index)
+
+linked_data = None  # ensure variable exists as required by the step
+
+if has_clinical:
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data_obj, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias checks and removal of biased demographic covariates
+    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,
+        note="INFO: Clinical features available and linked; demographics pruned if biased."
+    )
+
+    # 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 per sample; record metadata and do not attempt linking
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Alopecia/code/GSE66664.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE66664"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE66664"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alopecia/GSE66664.csv"
+out_gene_data_file = "./output/z1/preprocess/Alopecia/gene_data/GSE66664.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alopecia/clinical_data/GSE66664.csv"
+json_path = "./output/z1/preprocess/Alopecia/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  # Transcriptome analysis of DP cells (not miRNA-only or methylation)
+
+# 2) Variable availability and converters
+# Based on sample characteristics:
+# 0: ['cell line: BAB', 'cell line: BAN'] -> Use as Alopecia trait (BAB=balding, BAN=non-balding)
+# 1: ['agent: DHT'] -> Constant, not useful
+# 2: ['dose: 10nM', 'dose: 1nM']
+# 3: time points -> Not trait, age, or gender
+trait_row = 0
+age_row = None
+gender_row = None  # All samples are male per background; constant feature, thus considered unavailable
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s if s != '' else None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    t = v.lower()
+    # Normalize to improve robustness when extra descriptors are present
+    t = re.sub(r'\b(cell line|dermal papilla|dp)\b', '', t)
+    t = t.replace('-', ' ')
+    t = re.sub(r'\s+', ' ', t).strip()
+    if re.search(r'\bbab\b', t) or 'balding' in t:
+        return 1
+    if re.search(r'\bban\b', t) or 'non balding' in t or 'nonbald' in t or 'non bald' in t:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    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 v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if v_low in {'female', 'f', 'woman', 'girl'}:
+        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)
+    # Save clinical features
+    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 mapping columns based on observation:
+# - Probe identifiers in expression data: 'ILMN_...' (matches 'ID' column in annotation)
+# - Gene symbols in annotation: 'Symbol'
+prob_col = 'ID'
+gene_col = 'Symbol'
+
+# 2. Build probe-to-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(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# Ensure clinical features are available; load from file if not in scope
+if 'selected_clinical_df' not in globals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1) Normalize gene symbols; ensure output directory exists before saving
+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; drop biased demographic covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Derive availability flags for final validation (ensure native Python bool)
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in selected_clinical_df.index) and selected_clinical_df.loc[trait].notna().any())
+is_trait_biased_py = bool(is_trait_biased)
+
+# 5) Final validation and save cohort metadata
+note = ("INFO: DP cell line dataset; trait derived from 'cell line: BAB'(1) vs 'BAN'(0). "
+        "Male-only; no age available. DHT dose/time present but not included as covariates.")
+
+try:
+    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_py,
+        df=unbiased_linked_data,
+        note=note
+    )
+except TypeError as e:
+    # Fallback: manually write metadata if JSON serialization fails
+    is_available = bool(is_gene_available and is_trait_available)
+    record = {
+        "is_usable": bool(is_available and (is_trait_biased_py is False)),
+        "is_gene_available": bool(is_gene_available),
+        "is_trait_available": bool(is_trait_available),
+        "is_available": bool(is_available),
+        "is_biased": (bool(is_trait_biased_py) if is_available else None),
+        "has_age": (bool('Age' in unbiased_linked_data.columns) if is_available else None),
+        "has_gender": (bool('Gender' in unbiased_linked_data.columns) if is_available else None),
+        "sample_size": (int(len(unbiased_linked_data)) if is_available else None),
+        "note": str(note)
+    }
+
+    # Prepare directory and file
+    trait_directory = os.path.dirname(json_path)
+    os.makedirs(trait_directory, exist_ok=True)
+    if not os.path.exists(json_path):
+        with open(json_path, 'w') as file:
+            import json
+            json.dump({}, file)
+
+    # Read, update, and write atomically
+    import json
+    with open(json_path, "r") as file:
+        records = json.load(file)
+    records[cohort] = record
+    temp_path = json_path + ".tmp"
+    with open(temp_path, 'w') as file:
+        json.dump(records, file)
+    os.replace(temp_path, json_path)
+
+    is_usable = record["is_usable"]
+
+# 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/Alopecia/code/GSE80342.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE80342"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE80342"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alopecia/GSE80342.csv"
+out_gene_data_file = "./output/z1/preprocess/Alopecia/gene_data/GSE80342.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alopecia/clinical_data/GSE80342.csv"
+json_path = "./output/z1/preprocess/Alopecia/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  # Microarray gene expression profiling per background info
+
+# 2) Variable availability and conversion functions
+# From the sample characteristics dictionary:
+#  - trait (Alopecia) can be inferred from 'aatype' at row 7 (healthy_control vs AA subtypes)
+#  - age from 'agebaseline' at row 4
+#  - gender from 'gender' at row 3
+trait_row = 7
+age_row = 4
+gender_row = 3
+
+def convert_trait(x: str):
+    # Extract value after colon and map to binary: healthy_control -> 0, AA types -> 1
+    if not isinstance(x, str):
+        return None
+    val = x.split(":", 1)[1].strip().lower() if ":" in x else x.strip().lower()
+    if val in {"na", "n/a", "", "unknown"}:
+        return None
+    # Map AA subtypes to 1
+    aa_positive = {"persistent_patchy", "severe_patchy", "totalis", "universalis", "patchy", "alopecia_areata"}
+    if val in {"healthy_control", "control", "healthy"}:
+        return 0
+    if val in aa_positive:
+        return 1
+    # If it's not explicitly known, apply heuristic: any value containing 'control' -> 0, otherwise 1
+    if "control" in val:
+        return 0
+    # Conservatively assume non-control indicates AA involvement
+    return 1
+
+def convert_age(x: str):
+    # Extract numeric age from 'agebaseline: <number>'
+    if not isinstance(x, str):
+        return None
+    val = x.split(":", 1)[1].strip() if ":" in x else x.strip()
+    if val.lower() in {"na", "n/a", "", "unknown"}:
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", val)
+    if not m:
+        return None
+    try:
+        num = float(m.group(1))
+        # Return int if it's a whole number
+        return int(num) if num.is_integer() else num
+    except Exception:
+        return None
+
+def convert_gender(x: str):
+    # Map gender: F->0, M->1
+    if not isinstance(x, str):
+        return None
+    val = x.split(":", 1)[1].strip().lower() if ":" in x else x.strip().lower()
+    if val in {"na", "n/a", "", "unknown"}:
+        return None
+    if val.startswith("f"):
+        return 0
+    if val.startswith("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 (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 = preview_df(selected_clinical_df)
+    print(preview)
+    # 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
+# Affymetrix probe set IDs detected (e.g., '1007_s_at', '1552256_a_at'), 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
+# Identify the appropriate columns for probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Create the mapping DataFrame from the annotation
+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
+probe_level_df = gene_data  # preserve original probe-level 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. 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
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+is_trait_biased_bool = bool(is_trait_biased)
+
+note = (
+    "INFO: "
+    f"Samples={int(len(unbiased_linked_data))}, "
+    f"Genes={int(sum(col not in [trait, 'Age', 'Gender'] for col in unbiased_linked_data.columns))}, "
+    f"Age_included={bool('Age' in unbiased_linked_data.columns)}, "
+    f"Gender_included={bool('Gender' in unbiased_linked_data.columns)}."
+)
+
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, is_gene_available_final, is_trait_available_final,
+    is_trait_biased_bool, unbiased_linked_data, 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/Alopecia/code/GSE81071.py

