Add files using upload-large-folder tool

output/preprocess/COVID-19/clinical_data/GSE211378.csv

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output/preprocess/COVID-19/clinical_data/GSE212865.csv

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output/preprocess/COVID-19/clinical_data/GSE212866.csv

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output/preprocess/COVID-19/clinical_data/GSE227080.csv

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+COVID-19,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,24.0,46.0,50.0,37.0,19.0,40.0,46.0,63.0,54.0,46.0,48.0,34.0,22.0,59.0,39.0,27.0,61.0,38.0,44.0,41.0,49.0,19.0,38.0,43.0,62.0,30.0,59.0,40.0,61.0,47.0,59.0,37.0,53.0,30.0,29.0,48.0,32.0,55.0,51.0,48.0,31.0,60.0,24.0,47.0,20.0,42.0,41.0
+Gender,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0

output/preprocess/COVID-19/code/GSE185658.py

@@ -0,0 +1,210 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE185658"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE185658"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE185658.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE185658.csv"
+json_path = "./output/z2/preprocess/COVID-19/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 (Affymetrix microarray -> gene expression present)
+is_gene_available = True
+
+# 2) Variable availability (from provided Sample Characteristics Dictionary)
+# No explicit COVID-19 status, age, or gender fields are available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Data type conversion helpers
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    """
+    Convert to binary: COVID-19 positive (1) vs control/negative (0).
+    Heuristics for general GEO annotations if present.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    lv = v.lower()
+
+    # Positive indicators
+    pos_terms = [
+        "covid-19", "covid19", "covid", "sars-cov-2", "sars cov 2",
+        "sarscov2", "2019-ncov", "patient", "case", "positive", "pos"
+    ]
+    # Negative/control indicators
+    neg_terms = [
+        "healthy", "control", "negative", "neg", "healthy control", "non-covid", "non covid", "no covid"
+    ]
+    if any(t in lv for t in pos_terms):
+        return 1
+    if any(t in lv for t in neg_terms):
+        return 0
+
+    # Yes/No patterns
+    if re.fullmatch(r"yes|y|true|1", lv):
+        return 1
+    if re.fullmatch(r"no|n|false|0", lv):
+        return 0
+
+    return None
+
+def convert_age(x):
+    """
+    Convert to continuous age in years (float).
+    Extract first plausible number; return None if out of range or missing.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        # Basic human plausible range
+        if 0 <= age <= 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    """
+    Convert to binary gender: female -> 0, male -> 1.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    lv = v.lower().strip()
+    if lv in ["female", "f", "woman", "women", "girl"]:
+        return 0
+    if lv in ["male", "m", "man", "men", "boy"]:
+        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 (skip because trait_row is None)
+# If trait_row were available, we would extract and save clinical features like this:
+if trait_row is not None:
+    selected_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_df)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping
+id_col = 'ID' if 'ID' in gene_annotation.columns else ('ID_REF' if 'ID_REF' in gene_annotation.columns else None)
+if id_col is None:
+    raise ValueError("Probe ID column not found in gene annotation.")
+
+gene_symbol_candidates = [
+    'gene_assignment', 'Gene Symbol', 'Gene symbol', 'GENE_SYMBOL',
+    'Symbol', 'SYMBOL', 'gene_symbol', 'gene', 'GENE', 'gene_name',
+    'Gene Name', 'GENE_NAME'
+]
+gene_col = next((c for c in gene_symbol_candidates if c in gene_annotation.columns), None)
+if gene_col is None:
+    raise ValueError("Gene symbol column not found in gene annotation.")
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_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
+
+# 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)
+
+# 2-6) Since no clinical/trait data were identified earlier (trait_row is None), skip linking and final validation.
+# Record metadata appropriately to indicate trait is unavailable for association analysis.
+is_trait_available = False  # from Step 2 decision (trait_row = None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=is_trait_available
+)
+
+# Note: Do not attempt linking or saving linked data when trait is unavailable.