@@ -0,0 +1,454 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE81071"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE81071"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alopecia/GSE81071.csv"
+out_gene_data_file = "./output/z1/preprocess/Alopecia/gene_data/GSE81071.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alopecia/clinical_data/GSE81071.csv"
+json_path = "./output/z1/preprocess/Alopecia/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 os
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix mRNA microarrays from FFPE blocks
+
+# 2) Variable availability from the provided sample characteristics
+# Sample Characteristics Dictionary indicates only disease state and tissue, no explicit age or gender.
+# Trait here is Alopecia, which is not explicitly recorded; inferring from DLE is unreliable (<90% certainty).
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _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):
+    # Conservatively only map explicit alopecia indications
+    val = _after_colon(x).lower()
+    if val in ["", "na", "n/a", "none", "unknown", "not available"]:
+        return None
+    # Positive indications
+    if "alopecia" in val and not ("no alopecia" in val or "without alopecia" in val):
+        return 1
+    # Explicit negatives
+    if "no alopecia" in val or "without alopecia" in val:
+        return 0
+    # Healthy/control without alopecia info is uncertain -> None
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if not val:
+        return None
+    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):
+    val = _after_colon(x).lower()
+    if val in ["", "na", "n/a", "none", "unknown", "not available"]:
+        return None
+    if val in ["female", "f", "woman", "women"]:
+        return 0
+    if val in ["male", "m", "man", "men"]:
+        return 1
+    return None
+
+# 3) 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
+)
+
+# 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)
+
+# 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
+import os
+import re
+
+# We will try to map probes to gene symbols using the best available annotation.
+# 1) Try platform (GPL) annotation first for a SYMBOL-like column.
+# 2) Fallback to series-level annotation for symbol columns.
+# 3) If no symbol column works, map to Entrez IDs explicitly without using extract_human_gene_symbols.
+# 4) If all fail, keep probe-level data and warn.
+
+probe_col = 'ID'  # Matches gene_data index name after get_genetic_data()
+
+def find_symbol_column(df):
+    cols = list(df.columns)
+    # Direct symbol column candidates
+    direct_candidates = [
+        'SYMBOL', 'Gene Symbol', 'GENE_SYMBOL', 'GENE SYMBOL', 'Symbol', 'gene_symbol',
+        'Gene symbol', 'gene symbols', 'GENE_SYMBOLS', 'Gene Symbols'
+    ]
+    for cand in direct_candidates:
+        if cand in cols:
+            return cand
+    # Regex-based search for 'gene symbol' variants
+    regexes = [
+        r'^\s*gene\s*symbol\s*$',
+        r'^\s*symbol\s*$',
+        r'^\s*gene\s*symbols?\s*$',
+        r'associated\s*gene\s*symbol'
+    ]
+    for c in cols:
+        for pat in regexes:
+            if re.search(pat, c, flags=re.IGNORECASE):
+                return c
+    # Fallbacks that often contain symbol-like strings
+    fallbacks = [
+        'GENE_ASSIGNMENT', 'Gene Assignment', 'Gene assignment',
+        'GENE_TITLE', 'Gene Title', 'Gene title',
+        'Representative Public ID', 'REPRESENTATIVE_PUBLIC_ID'
+    ]
+    for fb in fallbacks:
+        if fb in cols:
+            return fb
+    return None
+
+def map_by_entrez(expression_df, anno_df, prob_col='ID', entrez_col='ENTREZ_GENE_ID'):
+    if (prob_col not in anno_df.columns) or (entrez_col not in anno_df.columns):
+        return None
+    m = anno_df.loc[:, [prob_col, entrez_col]].dropna()
+    if m.empty:
+        return None
+    m = m.rename(columns={prob_col: 'ID', entrez_col: 'Entrez'})
+    m['ID'] = m['ID'].astype(str).str.strip()
+    m = m[m['ID'] != '']
+    # Keep only probes present in expression data
+    m = m[m['ID'].isin(expression_df.index)]
+    if m.empty:
+        return None
+
+    def split_entrez(x):
+        if x is None:
+            return []
+        s = str(x)
+        s = s.replace('///', ';')
+        parts = re.split(r'[;,\s]+', s)
+        parts = [p for p in parts if re.fullmatch(r'\d+', p)]
+        return parts
+
+    m['Gene'] = m['Entrez'].map(split_entrez)
+    m['num_genes'] = m['Gene'].apply(len)
+    m = m.explode('Gene').dropna(subset=['Gene'])
+    if m.empty:
+        return None
+    m = m.set_index('ID')
+
+    merged = m.join(expression_df)
+    expr_cols = [c for c in merged.columns if c not in ['Gene', 'num_genes', 'Entrez']]
+    if not expr_cols:
+        return None
+    merged[expr_cols] = merged[expr_cols].div(merged['num_genes'].replace(0, 1), axis=0)
+    gene_expression = merged.groupby('Gene')[expr_cols].sum()
+    if gene_expression.empty:
+        return None
+    return gene_expression
+
+# Build annotation sources: platform first (if available), then series-level annotation
+annotation_sources = []
+
+# Search recursively for GPL files (gzipped) in the cohort directory
+try:
+    gpl_paths = []
+    for root, _, files in os.walk(in_cohort_dir):
+        for f in files:
+            fl = f.lower()
+            if ('gpl' in fl) and (f.endswith('.gz')):  # prioritize gz which get_gene_annotation can read
+                gpl_paths.append(os.path.join(root, f))
+    # Add platform annotations first
+    for p in sorted(gpl_paths):
+        try:
+            gpl_anno = get_gene_annotation(p)
+            annotation_sources.append(('platform_soft', gpl_anno))
+        except Exception:
+            continue
+except Exception:
+    pass
+
+# Add the series-level annotation as fallback
+annotation_sources.append(('series_soft', gene_annotation))
+
+mapped = False
+
+# Try symbol-based mapping first
+for src_name, anno_df in annotation_sources:
+    try:
+        gene_col = find_symbol_column(anno_df)
+        if gene_col is None:
+            continue
+        mapping_df = get_gene_mapping(anno_df, prob_col=probe_col, gene_col=gene_col)
+        if mapping_df.empty:
+            continue
+        # Map probes to symbols
+        candidate_gene_data = apply_gene_mapping(gene_data, mapping_df)
+        if candidate_gene_data is not None and candidate_gene_data.shape[0] > 0:
+            gene_data = candidate_gene_data
+            print(f"Gene mapping to SYMBOLs successful using source='{src_name}', "
+                  f"probe_col='{probe_col}', gene_col='{gene_col}'. "
+                  f"Mapped genes: {gene_data.shape[0]}")
+            mapped = True
+            break
+    except Exception:
+        continue
+
+# If symbol mapping failed, try Entrez-based mapping explicitly
+if not mapped:
+    for src_name, anno_df in annotation_sources:
+        try:
+            if 'ENTREZ_GENE_ID' not in anno_df.columns:
+                continue
+            candidate_gene_data = map_by_entrez(gene_data, anno_df, prob_col=probe_col, entrez_col='ENTREZ_GENE_ID')
+            if candidate_gene_data is not None and candidate_gene_data.shape[0] > 0:
+                gene_data = candidate_gene_data
+                print(f"Gene mapping to Entrez IDs successful using source='{src_name}', "
+                      f"probe_col='{probe_col}', gene_col='ENTREZ_GENE_ID'. "
+                      f"Mapped genes: {gene_data.shape[0]}")
+                mapped = True
+                break
+        except Exception:
+            continue
+
+# If both strategies fail, retain probe-level expression
+if not mapped:
+    print("WARNING: Failed to map probes to gene symbols or Entrez IDs. "
+          "Proceeding with probe-level expression data.")
+
+# Step 7: Data Normalization and Linking
+import os
+import re
+import pandas as pd
+
+# Helper to find a plausible SYMBOL column
+def _find_symbol_col(cols):
+    priority = [
+        'SYMBOL', 'Gene Symbol', 'GENE_SYMBOL', 'GENE SYMBOL', 'Symbol',
+        'gene_symbol', 'Gene symbol', 'GENE_SYMBOLS', 'Gene Symbols'
+    ]
+    for c in priority:
+        if c in cols:
+            return c
+    # Regex fallback
+    for c in cols:
+        if re.search(r'\bsymbols?\b', c, flags=re.IGNORECASE):
+            return c
+    return None
+
+# Build Entrez->Symbol mapping from any available annotation (GPL preferred, then series-level)
+entrez_to_symbol = {}
+annotation_sources = []
+
+# Try to load GPL annotations
+try:
+    gpl_paths = []
+    for root, _, files in os.walk(in_cohort_dir):
+        for f in files:
+            fl = f.lower()
+            if ('gpl' in fl) and f.endswith('.gz'):
+                gpl_paths.append(os.path.join(root, f))
+    for p in sorted(gpl_paths):
+        try:
+            gpl_anno = get_gene_annotation(p)
+            annotation_sources.append(('platform_soft', gpl_anno))
+        except Exception:
+            continue
+except Exception:
+    pass
+
+# Add previously loaded series-level annotation as fallback (from Step 5)
+if 'gene_annotation' in locals():
+    annotation_sources.append(('series_soft', gene_annotation))
+
+def _split_list_field(x):
+    if x is None:
+        return []
+    s = str(x)
+    # Common delimiters in GEO/GPL annotations
+    s = s.replace('///', ';')
+    parts = re.split(r'[;,/|\s]+', s)
+    parts = [p for p in parts if p]  # non-empty
+    return parts
+
+# Construct mapping
+for src_name, anno_df in annotation_sources:
+    try:
+        if 'ENTREZ_GENE_ID' not in anno_df.columns:
+            continue
+        sym_col = _find_symbol_col(anno_df.columns)
+        if sym_col is None:
+            continue
+        sub = anno_df[['ENTREZ_GENE_ID', sym_col]].dropna()
+        if sub.empty:
+            continue
+        for _, row in sub.iterrows():
+            entrez_list = [p for p in _split_list_field(row['ENTREZ_GENE_ID']) if re.fullmatch(r'\d+', p)]
+            sym_list = _split_list_field(row[sym_col])
+            # Choose the first plausible gene symbol token
+            sym = None
+            for token in sym_list:
+                tok = token.strip()
+                # Basic sanity: uppercase letters/digits/dash or C#orf#
+                if re.fullmatch(r"(?:[A-Z][A-Z0-9-]{0,9}|C\d+orf\d+)", tok):
+                    sym = tok
+                    break
+            if sym is None and sym_list:
+                sym = sym_list[0].strip()  # fallback to first token
+            if sym:
+                for e in entrez_list:
+                    if e not in entrez_to_symbol:
+                        entrez_to_symbol[e] = sym
+        # If we built a decent mapping, we can stop early
+        if len(entrez_to_symbol) > 0:
+            break
+    except Exception:
+        continue
+
+# 1) Normalize to gene symbols if possible, then apply synonym normalization; otherwise keep Entrez IDs
+normalized_gene_data = None
+note_parts = []
+try:
+    if len(entrez_to_symbol) > 0:
+        # Map current Entrez-indexed expression to SYMBOLs
+        mapped_index = gene_data.index.to_series().map(lambda x: entrez_to_symbol.get(str(x)))
+        symbol_gene_data = gene_data.copy()
+        symbol_gene_data.index = mapped_index
+        symbol_gene_data = symbol_gene_data[symbol_gene_data.index.notnull()]
+        if len(symbol_gene_data) > 0:
+            # Aggregate duplicates and normalize using synonym dictionary
+            symbol_gene_data = symbol_gene_data.groupby(symbol_gene_data.index).sum()
+            candidate = normalize_gene_symbols_in_index(symbol_gene_data)
+            if candidate is not None and len(candidate) > 0:
+                normalized_gene_data = candidate
+                note_parts.append("Mapped Entrez->SYMBOL using available annotation and normalized symbols via NCBI synonym dictionary.")
+            else:
+                note_parts.append("SYMBOL normalization produced empty matrix; falling back to Entrez-indexed matrix.")
+        else:
+            note_parts.append("No SYMBOLs obtained from Entrez mapping; falling back to Entrez-indexed matrix.")
+    else:
+        note_parts.append("No SYMBOL column available in annotation; kept Entrez-indexed matrix.")
+except Exception as e:
+    note_parts.append(f"Symbol normalization failed with error: {e}; kept Entrez-indexed matrix.")
+
+# Choose the gene matrix to save
+gene_matrix_to_save = normalized_gene_data if normalized_gene_data is not None else gene_data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_matrix_to_save.to_csv(out_gene_data_file)
+
+# 2-6) Linking and downstream steps should proceed only if trait data is available
+if 'selected_clinical_data' in locals():
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, gene_matrix_to_save)
+
+    # 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 metadata save
+    note = "INFO: " + " ".join(note_parts) if note_parts else "INFO: Standard preprocessing completed."
+    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)
+else:
+    # Trait not available; record final metadata with a note, no linking performed
+    note = "INFO: Trait not available; skipped linking and QC. " + (" ".join(note_parts) if note_parts else "")
+    _ = 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,  # placeholder; will be recorded as None since data is not available
+        df=gene_matrix_to_save,
+        note=note
+    )

output/preprocess/Alopecia/code/TCGA.py

@@ -0,0 +1,54 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alopecia/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Alopecia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alopecia/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Alopecia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Identify the most relevant TCGA cohort directory for the trait "Alopecia"
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_terms = ['alopecia', 'hair', 'hairloss', 'hair_loss', 'hypotrich', 'atrich', 'trichotillomania']
+selected_subdir = None
+for d in subdirs:
+    name_l = d.lower()
+    if any(term in name_l for term in trait_terms):
+        selected_subdir = d
+        break
+
+clinical_df = None
+genetic_df = None
+
+if selected_subdir is None:
+    # No suitable cohort found for Alopecia in TCGA; record and skip further processing.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort found for the trait; skipping.")
+else:
+    # Step 2: Locate clinicalMatrix and PANCAN files within the selected cohort directory
+    cohort_dir = os.path.join(tcga_root_dir, selected_subdir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files into DataFrames
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)
+
+    # Step 4: Print clinical column names
+    print(clinical_df.columns.tolist())

output/preprocess/Alzheimers_Disease/clinical_data/GSE117589.csv

@@ -1,4 +1,4 @@
-GSM3304268,GSM3304269,GSM3304270,GSM3304271,GSM3304272,GSM3304273,GSM3304274,GSM3304275,GSM3304276,GSM3304277,GSM3304278,GSM3304279,GSM3304280,GSM3304281,GSM3304282,GSM3304283,GSM3304284,GSM3304285,GSM3304286,GSM3304287,GSM3304288,GSM3304289,GSM3304290,GSM3304291,GSM3304292,GSM3304293,GSM3304294,GSM3304295,GSM3304296,GSM3304297,GSM3304298
-0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0
-60.0,64.0,72.0,73.0,75.0,92.0,60.0,69.0,72.0,87.0,60.0,64.0,72.0,73.0,75.0,92.0,60.0,60.0,69.0,72.0,87.0,60.0,64.0,72.0,73.0,92.0,60.0,60.0,69.0,72.0,87.0
-0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0
+,GSM3304268,GSM3304269,GSM3304270,GSM3304271,GSM3304272,GSM3304273,GSM3304274,GSM3304275,GSM3304276,GSM3304277,GSM3304278,GSM3304279,GSM3304280,GSM3304281,GSM3304282,GSM3304283,GSM3304284,GSM3304285,GSM3304286,GSM3304287,GSM3304288,GSM3304289,GSM3304290,GSM3304291,GSM3304292,GSM3304293,GSM3304294,GSM3304295,GSM3304296,GSM3304297,GSM3304298
+Alzheimers_Disease,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0
+Age,60.0,64.0,72.0,73.0,75.0,92.0,60.0,69.0,72.0,87.0,60.0,64.0,72.0,73.0,75.0,92.0,60.0,60.0,69.0,72.0,87.0,60.0,64.0,72.0,73.0,92.0,60.0,60.0,69.0,72.0,87.0
+Gender,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0

output/preprocess/Alzheimers_Disease/clinical_data/GSE122063.csv

@@ -1,4 +1,4 @@
 ,GSM3454053,GSM3454054,GSM3454055,GSM3454056,GSM3454057,GSM3454058,GSM3454059,GSM3454060,GSM3454061,GSM3454062,GSM3454063,GSM3454064,GSM3454065,GSM3454066,GSM3454067,GSM3454068,GSM3454069,GSM3454070,GSM3454071,GSM3454072,GSM3454073,GSM3454074,GSM3454075,GSM3454076,GSM3454077,GSM3454078,GSM3454079,GSM3454080,GSM3454081,GSM3454082,GSM3454083,GSM3454084,GSM3454085,GSM3454086,GSM3454087,GSM3454088,GSM3454089,GSM3454090,GSM3454091,GSM3454092,GSM3454093,GSM3454094,GSM3454095,GSM3454096,GSM3454097,GSM3454098,GSM3454099,GSM3454100,GSM3454101,GSM3454102,GSM3454103,GSM3454104,GSM3454105,GSM3454106,GSM3454107,GSM3454108,GSM3454109,GSM3454110,GSM3454111,GSM3454112,GSM3454113,GSM3454114,GSM3454115,GSM3454116,GSM3454117,GSM3454118,GSM3454119,GSM3454120,GSM3454121,GSM3454122,GSM3454123,GSM3454124,GSM3454125,GSM3454126,GSM3454127,GSM3454128,GSM3454129,GSM3454130,GSM3454131,GSM3454132,GSM3454133,GSM3454134,GSM3454135,GSM3454136,GSM3454137,GSM3454138,GSM3454139,GSM3454140,GSM3454141,GSM3454142,GSM3454143,GSM3454144,GSM3454145,GSM3454146,GSM3454147,GSM3454148,GSM3454149,GSM3454150,GSM3454151,GSM3454152,GSM3454153,GSM3454154,GSM3454155,GSM3454156,GSM3454157,GSM3454158,GSM3454159,GSM3454160,GSM3454161,GSM3454162,GSM3454163,GSM3454164,GSM3454165,GSM3454166,GSM3454167,GSM3454168,GSM3454169,GSM3454170,GSM3454171,GSM3454172,GSM3454173,GSM3454174,GSM3454175,GSM3454176,GSM3454177,GSM3454178,GSM3454179,GSM3454180,GSM3454181,GSM3454182,GSM3454183,GSM3454184,GSM3454185,GSM3454186,GSM3454187,GSM3454188
-Alzheimers_Disease,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Alzheimers_Disease,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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output/preprocess/Alzheimers_Disease/clinical_data/GSE139384.csv