output/preprocess/COVID-19/code/GSE211378.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE211378"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE211378"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE211378.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE211378.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE211378.csv"
+json_path = "./output/z2/preprocess/COVID-19/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 (NanoString nCounter Host Response panel -> mRNA expression)
+is_gene_available = True
+
+# 2) Variable availability
+# Trait (COVID-19 status) can be inferred from 'nanostring_id' where some entries contain 'Healthy'
+trait_row = 12  # 'nanostring_id' row
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(v):
+    val = _after_colon(v)
+    if val is None:
+        return None
+    low = val.lower()
+    if low in {"", "na", "n/a", "nan", "none"}:
+        return None
+    # If explicitly noted as Healthy -> control (0); otherwise assume COVID-19 convalescent (1)
+    if "healthy" in low:
+        return 0
+    return 1
+
+def convert_age(v):
+    val = _after_colon(v)
+    if val is None:
+        return None
+    low = val.lower()
+    if low in {"", "na", "n/a", "nan", "none"}:
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", low)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(v):
+    val = _after_colon(v)
+    if val is None:
+        return None
+    low = val.strip().lower()
+    if low in {"", "na", "n/a", "nan", "none", "unknown"}:
+        return None
+    # Map female->0, male->1
+    if low in {"female", "f", "woman", "girl"} or "female" in low:
+        return 0
+    if low in {"male", "m", "man", "boy"} or "male" in low:
+        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 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 and save
+    preview = preview_df(selected_clinical_df)
+    print("Clinical feature preview:", preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Ensure gene_data is available, normalize gene symbols, and save
+try:
+    gene_data  # check if exists
+except NameError:
+    try:
+        gene_data = get_genetic_data(matrix_file)
+    except NameError:
+        # Fallback to re-locate files
+        _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+        gene_data = get_genetic_data(matrix_file)
+
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical features and link with genetic data
+# Reload from disk to avoid scope issues
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Final quality validation and save cohort info
+note = "INFO: Trait (COVID-19) inferred from 'nanostring_id' field ('Healthy' as controls); Age/Gender not available."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/COVID-19/code/GSE212865.py

@@ -0,0 +1,287 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE212865"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE212865"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE212865.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE212865.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE212865.csv"
+json_path = "./output/z2/preprocess/COVID-19/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  # Microarray-based whole blood transcriptomics
+
+# 2) Variable availability and converters
+trait_row = 0  # 'disease state' entries indicate COVID-19 vs Control
+age_row = None
+gender_row = None
+
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        return val.strip()
+    return x
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower().replace("-", "").replace("_", "").replace(" ", "")
+    if s in {"control", "healthy", "normal"}:
+        return 0
+    # map any covid-related disease states to 1
+    if "covid" in s or "sarscov2" in s:
+        return 1
+    if s in {"na", "nan", ""}:
+        return None
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).lower()
+    if s in {"na", "nan", ""}:
+        return None
+    # extract a number (years)
+    m = re.search(r"([-+]?\d*\.?\d+)", s)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {"female", "f", "woman", "women"}:
+        return 0
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    if s in {"na", "nan", ""}:
+        return None
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print(clinical_preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import pandas as pd
+import re
+
+# Preserve the original probe-level expression data
+expression_df = gene_data
+expr_ids = set(expression_df.index.astype(str))
+
+# 1) Identify the annotation column that matches the expression IDs (numeric strings like '23064070')
+def build_candidate_series(s: pd.Series) -> dict:
+    """Return candidate ID series from an annotation column using different strategies."""
+    out = {}
+
+    # A. Raw string
+    cand_str = s.astype(str).str.strip()
+    out['raw'] = cand_str
+
+    # B. Numeric coercion
+    s_num = pd.to_numeric(s, errors='coerce')
+    cand_num = s_num.round().astype('Int64').astype(str)
+    cand_num = cand_num.where(~s_num.isna(), other=pd.NA)
+    out['numeric'] = cand_num
+
+    # C. Extract 8-digit numeric tokens (common for Affy transcript cluster IDs)
+    cand_8 = s.astype(str).str.findall(r'\d{8}').apply(lambda lst: lst[0] if isinstance(lst, list) and len(lst) > 0 else pd.NA)
+    out['digits8'] = cand_8
+
+    # D. Extract 7-10 digit tokens; pick the first if exists
+    cand_7_10 = s.astype(str).str.findall(r'\d{7,10}').apply(lambda lst: lst[0] if isinstance(lst, list) and len(lst) > 0 else pd.NA)
+    out['digits7_10'] = cand_7_10
+
+    return out
+
+best_col = None
+best_method = None
+best_match_count = -1
+best_series = None
+
+for col in gene_annotation.columns:
+    candidates = build_candidate_series(gene_annotation[col])
+    for method_name, ser in candidates.items():
+        vals = ser.dropna().astype(str).str.strip()
+        match_count = len(set(vals.unique()) & expr_ids)
+        if match_count > best_match_count:
+            best_match_count = match_count
+            best_col = col
+            best_method = method_name
+            best_series = ser
+
+print(f"[INFO] Best ID match: column='{best_col}', method='{best_method}', matches={best_match_count}")
+
+if best_match_count <= 0 or best_col is None or best_series is None:
+    # Diagnostic: show columns and small preview for troubleshooting
+    print("[ERROR] No overlap between expression IDs and any annotation column using multiple strategies.")
+    print(f"Annotation columns: {list(gene_annotation.columns)}")
+    print("Preview a few columns (top 2 rows):")
+    with pd.option_context('display.max_colwidth', 200):
+        print(gene_annotation.iloc[:2, :min(10, gene_annotation.shape[1])])
+    raise ValueError("Failed to find an annotation column that matches matrix IDs. Please inspect platform annotation.")
+
+# 2) Identify the gene symbol-containing column
+gene_col = None
+preferred_gene_cols = [
+    'SPOT_ID.1', 'gene_assignment', 'Gene Symbol', 'GeneSymbol', 'Symbol', 'gene_symbol',
+    'Gene symbol', 'GENE_SYMBOL', 'GENE_SYMBOLS', 'gene_symbols', 'GeneSymbols',
+    'Annotation', 'description', 'gene_description'
+]
+for c in preferred_gene_cols:
+    if c in gene_annotation.columns:
+        gene_col = c
+        break
+if gene_col is None:
+    # Fallback: choose column with longest average text length (likely rich annotation containing symbols)
+    text_lengths = {c: gene_annotation[c].astype(str).map(len).mean() for c in gene_annotation.columns}
+    gene_col = max(text_lengths, key=text_lengths.get)
+
+print(f"[INFO] Selected gene symbol column: '{gene_col}'")
+
+# 3) Build mapping using the best ID series and apply mapping
+ann_for_map = gene_annotation.copy()
+ann_for_map['__CAND_ID__'] = best_series.astype(str).str.strip()
+
+mapping_df = get_gene_mapping(ann_for_map, prob_col='__CAND_ID__', gene_col=gene_col)
+
+id_overlap = len(set(mapping_df['ID'].unique()) & expr_ids)
+print(f"[INFO] Overlap between mapping IDs and expression IDs: {id_overlap}")
+
+if id_overlap == 0 or len(mapping_df) == 0:
+    raise ValueError("Constructed mapping has zero overlap with expression IDs. Revisit ID/gene column selection.")
+
+# Apply mapping: distribute probe signal across genes and sum to gene-level
+gene_data = apply_gene_mapping(expression_df=expression_df, mapping_df=mapping_df)
+
+if gene_data.shape[0] == 0:
+    raise ValueError("Gene-level expression matrix is empty after applying mapping. Mapping likely incorrect.")