@@ -1,4 +1,4 @@
 ,GSM4140293,GSM4140294,GSM4140295,GSM4140296,GSM4140297,GSM4140298,GSM4140299,GSM4140300,GSM4140301,GSM4140302,GSM4140303,GSM4140304,GSM4140305,GSM4140306,GSM4140307,GSM4140308,GSM4140309,GSM4140310,GSM4140311,GSM4140312,GSM4140313,GSM4140314,GSM4140315,GSM4140316,GSM4140317,GSM4140318,GSM4140319,GSM4140320,GSM4140321,GSM4140322,GSM4140323,GSM4140324,GSM4140325
-Alzheimers_Disease,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,
+Alzheimers_Disease,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
 Age,,,,,,,,,,,,,66.0,77.0,70.0,74.0,76.0,60.0,79.0,71.0,63.0,65.0,70.0,81.0,70.0,74.0,73.0,72.0,72.0,75.0,85.0,76.0,74.0
 Gender,,,,,,,,,,,,,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0

output/preprocess/Alzheimers_Disease/code/GSE109887.py

@@ -0,0 +1,206 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE109887"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE109887"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE109887.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE109887.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE109887.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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 numpy as np
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Illumina HumanHT-12 v4 expression data
+
+# 2) Variable availability
+trait_row = 3    # 'disease state: AD' vs 'Control'
+age_row = 1      # 'age: <number>'
+gender_row = 0   # 'gender: Female'/'Male'
+
+# 2.2) Converters
+def _extract_value(cell):
+    if cell is None or (isinstance(cell, float) and np.isnan(cell)):
+        return None
+    s = str(cell).strip()
+    if ":" in s:
+        s = s.split(":", 1)[1].strip()
+    return s if s != "" else None
+
+def convert_trait(cell):
+    v = _extract_value(cell)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {"ad", "alzheimer", "alzheimer's disease", "alzheimers disease", "alzheimer disease"}:
+        return 1
+    if v_low in {"control", "ctrl", "healthy control", "non-demented", "nd", "cn"}:
+        return 0
+    if "alzheimer" in v_low or v_low == "ad":
+        return 1
+    if "control" in v_low:
+        return 0
+    return None
+
+def convert_age(cell):
+    v = _extract_value(cell)
+    if v is None:
+        return None
+    match = re.search(r"[-+]?\d*\.?\d+", v)
+    if match:
+        try:
+            age_val = float(match.group())
+            if 0 < age_val < 120:
+                return age_val
+        except Exception:
+            return None
+    return None
+
+def convert_gender(cell):
+    v = _extract_value(cell)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {"female", "f"}:
+        return 0
+    if v_low in {"male", "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 (only if trait available)
+if trait_row is not None:
+    assert 'clinical_data' in globals(), "clinical_data is not available from the previous step."
+    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)
+    print(clinical_preview)
+    # Save clinical data (keep index to preserve feature labels)
+    out_dir = os.path.dirname(out_clinical_data_file)
+    os.makedirs(out_dir, 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 Identifier Review
+# Based on the previous step's observation for GSE109887
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 6: 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 7: Gene Identifier Mapping
+# Decide the identifier and gene symbol columns based on annotation preview:
+# - Expression data index matches the 'ID' column in annotation
+# - Gene symbols appear to be in the 'ORF' column
+
+# 1-2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')
+
+# 3. Apply mapping to convert probe/identifier-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 8: 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 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 info
+# Ensure pure Python bools for JSON serialization
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+is_trait_biased = bool(is_trait_biased)
+
+note = ("INFO: Illumina HumanHT-12 v4 expression data; identifiers mapped using annotation "
+        "('ID'->'ORF'); gene symbols normalized via NCBI synonyms. Clinical features include "
+        "Alzheimers_Disease trait, Age, Gender.")
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    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/Alzheimers_Disease/code/GSE117589.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE117589"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE117589"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE117589.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE117589.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE117589.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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  # iPSC/neuronal models SuperSeries with diagnosis groups strongly suggests mRNA expression data.
+
+# 2) Variable availability and converters
+
+# Keys inferred from the Sample Characteristics Dictionary provided
+trait_row = 2  # diagnosis: normal vs sporadic Alzheimer's disease
+age_row = 1    # subject: e.g., '72M' (age+sex together)
+gender_row = 1 # subject: e.g., '72M' (age+sex together)
+
+def _after_colon(x):
+    if x is None:
+        return None
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if "alzheimer" in v:
+        return 1
+    if v == "normal":
+        return 0
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    m = re.search(r'(\d+(?:\.\d+)?)', val)
+    if not m:
+        return None
+    try:
+        num = float(m.group(1))
+        return int(num) if num.is_integer() else num
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+
+    # Prefer pattern like "72M" / "72 F"
+    m = re.match(r'^\s*\d+(?:\.\d+)?\s*([mf])\s*$', v)
+    if m:
+        return 1 if m.group(1) == 'm' else 0
+
+    if re.search(r'\bfemale\b', v):
+        return 0
+    if re.search(r'\bmale\b', v):
+        return 1
+
+    if v == 'f':
+        return 0
+    if 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 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, n=5)
+    print(clinical_preview)
+    # 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
+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
+# Map probe IDs ('ID') to human gene symbols parsed from the 'Description' column
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')
+
+# Convert probe-level data to gene-level expression using provided utility
+probe_data = gene_data  # from previous step
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 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
+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. Assess bias and remove biased demographic features if necessary
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Age and Gender parsed from combined 'subject' field (e.g., '72M'); trait from 'diagnosis' row."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, 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/Alzheimers_Disease/code/GSE122063.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE122063"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE122063"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE122063.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE122063.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE122063.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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 (Agilent Human 8x60k gene expression arrays)
+is_gene_available = True
+
+# 2) Variable availability (from Sample Characteristics Dictionary)
+trait_row = 0     # patient diagnosis: AD / Control / Vascular dementia
+age_row = 6       # age: numeric
+gender_row = 5    # Sex: Male/Female
+
+# 2.2) Converters
+def _after_colon(value):
+    if value is None:
+        return None
+    if not isinstance(value, str):
+        return value
+    parts = value.split(":", 1)
+    s = parts[1] if len(parts) > 1 else parts[0]
+    return s.strip().strip('"').strip("'")
+
+def convert_trait(value):
+    s = _after_colon(value)
+    if s is None:
+        return None
+    s_low = s.lower()
+    # Map Alzheimer's disease to 1; Control and Vascular dementia to 0 (non-AD)
+    if "alzheimer" in s_low:  # covers "alzheimer's disease" and variants
+        return 1
+    if any(k in s_low for k in ["control", "non-demented", "non demented", "healthy", "normal"]):
+        return 0
+    if any(k in s_low for k in ["vascular", "vad"]):
+        return 0
+    return None
+
+def convert_age(value):
+    s = _after_colon(value)
+    if s is None:
+        return None
+    s = s.replace(",", "").strip()
+    try:
+        return float(s)
+    except Exception:
+        return None
+
+def convert_gender(value):
+    s = _after_colon(value)
+    if s is None:
+        return None
+    s_low = s.lower()
+    if s_low in ["female", "f", "woman", "women"]:
+        return 0
+    if s_low in ["male", "m", "man", "men"]:
+        return 1
+    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 clinical data 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)
+    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("\nrequires_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'            # Matches the numeric probe IDs in gene_data index (e.g., '4', '5', ...)
+gene_symbol_col = 'GENE_SYMBOL'  # Contains gene symbols like 'HEBP1', 'KCNE4'
+
+# 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
+
+# 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. Bias assessment and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Cast to native Python bool to avoid JSON serialization issues
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in unbiased_linked_data.columns) and (unbiased_linked_data[trait].notna().any()))
+is_trait_biased = bool(is_trait_biased)
+
+note = ("INFO: Trait encoded as AD=1, Control/VaD=0. "
+        "Agilent Human 8x60k v2; dual channel processed as single channel. "
+        "Brain regions: frontal and temporal cortex.")
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    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/Alzheimers_Disease/code/GSE132903.py

@@ -0,0 +1,229 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE132903"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE132903"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE132903.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE132903.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE132903.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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  # Illumina Human HT-12 v4 mRNA arrays indicate gene expression data
+
+# 2. Variable availability from the provided Sample Characteristics Dictionary
+trait_row = 3     # diagnosis: ND/AD
+age_row = 2       # expired_age (years): values including numbers and "90+"
+gender_row = 1    # Sex: male/female
+
+# 2.2 Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    parts = str(x).split(':', 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    # Cases vs Controls mapping
+    if v_low in {'ad', "alzheimer's disease", 'alzheimers disease', 'alzheimers', 'alzheimer disease'}:
+        return 1
+    if v_low in {'nd', 'control', 'non-demented', 'non demented', 'non-demented control', 'normal'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v = v.strip()
+    # Handle formats like "90+"
+    v = v.replace('+', '')
+    # Extract leading number
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group())
+        # Ages are in years; cast to float to keep continuous type
+        return age_val
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {'male', 'm'}:
+        return 1
+    if v_low in {'female', 'f'}:
+        return 0
+    return None
+
+# 3. Save initial filtering 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,
+        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 of selected clinical features:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+else:
+    print("Trait data not available; skipping clinical feature extraction.")
+
+# For transparency in this step
+print({
+    "is_gene_available": is_gene_available,
+    "trait_row": trait_row,
+    "age_row": age_row,
+    "gender_row": gender_row
+})
+
+# 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_ prefixes indicate 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 the columns for probe IDs and gene symbols based on the annotation preview:
+# Probe identifier column: 'ID' (e.g., ILMN_1343***)
+# Gene symbol column: 'Symbol'
+
+# 1-2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 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 json
+
+# 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. Assess 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
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_count = len([c for c in unbiased_linked_data.columns if c not in covariate_cols])
+
+# Ensure pure Python bools for JSON serialization
+is_gene_available_final = bool(gene_cols_count > 0)
+is_trait_available_final = bool((trait in unbiased_linked_data.columns) and (unbiased_linked_data[trait].notna().sum() > 0))
+is_trait_biased = bool(is_trait_biased)
+
+note = "INFO: Illumina HT-12 V4 probes mapped to gene symbols; split multi-gene probes; ages like '90+' parsed as 90."
+
+try:
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available_final,
+        is_trait_available=is_trait_available_final,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+except TypeError:
+    # If JSON serialization fails (e.g., due to numpy.bool_), reset the JSON file and retry once
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+    with open(json_path, 'w') as f:
+        json.dump({}, f)
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available_final,
+        is_trait_available=is_trait_available_final,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+# 6. Save usable linked data
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Alzheimers_Disease/code/GSE137202.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE137202"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE137202"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE137202.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE137202.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE137202.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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
+is_gene_available = True  # Affymetrix PrimeView whole-genome expression profiling (mRNA) -> gene expression available
+
+# Map trait to genotype status (mutated AD models vs WT). No age/gender in cell lines.
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _after_colon(val):
+    if val is None:
+        return None
+    s = str(val)
+    parts = s.split(":", 1)
+    s = parts[1] if len(parts) == 2 else parts[0]
+    return s.strip().lower()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    # WT vs AD model (mutated)
+    if "wild" in v:
+        return 0
+    if "mut" in v or "app" in v or "psen" in v:
+        return 1
+    return None
+
+def convert_age(x):
+    # Not available in this dataset; safe general parser if ever present
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    # Extract first numeric token
+    import re
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 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
+)
+
+# Clinical feature extraction if 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)
+    print(preview)
+
+    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
+# Select 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')
+
+# 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 json
+
+# 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. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Compute availability flags based on actual data and coerce to native Python bool
+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 (linked_data[trait].notna().sum() > 0))
+
+# 3. Handle missing values
+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)
+is_trait_biased = bool(is_trait_biased)  # ensure native bool
+
+# 5. Final validation and save cohort info
+note = "INFO: Cell line AD model (WT vs mutated); no Age/Gender covariates available."
+try:
+    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
+    )
+except TypeError:
+    # If JSON serialization fails (e.g., corrupted existing file), recreate a clean metadata file and retry
+    if os.path.exists(json_path):
+        os.remove(json_path)
+    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 dataset 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/Alzheimers_Disease/code/GSE139384.py

@@ -0,0 +1,229 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE139384"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE139384"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE139384.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE139384.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE139384.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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  # Illumina HumanHT-12 v4 Expression BeadChip indicates mRNA expression data
+
+# 2) Variable availability and conversion functions
+
+# From the sample characteristics dictionary in the prompt, we infer:
+# - trait_row: 0 (contains clinical phenotypes for ALS/PDC and subject ids like AD/CT to infer AD status)
+# - age_row: 2 (mostly contains 'age: NN', with occasional gender entries we will ignore)
+# - gender_row: 1 (contains 'gender: Male/Female'; will ignore entries with clinical phenotypes)
+
+trait_row = 0
+age_row = 2
+gender_row = 1
+
+def _after_colon(value: str) -> str:
+    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):
+    """
+    Map Alzheimer's Disease status to binary:
+    - 1: Alzheimer's Disease
+      Heuristics:
+        * subject id contains 'AD' (e.g., 'subject id: AD3')
+        * 'clinical phenotypes' contains 'Alzheimer'
+    - 0: Non-AD (Healthy control or other diseases like ALS/PDC)
+      Heuristics:
+        * subject id contains 'CT'
+        * 'clinical phenotypes' contains 'healthy control', 'als', 'pdc'
+    - None: unknown/irrelevant entries
+    """
+    if value is None:
+        return None
+    s = str(value).lower()
+    v = _after_colon(value).lower()
+
+    # Direct Alzheimer detection
+    if "alzheimer" in s or "alzheimer" in v:
+        return 1
+
+    # Subject ID heuristics
+    if "subject id" in s:
+        # AD subjects
+        if re.search(r"\bad\d*\b", v):
+            return 1
+        # Controls
+        if re.search(r"\bct\d*\b", v) or "control" in v:
+            return 0
+
+    # Clinical phenotype heuristics for non-AD
+    if "clinical phenotypes" in s:
+        if "healthy control" in v or "control" in v:
+            return 0
+        if "als" in v or "pdc" in v:
+            return 0
+
+    return None
+
+def convert_age(value):
+    """
+    Extract age as continuous numeric value if present; else None.
+    """
+    if value is None:
+        return None
+    v = _after_colon(value)
+    m = re.search(r"\d+", v)
+    return int(m.group()) if m else None
+
+def convert_gender(value):
+    """
+    Map gender to binary: female -> 0, male -> 1. Unknown/other -> None.
+    Ignore entries that are not gender fields.
+    """
+    if value is None:
+        return None
+    s = str(value).lower()
+    v = _after_colon(value).lower()
+    if "gender" not in s:
+        return None
+    if "female" in v:
+        return 0
+    if "male" in v:
+        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 (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_result = preview_df(selected_clinical_df)
+    print(preview_result)
+
+    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
+# Illumina probe IDs (ILMN_*) 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
+# Decide the appropriate columns for probe IDs and gene symbols based on the preview:
+# - Probe identifiers match the 'ID' column (e.g., ILMN_1343***).
+# - Gene symbols are in the 'Symbol' column.
+
+# 1-2. Build the mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 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
+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)
+
+# Ensure clinical data is available in memory; if not, load from disk
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 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. Evaluate bias and drop 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: Normalized gene symbols using NCBI synonyms; linked clinical data; applied systematic missing value handling and bias checks."
+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/Alzheimers_Disease/code/GSE167559.py

@@ -0,0 +1,122 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE167559"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE167559"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE167559.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE167559.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE167559.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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) Determine gene expression availability (miRNA-only dataset => not suitable)
+is_gene_available = False
+
+# 2) Variable availability and converters based on provided Sample Characteristics Dictionary
+# Keys identified from the dictionary:
+# 0: tissue: serum
+# 1: diagnosis: NPH
+# 2: age: ...
+# 3: Sex: male/female
+# 4: apoe4: 0/1/2
+# For the AD trait, diagnosis is constant (all NPH). Therefore, trait is not available for association analysis.
+trait_row = None
+age_row = 2
+gender_row = 3
+
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(v):
+    # Binary: 1 = Alzheimer's disease, 0 = non-AD; unknown -> None
+    # This function won't be used since trait_row is None, but defined for completeness.
+    s = _after_colon(v)
+    if s is None or s == '':
+        return None
+    s_low = s.lower()
+    if 'alzheimer' in s_low or 'alzheimer’s' in s_low or 'alzheim' in s_low:
+        return 1
+    # Map other dementia/control labels to 0 when reasonably confident
+    negatives = ['control', 'healthy', 'normal', 'nph', 'dlb', 'lewy', 'vascular', 'vad', 'vci', 'mci', 'non-ad', 'non alzheimer']
+    if any(tok in s_low for tok in negatives):
+        return 0
+    return None
+
+def convert_age(v):
+    s = _after_colon(v)
+    if s is None or s == '':
+        return None
+    try:
+        return float(s)
+    except Exception:
+        return None
+
+def convert_gender(v):
+    # Binary: female -> 0, male -> 1; unknown -> None
+    s = _after_colon(v)
+    if s is None or s == '':
+        return None
+    s_low = s.lower()
+    if s_low in ['male', 'm']:
+        return 1
+    if s_low in ['female', 'f']:
+        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 (skip since trait_row is None)
+if (trait_row is not None) and ('clinical_data' in globals()):
+    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)

output/preprocess/Alzheimers_Disease/code/GSE185909.py

@@ -0,0 +1,218 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE185909"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE185909"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE185909.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE185909.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE185909.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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
+
+# Determine data availability based on provided background and characteristics
+is_gene_available = True  # Nimblegen human expression array with RMA preprocessing
+trait_row = 0  # diagnosis: AD/MCI/NCI
+age_row = 2    # age_death: numeric
+gender_row = 1 # Sex: Male/Female
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip().strip('"').strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == '' or v.lower() in {'na', 'n/a', 'nan', 'none', 'unknown'}:
+        return None
+    vl = v.lower().replace('’', "'").replace('_', ' ').strip()
+    # Map AD vs non-AD (NCI and MCI considered non-AD)
+    if vl in {'ad', "alzheimer's disease", 'alzheimers disease', 'alzheimer disease'}:
+        return 1
+    if vl in {'nci', 'mci', 'control', 'cn', 'ctl', 'non-ad', 'no cognitive impairment', 'mild cognitive impairment'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == '' or v.lower() in {'na', 'n/a', 'nan', 'none', 'unknown'}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        vv = ''.join(ch for ch in v if (ch.isdigit() or ch == '.' or ch == '-'))
+        try:
+            return float(vv) if vv not in {'', '-', '.'} else None
+        except Exception:
+            return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower().strip()
+    if vl in {'male', 'm'}:
+        return 1
+    if vl in {'female', 'f'}:
+        return 0
+    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
+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("Clinical features preview:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    # Preserve feature names (rows) when saving
+    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
+# Determine the identifier column in annotation that matches expression IDs
+expr_ids = set(gene_data.index.astype(str))
+
+# Candidate columns for probe/identifier and gene symbol
+candidate_id_cols = ['ID', 'ID_REF', 'GB_ACC', 'ProbeID', 'PROBE_ID', 'Accession', 'ACCESSION']
+candidate_gene_cols = [
+    'Gene Symbol', 'GENE_SYMBOL', 'SYMBOL', 'Symbol', 'Gene symbol',
+    'gene_assignment', 'GENE', 'GeneName', 'Gene', 'GENE_NAME',
+    'Entrez_Gene', 'GENE_SYMBOLS', 'GeneSymbol', 'Description', 'DESCRIPTION'
+]
+
+# Choose id column by maximum overlap with expression IDs
+best_id_col = None
+best_overlap = -1
+for col in candidate_id_cols:
+    if col in gene_annotation.columns:
+        overlap = len(expr_ids.intersection(set(gene_annotation[col].astype(str))))
+        if overlap > best_overlap:
+            best_overlap = overlap
+            best_id_col = col
+
+# Fallback to 'ID' if nothing else found
+if best_id_col is None and 'ID' in gene_annotation.columns:
+    best_id_col = 'ID'
+
+# Choose gene symbol column by availability with a preferred order
+best_gene_col = None
+for col in candidate_gene_cols:
+    if col in gene_annotation.columns:
+        best_gene_col = col
+        break
+
+# As a final fallback, use DESCRIPTION if nothing else is found
+if best_gene_col is None and 'DESCRIPTION' in gene_annotation.columns:
+    best_gene_col = 'DESCRIPTION'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=best_id_col, gene_col=best_gene_col)
+
+# Apply mapping to convert probe-level 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
+
+# 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. Link 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 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 (ensure native Python bools)
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
+
+note = "INFO: Gene IDs mapped via annotation DESCRIPTION using heuristic symbol extraction; symbols normalized using NCBI synonym mapping."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    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/Alzheimers_Disease/code/GSE214417.py

@@ -0,0 +1,96 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE214417"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE214417"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE214417.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE214417.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE214417.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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 availability
+# This GEO SuperSeries likely contains gene expression (non-miRNA, non-methylation) data.
+is_gene_available = True
+
+# 2) Variable availability and converters
+# Human trait data is not available in this mouse model dataset; gender is constant (Male only); age is murine.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x).strip()
+    # Extract substring after the first colon if present
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s
+
+def convert_trait(x):
+    # Not available for human AD status in this mouse dataset
+    return None
+
+def convert_age(x):
+    # Not available (murine age not used for human analysis in this context)
+    return None
+
+def convert_gender(x):
+    # Not available (murine data and constant Male)
+    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 since trait_row is None)
+# If trait_row were 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)
+#     selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Alzheimers_Disease/code/GSE243243.py

@@ -0,0 +1,133 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE243243"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE243243"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/GSE243243.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/GSE243243.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/GSE243243.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/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 based on background information
+# Affymetrix microarrays for RNA expression indicate gene expression data is available.
+is_gene_available = True
+
+# Step 2: Variable availability and conversion functions
+
+# Based on the sample characteristics, none of the keys map to Alzheimer's Disease status, age, or gender.
+# Keys present: 0 -> ASO treatments, 1 -> treatment time (h), 2 -> dose (microm)
+trait_row = None     # No AD case/control or diagnosis info
+age_row = None       # No age info
+gender_row = None    # No gender info
+
+def _get_value_after_colon(x):
+    if x is None:
+        return None
+    try:
+        s = str(x)
+    except Exception:
+        return None
+    if ':' in s:
+        return s.split(':', 1)[1].strip()
+    return s.strip()
+
+def convert_trait(x):
+    # Generic AD/control mapper (not used here since trait_row is None)
+    val = _get_value_after_colon(x)
+    if val is None or val == '':
+        return None
+    v = val.strip().lower()
+    # Map common AD labels
+    ad_pos = {'ad', 'alzheimer', 'alzheimer’s disease', 'alzheimer disease', 'alzheimers', 'case', 'patient'}
+    ad_neg = {'control', 'healthy', 'normal', 'non-ad', 'non ad', 'non_alzheimer'}
+    if v in ad_pos:
+        return 1
+    if v in ad_neg:
+        return 0
+    # Heuristic keywords
+    if 'alzheimer' in v or v == 'ad':
+        return 1
+    if 'control' in v or 'healthy' in v or 'normal' in v:
+        return 0
+    return None
+
+def convert_age(x):
+    # Parse age to continuous numeric (years) if provided; not used here (age_row is None)
+    val = _get_value_after_colon(x)
+    if val is None or val == '':
+        return None
+    s = val.strip().lower()
+    # Remove common units or text
+    for tok in ['years', 'year', 'yrs', 'yr', 'y', 'yo', 'age', 'ages']:
+        s = s.replace(tok, '')
+    s = s.replace('~', '').replace('+', '').replace('>', '').replace('<', '')
+    try:
+        return float(s.strip())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Map female->0, male->1
+    val = _get_value_after_colon(x)
+    if val is None or val == '':
+        return None
+    v = val.strip().lower()
+    if v in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if v in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    return None
+
+# Step 3: 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
+)
+
+# Step 4: Clinical feature extraction (skip since trait_row is None)
+# If trait_row was available, we would extract and save clinical data as follows:
+# 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/Alzheimers_Disease/code/TCGA.py