+
+print(f"[INFO] Gene-level data shape: {gene_data.shape}")
+
+# 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. Assess bias and remove biased demographic features (if any)
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available = trait in unbiased_linked_data.columns
+
+note = (
+    f"INFO: Gene rows pre-norm={gene_data.shape[0]}, post-norm={normalized_gene_data.shape[0]}; "
+    f"samples post-QC={unbiased_linked_data.shape[0]}; "
+    f"features post-QC={unbiased_linked_data.shape[1]}"
+)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/COVID-19/code/GSE212866.py

@@ -0,0 +1,327 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE212866"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE212866"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE212866.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE212866.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE212866.csv"
+json_path = "./output/z2/preprocess/COVID-19/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Microarray gene expression profiling is indicated in the background info.
+
+# 2. Variable Availability and Conversion Functions
+
+# Based on the sample characteristics dictionary:
+# 0: disease state (Covid19, Control, Covid19_SDRA)
+# 1: time (NA, D0, D7)
+# 2: tissue (peripheral blood)
+trait_row = 0
+age_row = None
+gender_row = None
+
+def _after_colon(v):
+    if v is None:
+        return None
+    s = str(v)
+    parts = s.split(":", 1)
+    s = parts[1] if len(parts) == 2 else parts[0]
+    return s.strip()
+
+def convert_trait(v):
+    s = _after_colon(v)
+    if not s:
+        return None
+    sl = s.lower().strip()
+    norm = re.sub(r'[\s_\-]+', '', sl)
+    # Map any COVID-related disease states to case=1; controls/healthy to 0
+    if "covid" in norm:
+        return 1
+    if "control" in norm or "healthy" in norm:
+        return 0
+    return None
+
+def convert_age(v):
+    s = _after_colon(v)
+    if not s:
+        return None
+    # Attempt to extract a number if present; not used since age_row is None
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    return float(m.group(1)) if m else None
+
+def convert_gender(v):
+    s = _after_colon(v)
+    if not s:
+        return None
+    sl = s.lower().strip()
+    if sl in {"female", "f", "woman", "women"}:
+        return 0
+    if sl in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if 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 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(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Robust probe-to-gene mapping across available SOFT files in the cohort directory
+
+import os
+import re
+import pandas as pd
+
+# Helper: try to select an ID column and a gene-symbol-containing column for a given annotation df
+def select_id_and_gene_cols(annotation_df: pd.DataFrame, expr_index: pd.Index) -> tuple:
+    expr_index = expr_index.astype(str).str.strip()
+    expr_set = set(expr_index)
+
+    candidate_cols = list(annotation_df.columns)
+
+    # Find best ID column by exact intersection count with expression IDs
+    best_id_col, best_match_count = None, -1
+    for col in candidate_cols:
+        try:
+            col_values = annotation_df[col].astype(str).str.strip()
+            match_count = col_values.isin(expr_index).sum()
+            if match_count > best_match_count:
+                best_match_count = match_count
+                best_id_col = col
+        except Exception:
+            continue
+
+    # If nothing matches, try some common fallbacks to suggest an ID column (may still be useless here)
+    if best_match_count <= 0 or best_id_col is None:
+        for fallback in ['ID', 'ID_REF', 'probeset_id', 'PROBESET_ID', 'ProbeID', 'PROBE_ID', 'reporter_id', 'Reporter ID']:
+            if fallback in annotation_df.columns:
+                try:
+                    col_values = annotation_df[fallback].astype(str).str.strip()
+                    match_count = col_values.isin(expr_index).sum()
+                except Exception:
+                    match_count = -1
+                if match_count > best_match_count:
+                    best_match_count = match_count
+                    best_id_col = fallback
+
+    # Find the best gene-symbol column
+    def has_symbols_fraction(series, sample_n=2000):
+        s = series.astype(str)
+        if len(s) > sample_n:
+            s = s.sample(sample_n, random_state=0)
+        counts = s.apply(lambda x: len(extract_human_gene_symbols(x)))
+        return float((counts > 0).mean())
+
+    best_gene_col, best_gene_frac = None, -1.0
+    # Prefer common gene symbol columns first
+    preferred_symbol_cols = [
+        'Gene Symbol', 'GENE_SYMBOL', 'GeneSymbol', 'Symbol', 'SYMBOL', 'gene_symbol',
+        'Gene.symbol', 'gene_assignment', 'SPOT_ID.1', 'SPOT_ID', 'Description', 'GB_ACC', 'Target Description'
+    ]
+    for col in preferred_symbol_cols + candidate_cols:
+        if col not in annotation_df.columns:
+            continue
+        if best_id_col is not None and col == best_id_col:
+            continue
+        try:
+            frac = has_symbols_fraction(annotation_df[col])
+        except Exception:
+            continue
+        prefer = 1 if re.search(r'(symbol|gene|SPOT_ID(\.1)?)', str(col), re.I) else 0
+        if (frac > best_gene_frac) or (frac == best_gene_frac and prefer == 1):
+            best_gene_frac = frac
+            best_gene_col = col
+
+    return best_id_col, int(best_match_count) if best_match_count is not None else 0, best_gene_col, float(best_gene_frac if best_gene_frac is not None else 0.0)
+
+
+# 1) Gather all SOFT files in the cohort directory (Series and Platform), including the one already loaded
+soft_files = [f for f in os.listdir(in_cohort_dir) if 'soft' in f.lower()]
+soft_paths = [os.path.join(in_cohort_dir, f) for f in soft_files]
+
+# Include the already loaded annotation as candidate 0 without re-reading from disk
+candidates = []
+try:
+    id_col, match_count, gene_col, gene_frac = select_id_and_gene_cols(gene_annotation, gene_data.index)
+    candidates.append({
+        'source': soft_file,
+        'annotation': gene_annotation,
+        'id_col': id_col,
+        'match_count': match_count,
+        'gene_col': gene_col,
+        'gene_frac': gene_frac
+    })
+except Exception:
+    pass
+
+# Load other SOFT files and evaluate
+for p in soft_paths:
+    if p == soft_file:
+        continue
+    try:
+        ann = get_gene_annotation(p)
+        id_col, match_count, gene_col, gene_frac = select_id_and_gene_cols(ann, gene_data.index)
+        candidates.append({
+            'source': p,
+            'annotation': ann,
+            'id_col': id_col,
+            'match_count': match_count,
+            'gene_col': gene_col,
+            'gene_frac': gene_frac
+        })
+    except Exception:
+        continue
+
+# 2) Choose the best candidate with non-zero ID matches
+best = None
+for c in sorted(candidates, key=lambda x: (x['match_count'], x['gene_frac']), reverse=True):
+    if c['id_col'] is not None and c['match_count'] > 0 and c['gene_col'] is not None:
+        best = c
+        break
+
+if best is None:
+    print("WARNING: No SOFT annotation with non-zero ID intersection was found. "
+          "The provided SOFT may not correspond to the matrix platform. "
+          "Gene-level data cannot be generated for this cohort at this time.")
+    # Produce an empty gene_data to signal failure gracefully
+    gene_data = pd.DataFrame()
+else:
+    print(f"Selected annotation file: {os.path.basename(best['source'])}")
+    print(f"ID column: {best['id_col']} with {best['match_count']} ID matches")
+    print(f"Gene column: {best['gene_col']} with symbol-detection fraction ~ {best['gene_frac']:.2f}")
+
+    # 3) Build mapping dataframe using selected columns
+    mapping_df = get_gene_mapping(best['annotation'], prob_col=best['id_col'], gene_col=best['gene_col'])
+
+    # Keep only rows whose IDs are actually present in expression data
+    mapping_df = mapping_df[mapping_df['ID'].astype(str).isin(gene_data.index.astype(str))]
+
+    if mapping_df.empty:
+        print("WARNING: Mapping dataframe is empty after filtering to expression IDs. Gene-level data will be empty.")