@@ -0,0 +1,53 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alzheimers_Disease/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Alzheimers_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alzheimers_Disease/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Alzheimers_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Discover available TCGA subdirectories
+all_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Select cohort directory matching Alzheimer's Disease (none of TCGA cohorts should match; be conservative)
+keywords = {"alzheimer", "alzheimers", "alzheimer's", "dementia", "neurodegenerative"}
+candidates = [d for d in all_subdirs if any(k in d.lower() for k in keywords)]
+
+selected_dir = None
+if candidates:
+    # If any unexpected match appears, choose the one with the longest matching keyword (most specific)
+    def match_score(name):
+        lname = name.lower()
+        return max((len(k) for k in keywords if k in lname), default=0)
+    candidates.sort(key=lambda x: match_score(x), reverse=True)
+    selected_dir = candidates[0]
+
+if selected_dir is None:
+    # No relevant TCGA cohort for Alzheimer's Disease; record and stop further processing.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_path = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_path)
+
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+    print(clinical_df.columns.tolist())

output/preprocess/Alzheimers_Disease/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE243243": {
-    "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": 93
-  },
-  "GSE214417": {
-    "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": 24
-  },
-  "GSE185909": {
-    "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": 35
-  },
-  "GSE167559": {
-    "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
-  },
-  "GSE139384": {
-    "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": 12
-  },
-  "GSE137202": {
-    "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": 30
-  },
-  "GSE132903": {
-    "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": 195
-  },
-  "GSE122063": {
-    "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": 100
-  },
-  "GSE117589": {
-    "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
-  },
-  "GSE109887": {
-    "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": 78
-  },
-  "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
-  }
-}
+{"GSE243243": {"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}, "GSE214417": {"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}, "GSE185909": {"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": 35, "note": "INFO: Gene IDs mapped via annotation DESCRIPTION using heuristic symbol extraction; symbols normalized using NCBI synonym mapping."}, "GSE167559": {"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}, "GSE139384": {"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": 33, "note": "INFO: Normalized gene symbols using NCBI synonyms; linked clinical data; applied systematic missing value handling and bias checks."}, "GSE137202": {"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": 30, "note": "INFO: Cell line AD model (WT vs mutated); no Age/Gender covariates available."}, "GSE132903": {"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": 195, "note": "INFO: Illumina HT-12 V4 probes mapped to gene symbols; split multi-gene probes; ages like '90+' parsed as 90."}, "GSE122063": {"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": 136, "note": "INFO: Trait encoded as AD=1, Control/VaD=0. Agilent Human 8x60k v2; dual channel processed as single channel. Brain regions: frontal and temporal cortex."}, "GSE117589": {"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": 31, "note": "INFO: Age and Gender parsed from combined 'subject' field (e.g., '72M'); trait from 'diagnosis' row."}, "GSE109887": {"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": 78, "note": "INFO: Illumina HumanHT-12 v4 expression data; identifiers mapped using annotation ('ID'->'ORF'); gene symbols normalized via NCBI synonyms. Clinical features include Alzheimers_Disease trait, Age, Gender."}, "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/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE118336.csv

@@ -1,2 +1,2 @@
-,0,1,2
-Amyotrophic_Lateral_Sclerosis,0.0,1.0,1.0
+,GSM3325490,GSM3325491,GSM3325492,GSM3325493,GSM3325494,GSM3325495,GSM3325496,GSM3325497,GSM3325498,GSM3325499,GSM3325500,GSM3325501,GSM3325502,GSM3325503,GSM3325504,GSM3325505,GSM3325506,GSM3325507,GSM3325508,GSM3325509,GSM3325510,GSM3325511,GSM3325512,GSM3325513,GSM3325514,GSM3325515,GSM3325516,GSM3325517,GSM3325518,GSM3325519,GSM3325520,GSM3325521,GSM3325522,GSM3325523,GSM3325524,GSM3325525,GSM3325526,GSM3325527,GSM3325528,GSM3325529,GSM3325530,GSM3325531,GSM3325532,GSM3325533,GSM3325534,GSM3325535,GSM3325536,GSM3325537,GSM3325538,GSM3325539,GSM3325540,GSM3325541,GSM3325542,GSM3325543,GSM3325544,GSM3325545,GSM3325546,GSM3325547,GSM3325548,GSM3325549
+Amyotrophic_Lateral_Sclerosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE139384.csv

@@ -1,4 +1,4 @@
-Sample_0,Sample_1,Sample_2,Sample_3,Sample_4,Sample_5,Sample_6,Sample_7,Sample_8,Sample_9,Sample_10,Sample_11,Sample_12,Sample_13,Sample_14,Sample_15
-0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,,,,,,
-,66.0,77.0,70.0,74.0,76.0,60.0,79.0,71.0,63.0,65.0,81.0,73.0,72.0,75.0,85.0
-,,0.0,1.0,,,,,,,,,,,,
+,GSM4140293,GSM4140294,GSM4140295,GSM4140296,GSM4140297,GSM4140298,GSM4140299,GSM4140300,GSM4140301,GSM4140302,GSM4140303,GSM4140304,GSM4140305,GSM4140306,GSM4140307,GSM4140308,GSM4140309,GSM4140310,GSM4140311,GSM4140312,GSM4140313,GSM4140314,GSM4140315,GSM4140316,GSM4140317,GSM4140318,GSM4140319,GSM4140320,GSM4140321,GSM4140322,GSM4140323,GSM4140324,GSM4140325
+Amyotrophic_Lateral_Sclerosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0
+Age,,,,,,,,,,,,,66.0,77.0,70.0,74.0,76.0,60.0,79.0,71.0,63.0,65.0,70.0,81.0,70.0,74.0,73.0,72.0,72.0,75.0,85.0,76.0,74.0
+Gender,,,,,,,,,,,,,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0

output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE26927.csv

@@ -1,4 +1,4 @@
-,GSM663008,GSM663009,GSM663010,GSM663011,GSM663012,GSM663013,GSM663014,GSM663015,GSM663016,GSM663017,GSM663018,GSM663019,GSM663020,GSM663021,GSM663022,GSM663023,GSM663024,GSM663025,GSM663026,GSM663027,GSM663028,GSM663029,GSM663030,GSM663031,GSM663032,GSM663033,GSM663034,GSM663035,GSM663036,GSM663037,GSM663038,GSM663039,GSM663040,GSM663041,GSM663042,GSM663043,GSM663044,GSM663045,GSM663046,GSM663047,GSM663048,GSM663049,GSM663050,GSM663051,GSM663052,GSM663053,GSM663054,GSM663055,GSM663056,GSM663057,GSM663058,GSM663059,GSM663060,GSM663061,GSM663062,GSM663063,GSM663064,GSM663065,GSM663066,GSM663067,GSM663068,GSM663069,GSM663070,GSM663071,GSM663072,GSM663073,GSM663074,GSM663075,GSM663076,GSM663077,GSM663078,GSM663079,GSM663080,GSM663081,GSM663082,GSM663083,GSM663084,GSM663085,GSM663086,GSM663087,GSM663088,GSM663089,GSM663090,GSM663091,GSM663092,GSM663093,GSM663094,GSM663095,GSM663096,GSM663097,GSM663098,GSM663099,GSM663100,GSM663101,GSM663102,GSM663103,GSM663104,GSM663105,GSM663106,GSM663107,GSM663108,GSM663109,GSM663110,GSM663111,GSM663112,GSM663113,GSM663114,GSM663115,GSM663116,GSM663117,GSM663118,GSM663119,GSM663120,GSM663121,GSM663122,GSM663123,GSM663124,GSM663125
-Amyotrophic_Lateral_Sclerosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+GSM663008,GSM663009,GSM663010,GSM663011,GSM663012,GSM663013,GSM663014,GSM663015,GSM663016,GSM663017,GSM663018,GSM663019,GSM663020,GSM663021,GSM663022,GSM663023,GSM663024,GSM663025,GSM663026,GSM663027,GSM663028,GSM663029,GSM663030,GSM663031,GSM663032,GSM663033,GSM663034,GSM663035,GSM663036,GSM663037,GSM663038,GSM663039,GSM663040,GSM663041,GSM663042,GSM663043,GSM663044,GSM663045,GSM663046,GSM663047,GSM663048,GSM663049,GSM663050,GSM663051,GSM663052,GSM663053,GSM663054,GSM663055,GSM663056,GSM663057,GSM663058,GSM663059,GSM663060,GSM663061,GSM663062,GSM663063,GSM663064,GSM663065,GSM663066,GSM663067,GSM663068,GSM663069,GSM663070,GSM663071,GSM663072,GSM663073,GSM663074,GSM663075,GSM663076,GSM663077,GSM663078,GSM663079,GSM663080,GSM663081,GSM663082,GSM663083,GSM663084,GSM663085,GSM663086,GSM663087,GSM663088,GSM663089,GSM663090,GSM663091,GSM663092,GSM663093,GSM663094,GSM663095,GSM663096,GSM663097,GSM663098,GSM663099,GSM663100,GSM663101,GSM663102,GSM663103,GSM663104,GSM663105,GSM663106,GSM663107,GSM663108,GSM663109,GSM663110,GSM663111,GSM663112,GSM663113,GSM663114,GSM663115,GSM663116,GSM663117,GSM663118,GSM663119,GSM663120,GSM663121,GSM663122,GSM663123,GSM663124,GSM663125
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output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE68608.csv

@@ -1,2 +1,2 @@
-ID_REF,1
-,1.0
+,GSM1676853,GSM1676854,GSM1676855,GSM1676856,GSM1676857,GSM1676858,GSM1676859,GSM1676860,GSM1676861,GSM1676862,GSM1676863
+Amyotrophic_Lateral_Sclerosis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0

output/preprocess/Amyotrophic_Lateral_Sclerosis/code/GSE118336.py

@@ -0,0 +1,254 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE118336"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE118336"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE118336.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE118336.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE118336.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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  # HTA2.0 human transcriptome array indicates mRNA expression profiling.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify rows in the sample characteristics
+trait_row = 1     # 'genotype' with values including WT/WT and H517D variants; map to ALS status (case/control)
+age_row = None    # No human age available; 'time (differentiation ...)' is not human age
+gender_row = None # No gender information available
+
+# 2.2 Converters
+def _extract_value(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        return None
+    # Take value after the last colon to be robust to extra colons
+    parts = x.split(':')
+    val = parts[-1].strip() if parts else None
+    return val if val != '' else None
+
+def convert_trait(x):
+    """
+    Binary mapping for ALS status proxied by FUS H517D mutation status in genotype:
+    - 1 if genotype contains H517D (mutant; disease model)
+    - 0 if genotype is WT/WT (control)
+    - None otherwise
+    """
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower().replace(' ', '')
+    # Any presence of H517D indicates mutant (case)
+    if 'h517d' in v_low:
+        return 1
+    # Explicit wildtype control
+    if 'wt/wt' in v_low:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not applicable; return None
+    return None
+
+def convert_gender(x):
+    # Not applicable; 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 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=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    print(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
+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 re
+
+# Determine which annotation column best matches the probe IDs in gene_data.index
+gene_ids = set(gene_data.index.astype(str))
+
+best_col = None
+best_mode = None  # 'direct' or 'suffixed'
+best_overlap = -1
+
+# Try to match annotation columns to expression IDs
+for col in gene_annotation.columns:
+    col_values = gene_annotation[col].astype(str).str.strip()
+    col_values_norm = col_values.str.replace(r'\.0$', '', regex=True)
+
+    direct_set = set(col_values_norm)
+    # Common Affymetrix suffix used in this dataset is "_st"
+    suffixed_set = set(v + '_st' for v in col_values_norm)
+
+    # Compute overlaps
+    direct_overlap = len(direct_set & gene_ids)
+    suffixed_overlap = len(suffixed_set & gene_ids)
+
+    # Choose the best mode for this column
+    if direct_overlap > best_overlap:
+        best_overlap = direct_overlap
+        best_col = col
+        best_mode = 'direct'
+    if suffixed_overlap > best_overlap:
+        best_overlap = suffixed_overlap
+        best_col = col
+        best_mode = 'suffixed'
+
+print(f"[DEBUG] Best matching annotation column: {best_col} (mode={best_mode}), overlap={best_overlap}")
+
+# Fail fast if no overlap found
+if best_overlap == 0 or best_col is None:
+    raise ValueError(
+        "No overlap between annotation identifiers and expression IDs was found.\n"
+        "The annotation preview shows IDs like 'TC01000001.hg.1' while the expression IDs look like '2824546_st'.\n"
+        "This suggests the SOFT annotation corresponds to transcript-cluster IDs (TC...), whereas the matrix uses "
+        "probeset IDs with '_st' suffix. Please provide the correct platform (GPL) annotation for probeset-level IDs."
+    )
+
+# Prepare a mapping ID column in the annotation that aligns with gene_data.index
+mapping_id_col = "__probe_id_for_mapping__"
+ann_values = gene_annotation[best_col].astype(str).str.strip().str.replace(r'\.0$', '', regex=True)
+if best_mode == 'direct':
+    gene_annotation[mapping_id_col] = ann_values
+else:
+    gene_annotation[mapping_id_col] = ann_values + "_st"
+
+# Choose a gene-symbol containing column
+if 'gene_assignment' in gene_annotation.columns:
+    gene_col = 'gene_assignment'
+elif 'mrna_assignment' in gene_annotation.columns:
+    gene_col = 'mrna_assignment'
+else:
+    candidates = [c for c in gene_annotation.columns if re.search(r'symbol|gene', c, re.IGNORECASE)]
+    gene_col = candidates[0] if candidates else gene_annotation.columns[0]
+
+print(f"[DEBUG] Selected gene symbol column: {gene_col}")
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=mapping_id_col, gene_col=gene_col)
+
+# Validate that mapping_df actually intersects with expression IDs
+id_intersection = set(mapping_df['ID']) & gene_ids
+print(f"[DEBUG] Mapping rows intersecting expression IDs: {len(id_intersection)}")
+if len(id_intersection) == 0:
+    raise ValueError(
+        "Mapping failed: No overlap between mapping_df IDs and expression_df index after preprocessing. "
+        "Likely due to mismatched identifier systems (TC... vs probeset '_st')."
+    )
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+print(f"[DEBUG] Gene-level data shape after mapping: {gene_data.shape}")
+
+# Step 7: Data Normalization and Linking
+import os
+import builtins
+
+# 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. Assess 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
+# Ensure native Python bools for JSON serialization
+is_gene_available_flag = builtins.bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_flag = builtins.bool((trait in unbiased_linked_data.columns) and builtins.bool(unbiased_linked_data[trait].notna().any()))
+is_trait_biased_flag = builtins.bool(is_trait_biased)
+
+note = "INFO: Trait derived from genotype (FUS H517D) in iPSC-derived MNs; HTA2.0 array."
+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_flag,
+    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/Amyotrophic_Lateral_Sclerosis/code/GSE139384.py