+        gene_data = pd.DataFrame()
+    else:
+        # 4) Apply mapping to convert probe-level to gene-level data
+        probe_data = gene_data  # original probe-level data
+        gene_data = apply_gene_mapping(probe_data, mapping_df)
+        print(f"Gene-level data shape: {gene_data.shape}")
+        if not gene_data.empty:
+            print(f"First 5 mapped genes: {list(gene_data.index[:5])}")
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+pre_gene_count = 0 if 'gene_data' not in globals() or gene_data is None or gene_data.empty else gene_data.shape[0]
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+post_gene_count = 0 if normalized_gene_data is None or normalized_gene_data.empty else normalized_gene_data.shape[0]
+
+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
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Build a note for metadata
+if post_gene_count == 0:
+    note = f"WARNING: Normalization removed all {pre_gene_count} mapped entries; no recognized gene symbols remained."
+else:
+    dropped = max(pre_gene_count - post_gene_count, 0)
+    drop_pct = (dropped / pre_gene_count * 100.0) if pre_gene_count > 0 else 0.0
+    note = f"INFO: Normalized gene symbols: {pre_gene_count} -> {post_gene_count} genes (dropped {dropped}, {drop_pct:.1f}%)."
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=(post_gene_count > 0),
+    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/COVID-19/code/GSE213313.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE213313"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE213313"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE213313.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE213313.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE213313.csv"
+json_path = "./output/z2/preprocess/COVID-19/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  # Microarray whole-blood RNA implies gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability from Sample Characteristics
+trait_row = 1        # 'disease state: COVID-19' vs 'Healthy'
+# Age and gender were not provided in the sample characteristics (controls were matched by age/gender per design,
+# but individual values are not present), so treat them as unavailable for analysis.
+age_row = None
+gender_row = None
+
+is_trait_available = trait_row is not None
+
+# 2.2 Converters
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"covid-19", "covid19", "sars-cov-2", "case", "patient"}:
+        return 1
+    if vl in {"healthy", "control", "healthy control", "ctrl"}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"na", "n/a", "nan", "none", "unknown", ""}:
+        return None
+    m = re.search(r"[-+]?\d+(\.\d+)?", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(0))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"female", "f", "woman", "girl"}:
+        return 0
+    if vl in {"male", "m", "man", "boy"}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if clinical data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_selected_preview = preview_df(selected_clinical_df)
+    print(clinical_selected_preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping: probe IDs are in 'ID', gene symbols in '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
+probe_data = gene_data  # preserve 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
+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
+is_gene_available_final = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (trait in selected_clinical_df.index) and (selected_clinical_df.loc[trait].notna().sum() > 0)
+
+note = "INFO: Only disease state available per sample; Age and Gender not provided in sample characteristics."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=str(cohort),
+    info_path=json_path,
+    is_gene_available=bool(is_gene_available_final),
+    is_trait_available=bool(is_trait_available_final),
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=str(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/COVID-19/code/GSE216705.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE216705"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE216705"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE216705.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE216705.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE216705.csv"
+json_path = "./output/z2/preprocess/COVID-19/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability (microarray metadata suggests gene expression data is present)
+is_gene_available = True
+
+# 2) Variable availability based on the provided Sample Characteristics Dictionary
+# Given keys: {0: ['strain: C57BL/6'], 1: ['metadata info: metaData_microarrays.txt']}
+# No human trait/age/gender fields; 'strain' is mouse and constant, so unusable.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _extract_value(cell):
+    if cell is None:
+        return None
+    if isinstance(cell, str):
+        parts = cell.split(':', 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        val = val.strip().strip('"').strip()
+        return val if val not in {"", "NA", "N/A", "na", "n/a", "null", "None"} else None
+    return cell
+
+def convert_trait(cell):
+    """
+    Binary: 1 = COVID-19/SARS-CoV-2/infected/positive (when context suggests COVID testing),
+            0 = control/healthy/uninfected/negative (when context suggests COVID testing).