@@ -0,0 +1,213 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE139384"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE139384"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE139384.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE139384.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE139384.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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 background info
+is_gene_available = True  # Illumina HumanHT-12 v4 Expression BeadChip (mRNA expression)
+
+# Identify rows in the sample characteristics dictionary
+trait_row = 0   # Contains clinical phenotypes (ALS, PDC, ALS+D, PDC+A) and subject IDs (CT#, AD#)
+age_row = 2     # Contains most age values
+gender_row = 1  # Contains gender values
+
+# Conversion utilities
+def _after_colon(value):
+    if value is None:
+        return None
+    if not isinstance(value, str):
+        try:
+            value = str(value)
+        except Exception:
+            return None
+    parts = value.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    v = v.strip()
+    return v if v != "" else None
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower().replace('`', "'").strip()
+    # Direct phenotype mappings
+    if vl in {"als", "als+d"}:
+        return 1
+    if vl in {"pdc", "pdc+a", "alzheimer's disease", "alzheimer’s disease", "healthy control", "control"}:
+        return 0
+    # Heuristic: subject IDs indicating controls
+    # CT# -> Healthy Control; AD# -> Alzheimer's Disease
+    if vl.startswith("ct"):
+        return 0
+    if vl.startswith("ad"):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Filter out non-age entries inadvertently present in this row
+    if v.lower().startswith("gender") or v.lower().startswith("tissue"):
+        return None
+    # Extract numeric age
+    try:
+        vnum = ''.join(ch for ch in v if (ch.isdigit() or ch == '.' or ch == '-'))
+        if vnum == "" or vnum == "-":
+            return None
+        return float(vnum)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl.startswith("female"):
+        return 0
+    if vl.startswith("male"):
+        return 1
+    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, preview, and save
+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)
+    print(clinical_preview)
+
+    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
+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 appropriate columns in the annotation for probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'Symbol' if 'Symbol' in gene_annotation.columns else 'ILMN_Gene'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 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 = 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:
+    # Fallback: load previously saved clinical features
+    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 assessment (remove biased covariates if needed)
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Derive availability flags from actual data status, coerce to native Python bool
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+has_trait_row = bool(isinstance(selected_clinical_df, pd.DataFrame) and (trait in selected_clinical_df.index))
+trait_non_na_any = bool(selected_clinical_df.loc[trait].notna().any()) if has_trait_row else False
+is_trait_available = bool(has_trait_row and trait_non_na_any)
+
+# 5. Final validation and save cohort metadata
+note = ("INFO: Illumina HumanHT-12 v4 platform; probe->gene mapping applied using SOFT annotation; "
+        "multi-mapped probes split equally and summed per gene; gene symbols normalized via NCBI synonyms; "
+        "trait mapped as ALS=1, non-ALS (PDC/AD/Control)=0.")
+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/Amyotrophic_Lateral_Sclerosis/code/GSE212131.py

@@ -0,0 +1,221 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE212131"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE212131"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE212131.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212131.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE212131.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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  # Affymetrix Human Exon 1.0ST microarray for mRNA indicates gene expression data is present.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics dictionary: {0: ['gender: Male', 'gender: Female']}
+trait_row = None         # All samples are ALS patients; no case-control variability provided.
+age_row = None           # No age information found.
+gender_row = 0           # Gender information is available.
+
+# Conversion functions
+def _extract_after_colon(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):
+    # Binary: ALS = 1, Control/Healthy = 0
+    v = _extract_after_colon(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower()
+    # Heuristics for ALS vs control
+    if 'als' in vl or 'amyotrophic lateral sclerosis' in vl:
+        return 1
+    if 'control' in vl or 'healthy' in vl or 'normal' in vl:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous: extract numeric age in years if present
+    v = _extract_after_colon(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower()
+    # Remove common units/words
+    for token in ['years', 'year', 'yrs', 'yr', 'y/o', 'yo', 'age']:
+        vl = vl.replace(token, '')
+    vl = vl.replace('~', ' ').replace('+', ' ').replace('approximately', ' ')
+    vl = ''.join(ch if ch.isdigit() or ch == '.' else ' ' for ch in vl)
+    try:
+        nums = [float(t) for t in vl.split() if t.replace('.', '', 1).isdigit()]
+        if len(nums) == 0:
+            return None
+        return nums[0]
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: Female = 0, Male = 1
+    v = _extract_after_colon(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower().strip()
+    if vl in ['male', 'm', 'man', 'boy']:
+        return 1
+    if vl in ['female', 'f', 'woman', 'girl']:
+        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 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=False)
+
+# 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
+# Map probe IDs to gene symbols using the annotation, then aggregate to gene-level expression.
+
+# 1-2. Identify identifier and gene symbol columns and build mapping dataframe
+# Probe/ID column: 'ID' (matches expression data index)
+# Gene symbol info column: 'gene_assignment'
+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
+probe_data = gene_data  # keep original probe-level data
+gene_data = apply_gene_mapping(probe_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 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 context
+trait_available = ('trait_row' in locals()) and (trait_row is not None)
+
+# 2-6. Link data and downstream processing only if trait data is available
+linked_data = None
+if trait_available:
+    # Ensure clinical features are available; create if missing
+    if 'selected_clinical_df' not in locals():
+        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
+        )
+
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias assessment and removal of biased demographics
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 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 clinical and gene data; performed missing value handling and bias checks."
+    )
+
+    # 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 unavailable; skip linking and mark as not usable
+    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 in this series; only gender present. Skipping linkage and marking cohort as not usable for association analysis."
+    )

output/preprocess/Amyotrophic_Lateral_Sclerosis/code/GSE212134.py

@@ -0,0 +1,139 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE212134"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE212134"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE212134.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212134.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE212134.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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
+# Based on the SuperSeries title mentioning mRNA (gene expression) and microRNA, consider gene expression available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# From the sample characteristics dictionary: only gender information is available at key 0.
+# Trait (ALS) is constant or not explicitly available => not usable for association analysis.
+trait_row = None
+age_row = None
+gender_row = 0
+
+# 2.2 Data Type Conversion
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Generic mapper for case/control if present; returns 1 for ALS/case, 0 for control; else None.
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # Positive mappings
+    pos_terms = [
+        "als", "amyotrophic lateral sclerosis", "patient", "case",
+        "als patient", "als_case", "disease: als"
+    ]
+    neg_terms = ["control", "healthy", "normal", "non-als", "non als", "hc"]
+    if any(t == v or t in v for t in pos_terms):
+        return 1
+    if any(t == v or t in v for t in neg_terms):
+        return 0
+    return None
+
+def convert_age(x):
+    # Return age in years as float if parsable; else None.
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Extract first number (int/float)
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    num = float(m.group(1))
+    # Unit inference
+    if "month" in v or "mo" in v:
+        return num / 12.0
+    if "week" in v or "wk" in v or "wks" in v:
+        return num / 52.0
+    if "day" in v or "d " in v or v.endswith("d"):
+        return num / 365.25
+    # default assume years
+    return num
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.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 (skip if trait_row is None)
+if trait_row is not None and 'clinical_data' in globals():
+    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
+    )
+    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)

output/preprocess/Amyotrophic_Lateral_Sclerosis/code/GSE26927.py

@@ -0,0 +1,214 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE26927"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE26927"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE26927.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE26927.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE26927.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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
+# Illumina HumanRef-8 v2 BeadChip is a whole-genome expression array -> gene data available
+is_gene_available = True
+
+# 2) Variable Availability
+# From the provided Sample Characteristics Dictionary:
+# 0 -> disease: ...
+# 1 -> gender: M/F
+# 2 -> age at death (in years): ...
+trait_row = 0
+age_row = 2
+gender_row = 1
+
+# 2.2) Data Type Conversion Functions
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == '' or v == '?':
+        return None
+    v_low = v.lower()
+    # Map ALS to 1, other diseases to 0
+    if 'amyotrophic' in v_low and 'lateral' in v_low and 'sclerosis' in v_low:
+        return 1
+    # Common abbreviations or synonyms (safety)
+    if v_low in {'als', 'amyotrophic lateral sclerosis'}:
+        return 1
+    # For other disease labels in this dataset, map to 0
+    known_non_als = [
+        "huntington", "parkinson", "multiple sclerosis", "schizophrenia", "alzheimer"
+    ]
+    if any(k in v_low for k in known_non_als):
+        return 0
+    # If unknown label appears, default to None to avoid mislabeling
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v = str(v).strip()
+    if v in {'', '?', 'na', 'n/a', 'none'}:
+        return None
+    # Extract first numeric (int or float)
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).strip().lower()
+    if v_low in {'f', 'female'}:
+        return 0
+    if v_low in {'m', 'male'}:
+        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 is available)
+if trait_row is not None:
+    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
+    preview = preview_df(selected)
+    print(preview)
+
+    # Ensure output directory exists and save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected.to_csv(out_clinical_data_file, index=False)
+
+# 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 based on the annotation preview:
+# Probe IDs: 'ID' (e.g., 'ILMN_10000'), Gene Symbols: 'SYMBOL' (e.g., 'THBS3')
+
+# 1-2) Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SYMBOL')
+
+# 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
+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. Reload clinical data (created in Step 2) and reconstruct feature index
+selected_clinical_data = pd.read_csv(out_clinical_data_file)
+selected_clinical_data.index = [trait, 'Age', 'Gender']
+
+# 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. Check 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: Platform Illumina HumanRef-8 v2; multiple CNS diseases present; "
+    "trait encoded as ALS=1, others=0; gene symbols normalized via NCBI synonyms."
+)
+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/Amyotrophic_Lateral_Sclerosis/code/GSE52937.py

@@ -0,0 +1,112 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE52937"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE52937"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE52937.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE52937.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE52937.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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) Determine gene expression availability based on background info
+is_gene_available = True  # Expression profiling on A549 cell line with siRNA/infection perturbations
+
+# 2) Variable availability and conversion functions
+# From the sample characteristics, there is no ALS trait labeling, nor age or gender for human subjects.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(value):
+    if value is None:
+        return None
+    if isinstance(value, str) and ':' in value:
+        return value.split(':', 1)[1].strip()
+    return value
+
+def convert_trait(x):
+    # No ALS case/control or diagnosis labeling available in this cell-line dataset
+    return None
+
+def convert_age(x):
+    # Generic age parser; not used here since no age is available
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract first number that looks like age
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', str(v))
+    if not m:
+        return None
+    age = float(m.group(1))
+    if 0 < age < 120:
+        return age
+    return None
+
+def convert_gender(x):
+    # Generic gender parser; not used here since no gender is available
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if s in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    # Heuristics for encoded values
+    if s in {'0', '1'}:
+        return int(s)
+    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)
+# If trait_row were available, we would 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)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Amyotrophic_Lateral_Sclerosis/code/GSE61322.py

@@ -0,0 +1,145 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE61322"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE61322"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE61322.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE61322.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE61322.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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  # Expression microarray study; likely gene expression data
+
+# 2) Variable availability and converters
+
+# Given the sample characteristics, ALS-specific trait information is not available.
+# The dataset appears focused on AOA2; 'diagnosis' distinguishes AOA2 affected vs carrier.
+trait_row = None  # No ALS information available
+age_row = None    # No age field in the sample characteristics dictionary
+gender_row = None # No gender/sex field in the sample characteristics dictionary
+
+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):
+    # Binary: ALS presence (1) vs non-ALS (0)
+    v = _extract_value(x)
+    if v is None or v == "":
+        return None
+    vl = v.lower()
+    # Positive ALS indicators
+    als_pos = ["amyotrophic lateral sclerosis", "als4", "als"]
+    if any(k in vl for k in als_pos):
+        return 1
+    # Negative ALS indicators (dataset-specific: AOA2, carrier, control, healthy)
+    als_neg = [
+        "aoa2", "ataxia with oculomotor apraxia", "carrier", "control",
+        "healthy", "normal", "wild type", "wt", "non-als", "no als"
+    ]
+    if any(k in vl for k in als_neg):
+        return 0
+    # Ambiguous dataset-specific fields
+    if vl in {"affected", "presumed", "definite"}:
+        # In this dataset, 'affected' relates to AOA2; treat as non-ALS
+        return 0
+    # Unknown/NA patterns
+    if vl in {"na", "n/a", "not available", "unknown", "none"}:
+        return None
+    return None
+
+def convert_age(x):
+    # Continuous: years
+    v = _extract_value(x)
+    if v is None or v == "":
+        return None
+    vl = v.lower()
+    # Handle common NA tokens
+    if vl in {"na", "n/a", "not available", "unknown"}:
+        return None
+    # Extract first number
+    nums = re.findall(r"\d+\.?\d*", vl)
+    if not nums:
+        return None
+    try:
+        val = float(nums[0])
+    except ValueError:
+        return None
+    # Unit handling
+    if "month" in vl:
+        return round(val / 12.0, 3)
+    return val
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    v = _extract_value(x)
+    if v is None or v == "":
+        return None
+    vl = v.lower()
+    if vl in {"male", "m", "man"}:
+        return 1
+    if vl 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 since trait_row is None)
+# If trait_row becomes available in future, uncomment and use:
+# if trait_row is not None:
+#     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 = preview_df(selected, n=5)
+#     os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+#     selected.to_csv(out_clinical_data_file, index=True)

output/preprocess/Amyotrophic_Lateral_Sclerosis/code/GSE68607.py

@@ -0,0 +1,209 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE68607"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE68607"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE68607.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE68607.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE68607.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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 availability (Affymetrix Human Exon 1.0 ST -> mRNA expression)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# From the sample characteristics, trait (ALS vs Control) is in key 1: 'patient group: ...'
+trait_row = 1
+
+# Age and gender are not available in the provided characteristics
+age_row = None
+gender_row = None
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    vl = v.lower()
+    # Any ALS subtype counts as case; controls as 0
+    if "control" in vl:
+        return 0
+    if "als" in vl:
+        return 1
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    vl = v.lower()
+    # Extract first integer/float found
+    m = re.search(r"(\d+(\.\d+)?)", vl)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    vl = v.strip().lower()
+    # Map female->0, male->1
+    if vl in {"female", "f", "woman", "women"}:
+        return 0
+    if vl in {"male", "m", "man", "men"}:
+        return 1
+    # Heuristics for embedded strings
+    if "female" in vl:
+        return 0
+    if "male" in vl:
+        return 1
+    return None
+
+# 3) Save metadata via 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 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 and gender_row are None by default and thus omitted
+    )
+    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, 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
+# Map ENST transcript IDs to gene symbols using annotation
+# Identifier column in annotation matches gene_data index: 'ID' (e.g., ENST0000...)
+# Gene symbol column in annotation: 'ORF' (e.g., DDX11L10, WASH5P)
+prob_col = 'ID'
+gene_col = 'ORF'
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# Convert probe/transcript-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 json
+
+# 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 the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Derive availability flags just before final validation (ensure native Python bools)
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+
+# 3. Handle missing values
+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)
+is_trait_biased = bool(is_trait_biased)
+
+# Defensive: ensure the cohort info JSON is a valid dict if it exists
+if os.path.exists(json_path):
+    try:
+        with open(json_path, "r") as f:
+            _ = json.load(f)
+    except Exception:
+        with open(json_path, "w") as f:
+            json.dump({}, f)
+
+# 5. Final validation and metadata saving
+note = (
+    "INFO: ENST transcript IDs mapped to gene symbols via ORF; gene symbols normalized by NCBI synonyms; "
+    f"Age/Gender unavailable in this series; post-QC samples: {len(unbiased_linked_data)}"
+)
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    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/Amyotrophic_Lateral_Sclerosis/code/GSE68608.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE68608"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE68608"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE68608.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE68608.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE68608.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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  # Splicing dysregulation study in motor neurons implies gene expression data (not miRNA/methylation only)
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics dictionary in the prompt:
+# 0 -> subject id (not used for analysis)
+# 1 -> patient group: ALS due to mutated C9ORF72 / Neurologically healthy, non-disease control (trait)
+# 2 -> tissue: Laser captured motor neurons (constant, not useful)
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _after_colon(value):
+    if value is None:
+        return None
+    parts = str(value).split(':', 1)
+    return parts[1].strip() if len(parts) > 1 else str(value).strip()
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Cases: ALS patients with C9ORF72 mutation
+    if ('als' in vl) or ('patient' in vl) or ('mutated' in vl) or ('c9orf72' in vl):
+        return 1
+    # Controls: healthy / non-disease
+    if ('control' in vl) or ('healthy' in vl) or ('non-disease' in vl) or ('neurologically healthy' in vl):
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Extract first numeric value
+    m = re.search(r'[-+]?\d*\.?\d+', vl)
+    if not m:
+        return None
+    num = float(m.group())
+    # Convert months to years if clearly specified
+    if 'month' in vl and 'year' not in vl:
+        return round(num / 12.0, 3)
+    return num
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if vl in {'female', 'f', 'woman', 'girl'}:
+        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 data 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 if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Clinical features 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
+# Affymetrix probe set IDs (e.g., '1007_s_at') are not human gene symbols and require mapping.
+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 appropriate columns for probe IDs and gene symbols based on the preview
+probe_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get gene mapping dataframe
+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
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: 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)
+
+# Compute availability flags based on actual data before processing
+is_gene_available_fin = (normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+is_trait_available_fin = trait in linked_data.columns
+
+# 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; 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: Trait available; no age/gender provided by dataset. Affymetrix probe IDs mapped to gene symbols; motor neuron LCM samples."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_fin,
+    is_trait_available=is_trait_available_fin,
+    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/Amyotrophic_Lateral_Sclerosis/code/GSE95810.py

@@ -0,0 +1,143 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+cohort = "GSE95810"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
+in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE95810"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/GSE95810.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE95810.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE95810.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/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  # Series title indicates gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on provided Sample Characteristics, only amyloid beta 42 levels are present.
+# No ALS trait, age, or gender information is available.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _extract_value(cell):
+    if cell is None:
+        return None
+    if not isinstance(cell, str):
+        return cell
+    parts = cell.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else cell.strip()
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    # Positive for ALS
+    if "amyotrophic lateral sclerosis" in s or re.search(r"\bals\b", s):
+        return 1
+    # Negative for non-ALS (e.g., AD, control)
+    if any(k in s for k in ["control", "healthy", "normal", "non-als", "non als", "non-carrier", "non carrier",
+                            "alzheimer", "alzheimers", "pre-symptomatic", "pre-symptomatic alzheimer", "ad"]):
+        return 0
+    if s in {"1", "0"}:
+        return int(s)
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    # Handle months
+    m = re.search(r"([\d\.]+)\s*(month|mo|months)", s)
+    if m:
+        try:
+            return float(m.group(1)) / 12.0
+        except:
+            return None
+    # Handle years
+    m = re.search(r"([\d\.]+)", s)
+    if m:
+        try:
+            return float(m.group(1))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    if s in {"female", "f", "woman", "women"}:
+        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 becomes available in future steps, uncomment the following:
+# 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)
+
+# 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")

output/preprocess/Amyotrophic_Lateral_Sclerosis/code/TCGA.py

@@ -0,0 +1,65 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Amyotrophic_Lateral_Sclerosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Amyotrophic_Lateral_Sclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most appropriate TCGA cohort directory for ALS (likely none)
+synonyms = [
+    "amyotrophic lateral sclerosis",
+    "als",
+    "motor neuron disease",
+    "lou gehrig"
+]
+
+tcga_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+lc_subdirs = [d.lower() for d in tcga_subdirs]
+
+selected_idx = None
+for i, d in enumerate(lc_subdirs):
+    if any(s in d for s in synonyms):
+        selected_idx = i
+        break
+
+tcga_selected_dir = None if selected_idx is None else tcga_subdirs[selected_idx]
+
+clinical_df = None
+genetic_df = None
+clinical_file_path = None
+genetic_file_path = None
+
+if tcga_selected_dir is None:
+    print("No suitable TCGA cohort found for trait 'Amyotrophic Lateral Sclerosis'. Skipping TCGA for this trait.")
+    # Record as unavailable and complete
+    _ = 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 data file paths
+    cohort_dir = os.path.join(tcga_root_dir, tcga_selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+    # Step 4: Print column names of the clinical data
+    print(list(clinical_df.columns))

output/preprocess/Amyotrophic_Lateral_Sclerosis/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE95810": {
-    "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
-  },
-  "GSE68608": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": true,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE68607": {
-    "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": 69
-  },
-  "GSE61322": {
-    "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": 33
-  },
-  "GSE52937": {
-    "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": 54
-  },
-  "GSE26927": {
-    "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": 118
-  },
-  "GSE212134": {
-    "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
-  },
-  "GSE212131": {
-    "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
-  },
-  "GSE139384": {
-    "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
-  },
-  "GSE118336": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": true,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "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
-  }
-}
+{"GSE95810": {"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}, "GSE68608": {"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": 11, "note": "INFO: Trait available; no age/gender provided by dataset. Affymetrix probe IDs mapped to gene symbols; motor neuron LCM samples."}, "GSE68607": {"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": 69, "note": "INFO: ENST transcript IDs mapped to gene symbols via ORF; gene symbols normalized by NCBI synonyms; Age/Gender unavailable in this series; post-QC samples: 69"}, "GSE61322": {"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}, "GSE52937": {"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}, "GSE26927": {"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": 118, "note": "INFO: Platform Illumina HumanRef-8 v2; multiple CNS diseases present; trait encoded as ALS=1, others=0; gene symbols normalized via NCBI synonyms."}, "GSE212134": {"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}, "GSE212131": {"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 in this series; only gender present. Skipping linkage and marking cohort as not usable for association analysis."}, "GSE139384": {"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": 33, "note": "INFO: Illumina HumanHT-12 v4 platform; probe->gene mapping applied using SOFT annotation; multi-mapped probes split equally and summed per gene; gene symbols normalized via NCBI synonyms; trait mapped as ALS=1, non-ALS (PDC/AD/Control)=0."}, "GSE118336": {"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": 60, "note": "INFO: Trait derived from genotype (FUS H517D) in iPSC-derived MNs; HTA2.0 array."}, "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/Angelman_Syndrome/code/GSE43900.py

@@ -0,0 +1,79 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Angelman_Syndrome"
+cohort = "GSE43900"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Angelman_Syndrome"
+in_cohort_dir = "../DATA/GEO/Angelman_Syndrome/GSE43900"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Angelman_Syndrome/GSE43900.csv"
+out_gene_data_file = "./output/z1/preprocess/Angelman_Syndrome/gene_data/GSE43900.csv"
+out_clinical_data_file = "./output/z1/preprocess/Angelman_Syndrome/clinical_data/GSE43900.csv"
+json_path = "./output/z1/preprocess/Angelman_Syndrome/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  # Likely gene expression data (not miRNA/methylation)
+trait_row = None          # No human trait (Angelman Syndrome) info available
+age_row = None            # No age information
+gender_row = None         # No gender information
+
+# Conversion functions (defined for interface compatibility; not used since rows are None)
+def _extract_value(x):
+    if x is None:
+        return None
+    try:
+        # Typical format: "key: value"
+        parts = str(x).split(":", 1)
+        return parts[1].strip() if len(parts) > 1 else str(x).strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    # No trait information available in this dataset; return None
+    return None
+
+def convert_age(x):
+    # No age information; return None
+    return None
+
+def convert_gender(x):
+    # No gender information; return None
+    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 is skipped because trait_row is None
+# (If trait_row were available, we would call geo_select_clinical_features and save the output.)

output/preprocess/Angelman_Syndrome/code/TCGA.py

@@ -0,0 +1,51 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Angelman_Syndrome"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Angelman_Syndrome/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Angelman_Syndrome/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Angelman_Syndrome/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Angelman_Syndrome/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Identify the most relevant TCGA cohort directory for Angelman Syndrome (none expected)
+available_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+search_terms = {"angelman", "ube3a"}
+matching_dirs = [d for d in available_dirs if any(term in d.lower() for term in search_terms)]
+
+selected_dir = None
+if matching_dirs:
+    # If multiple, choose the most specific (heuristic: longest name)
+    selected_dir = sorted(matching_dirs, key=len, reverse=True)[0]
+
+if selected_dir is None:
+    # No suitable TCGA cancer cohort matches Angelman Syndrome; record and stop
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+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)
+
+    # Step 3: Load both files as DataFrames
+    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_df.columns.tolist())

output/preprocess/Angelman_Syndrome/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE43900": {
-    "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": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  }
-}
+{"GSE43900": {"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}, "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/Aniridia/clinical_data/GSE137997.csv

@@ -1,4 +1,4 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23
-1.0,0.0,,,,,,,,,,,,,,,,,,,,,,
-20.0,28.0,38.0,57.0,26.0,18.0,36.0,42.0,55.0,54.0,34.0,51.0,46.0,52.0,53.0,40.0,39.0,59.0,32.0,37.0,29.0,19.0,25.0,22.0
-0.0,1.0,0.0,,,,,,,,,,,,,,,,,,,,,
+,GSM4096349,GSM4096350,GSM4096351,GSM4096352,GSM4096353,GSM4096354,GSM4096355,GSM4096356,GSM4096357,GSM4096358,GSM4096359,GSM4096360,GSM4096361,GSM4096362,GSM4096363,GSM4096364,GSM4096365,GSM4096366,GSM4096367,GSM4096368,GSM4096369,GSM4096370,GSM4096371,GSM4096372,GSM4096373,GSM4096374,GSM4096375,GSM4096376,GSM4096377,GSM4096378,GSM4096379,GSM4096380,GSM4096381,GSM4096382,GSM4096383,GSM4096384,GSM4096385,GSM4096386,GSM4096387,GSM4096388
+Aniridia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,20.0,20.0,28.0,20.0,38.0,57.0,26.0,18.0,36.0,42.0,18.0,42.0,36.0,28.0,55.0,54.0,34.0,51.0,46.0,52.0,53.0,54.0,40.0,55.0,57.0,28.0,39.0,59.0,20.0,32.0,37.0,34.0,28.0,28.0,29.0,19.0,25.0,25.0,34.0,22.0
+Gender,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0

output/preprocess/Aniridia/code/GSE137996.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Aniridia"
+cohort = "GSE137996"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Aniridia"
+in_cohort_dir = "../DATA/GEO/Aniridia/GSE137996"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Aniridia/GSE137996.csv"
+out_gene_data_file = "./output/z1/preprocess/Aniridia/gene_data/GSE137996.csv"
+out_clinical_data_file = "./output/z1/preprocess/Aniridia/clinical_data/GSE137996.csv"
+json_path = "./output/z1/preprocess/Aniridia/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  # mRNA profiling present (not purely miRNA/methylation)
+
+# 2. Variable Availability and Converters
+# Identified rows from the Sample Characteristics Dictionary
+trait_row = 2   # 'disease: AAK' vs 'disease: healthy control'
+age_row = 0     # 'age: ...'
+gender_row = 1  # 'gender: F', 'gender: W', 'gender: M'
+
+def _extract_value(x):
+    if x is None:
+        return ""
+    s = str(x)
+    return s.split(":", 1)[1].strip() if ":" in s else s.strip()
+
+def convert_trait(x):
+    val = _extract_value(x).lower()
+    case_set = {
+        'aak',
+        'aniridia',
+        'congenital aniridia',
+        'aniridia-associated keratopathy',
+        'aniridia associated keratopathy'
+    }
+    control_set = {'healthy control', 'healthy', 'control', 'normal'}
+    if val in case_set:
+        return 1
+    if val in control_set:
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    m = re.search(r'[-+]?\d+\.?\d*', val)
+    if m:
+        try:
+            v = float(m.group())
+            return v
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _extract_value(x).lower()
+    # Heuristics: M/male/man -> 1; F/female/woman/W -> 0
+    if val in {'m', 'male', 'man'} or val.startswith('m'):
+        return 1
+    if val in {'f', 'female', 'woman', 'w'} or val.startswith('f') or val.startswith('w'):
+        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 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)
+    print(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
+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 for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# 2. Create the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply the 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 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 the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Flags for availability before stringent missing handling (ensure native Python bool)
+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 list(linked_data.columns)) and linked_data[trait].notna().any())
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias evaluation and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Ensure columns are a Python list to avoid numpy.bool_ in membership checks inside the validator
+unbiased_linked_data.columns = list(unbiased_linked_data.columns)
+is_trait_biased = bool(is_trait_biased)
+
+# 5. Final validation and metadata saving
+note = (
+    f"INFO: Normalized gene symbols and linked with clinical data. "
+    f"Gene matrix shape (normalized): {normalized_gene_data.shape}. "
+    f"Linked data shape after QC: {unbiased_linked_data.shape}."
+)
+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,
+    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/Aniridia/code/GSE137997.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Aniridia"
+cohort = "GSE137997"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Aniridia"
+in_cohort_dir = "../DATA/GEO/Aniridia/GSE137997"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Aniridia/GSE137997.csv"
+out_gene_data_file = "./output/z1/preprocess/Aniridia/gene_data/GSE137997.csv"
+out_clinical_data_file = "./output/z1/preprocess/Aniridia/clinical_data/GSE137997.csv"
+json_path = "./output/z1/preprocess/Aniridia/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  # mRNA expression is indicated in the series title; suitable for gene expression analysis.
+
+# 2) Variable availability and conversion functions
+
+# Rows identified from the provided sample characteristics dictionary:
+# 0: age, 1: gender, 2: disease (AAK vs healthy control)
+trait_row = 2
+age_row = 0
+gender_row = 1
+
+# Conversion helpers
+import os
+import re
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1].strip() if len(parts) > 1 else x.strip()
+        return val if val != "" else None
+    return x
+
+# trait: binary (0=control, 1=Aniridia/AAK)
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    # Exact matches first
+    if vl in {"healthy control", "control", "healthy", "normal"}:
+        return 0
+    if vl in {"aak", "aniridia", "aniridia-associated keratopathy", "aniridia associated keratopathy"}:
+        return 1
+    # Substring fallback
+    if "healthy" in vl and "control" in vl:
+        return 0
+    if "aak" in vl or "aniridia" in vl:
+        return 1
+    if vl in {"na", "n/a", "unknown", "null"}:
+        return None
+    return None
+
+# age: continuous (years)
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group())
+        except:
+            return None
+    return None
+
+# gender: binary (0=female, 1=male)
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"f", "female", "woman", "w"}:
+        return 0
+    if vl in {"m", "male", "man"}:
+        return 1
+    if vl in {"na", "n/a", "unknown", "null"}:
+        return None
+    if len(vl) == 1:
+        if vl == "f":
+            return 0
+        if vl == "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 (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
+    )
+    clinical_features_preview = preview_df(selected_clinical_df)
+    print(clinical_features_preview)
+    # 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
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")

output/preprocess/Aniridia/code/GSE204791.py

@@ -0,0 +1,198 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Aniridia"
+cohort = "GSE204791"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Aniridia"
+in_cohort_dir = "../DATA/GEO/Aniridia/GSE204791"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Aniridia/GSE204791.csv"
+out_gene_data_file = "./output/z1/preprocess/Aniridia/gene_data/GSE204791.csv"
+out_clinical_data_file = "./output/z1/preprocess/Aniridia/clinical_data/GSE204791.csv"
+json_path = "./output/z1/preprocess/Aniridia/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  # mRNA microarray data are described in the background
+
+# 2) Variable availability and conversion
+
+# Based on the sample characteristics:
+# 0: 'age: ...'                -> available
+# 1: 'gender: M/F'             -> available
+# 2: 'disease: KC/healthy...'  -> this does not capture Aniridia; trait is therefore unavailable here
+trait_row = None
+age_row = 0
+gender_row = 1
+
+def _after_colon(s: str) -> str:
+    if s is None:
+        return ""
+    parts = str(s).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(s).strip()
+
+def convert_trait(x):
+    """
+    Map to Aniridia presence:
+    - 1 if mentions aniridia/AAK/PAX6 mutation explicitly
+    - 0 if indicates healthy control
+    - None otherwise (e.g., keratoconus, unspecified)
+    """
+    val = _after_colon(x).lower()
+    if not val:
+        return None
+    if any(k in val for k in ["aniridia", "aak", "pax6"]):
+        return 1
+    if "healthy" in val or "control" in val:
+        return 0
+    if "kc" in val or "keratoconus" in val:
+        return None
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if not val:
+        return None
+    # Extract first integer/float in the string
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group())
+        # Age must be within a plausible human range
+        if 0 <= age_val <= 120:
+            return age_val
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    val = _after_colon(x).strip().lower()
+    if not val:
+        return None
+    if val in ["f", "female", "woman", "women"]:
+        return 0
+    if val in ["m", "male", "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: skip because trait_row is None
+# (If trait_row becomes available in future steps, use geo_select_clinical_features and preview_df, then save CSV.)
+
+# 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
+# IDs like 'A_19_P...' (Agilent probe IDs) and '(+)E1A_r60_1' are 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
+# Decide which annotation column best matches the probe IDs in the expression data
+candidate_probe_cols = [col for col in ['ID', 'SPOT_ID'] if col in gene_annotation.columns]
+if not candidate_probe_cols:
+    raise ValueError("No suitable probe ID column found in annotation (expected 'ID' or 'SPOT_ID').")
+
+overlaps = {}
+for col in candidate_probe_cols:
+    ann_ids = gene_annotation[col].astype(str).str.strip()
+    overlaps[col] = ann_ids.isin(gene_data.index).sum()
+
+probe_col = max(overlaps, key=overlaps.get)
+
+# Decide the gene symbol column
+symbol_col = 'GENE_SYMBOL' if 'GENE_SYMBOL' in gene_annotation.columns else None
+if symbol_col is None:
+    raise ValueError("No suitable gene symbol column found in annotation (expected 'GENE_SYMBOL').")
+
+# Build mapping and convert probe-level data to gene-level data
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save gene-only data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Proceed only if clinical data with trait is available (selected_clinical_data created in Step 2 when trait_row != None)
+if 'selected_clinical_data' in globals() and selected_clinical_data is not None:
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias checking and removal of biased demographic covariates
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        True,
+        cohort,
+        json_path,
+        True,   # gene data available
+        True,   # trait data available
+        is_trait_biased,
+        unbiased_linked_data
+    )
+
+    # Save linked data if usable
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait data unavailable; record metadata (initial filter) 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/Aniridia/code/TCGA.py

@@ -0,0 +1,73 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Aniridia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Aniridia/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Aniridia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Aniridia/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Aniridia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# 1) Select the most relevant TCGA cohort directory for the trait "Aniridia"
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Heuristic keyword scoring to approximate phenotypic overlap with "Aniridia"
+keywords_weights = [
+    ("aniridia", 10),
+    ("iris", 6),
+    ("ocular", 5),
+    ("eye", 4),
+    ("uveal", 4),
+    ("uvea", 4),
+    ("retina", 3),
+    ("optic", 2),
+    ("ophthalm", 2)
+]
+
+def score_dir(name: str) -> int:
+    ln = name.lower()
+    return sum(w for k, w in keywords_weights if k in ln)
+
+scored = [(d, score_dir(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:
+    # No suitable cohort; record and exit step gracefully
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort directory found for the trait. Skipping.")
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # 2) Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3) Load both files as DataFrames
+    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')
+
+    # Keep references for downstream steps
+    SELECTED_TCGA_DIR = selected_dir
+    SELECTED_CLINICAL_PATH = clinical_file_path
+    SELECTED_GENETIC_PATH = genetic_file_path
+    TCGA_CLINICAL_DF = clinical_df
+    TCGA_GENETIC_DF = genetic_df
+
+    # 4) Print the column names of the clinical data
+    print(list(clinical_df.columns))

output/preprocess/Aniridia/cohort_info.json

@@ -1,42 +1 @@
-{
-  "GSE204791": {
-    "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": 31
-  },
-  "GSE137997": {
-    "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": 64
-  },
-  "GSE137996": {
-    "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
-  },
-  "TCGA": {
-    "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": 80
-  }
-}
+{"GSE204791": {"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}, "GSE137996": {"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, "note": "INFO: Normalized gene symbols and linked with clinical data. Gene matrix shape (normalized): (20778, 40). Linked data shape after QC: (40, 20781)."}}