+    """
+    val = _extract_value(cell)
+    if val is None:
+        return None
+    s = val.lower()
+
+    # Strong positive indicators
+    if any(k in s for k in ["covid", "sars-cov-2", "sars cov 2", "sarscov2", "ncov"]):
+        # If explicitly non-COVID
+        if any(k in s for k in ["non-covid", "non covid", "not covid"]):
+            return 0
+        # If mentions infection/infected
+        if any(k in s for k in ["infect", "patient", "case"]):
+            return 1
+        # If mentions test result
+        if "pcr" in s and "negative" in s:
+            return 0
+        if "pcr" in s and "positive" in s:
+            return 1
+        # Otherwise, assume COVID context implies case
+        return 1
+
+    # Explicit control/healthy/mocks
+    if any(k in s for k in ["control", "healthy", "mock", "uninfected", "naive", "baseline"]):
+        return 0
+
+    # Generic positive/negative if test context indicated
+    if "pcr" in s:
+        if "positive" in s:
+            return 1
+        if "negative" in s:
+            return 0
+
+    # Unknown/irrelevant field
+    return None
+
+def convert_age(cell):
+    """Continuous age in years where possible."""
+    val = _extract_value(cell)
+    if val is None:
+        return None
+    s = val.lower()
+    # Find first number
+    m = re.search(r"[-+]?\d*\.?\d+", s)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+    except:
+        return None
+    # Heuristic: if clearly days/months, convert to years
+    if any(k in s for k in ["day", "d "]):
+        return round(age / 365.25, 3)
+    if "month" in s:
+        return round(age / 12.0, 3)
+    return age
+
+def convert_gender(cell):
+    """Binary gender: female->0, male->1."""
+    val = _extract_value(cell)
+    if val is None:
+        return None
+    s = val.strip().lower()
+    # Common mappings
+    female_tokens = {"female", "f", "woman", "girl", "fem", "femenine", "feminine"}
+    male_tokens = {"male", "m", "man", "boy", "masc", "masculine"}
+    if s in female_tokens:
+        return 0
+    if s in male_tokens:
+        return 1
+    # Handle phrases
+    if "female" in s:
+        return 0
+    if "male" in s:
+        return 1
+    if s in {"unknown", "na", "n/a", "not determined", "undetermined"}:
+        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 (skip because trait_row is None)
+# If trait_row becomes available in future steps, uncomment and use:
+# if trait_row is not None:
+#     selected_clinical = geo_select_clinical_features(
+#         clinical_df=clinical_data,
+#         trait=trait,
+#         trait_row=trait_row,
+#         convert_trait=convert_trait,
+#         age_row=age_row,
+#         convert_age=convert_age,
+#         gender_row=gender_row,
+#         convert_gender=convert_gender
+#     )
+#     preview = preview_df(selected_clinical, n=5)
+#     selected_clinical.to_csv(out_clinical_data_file, index=True)

output/preprocess/COVID-19/code/GSE227080.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE227080"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE227080"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE227080.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE227080.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE227080.csv"
+json_path = "./output/z2/preprocess/COVID-19/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # NanoString mRNA expression of 579 immunological genes
+
+# 2) Variable Availability and Data Type Conversion
+# From the Sample Characteristics Dictionary:
+# 0: gender, 1: age, 2: severity (NEG, MILD, MOD_SEV)
+trait_row = 2
+age_row = 1
+gender_row = 0
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        return x
+    parts = x.split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_up = str(v).strip().upper()
+    # Controls
+    if any(k in v_up for k in ["NEG", "CONTROL", "CTRL"]):
+        return 0
+    # Cases (COVID-19 positive with mild/moderate/severe)
+    case_tokens = {"POS", "POSITIVE", "MILD", "MOD", "MOD_SEV", "MOD/SEV", "SEV", "SEVERE"}
+    if v_up in case_tokens:
+        return 1
+    # Heuristic: if mentions SARS or COVID and not explicitly NEG, assume case
+    if ("SARS" in v_up or "COVID" in v_up) and "NEG" not in v_up:
+        return 1
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v = str(v).strip()
+    if v == "" or v.upper() in {"NA", "N/A", "UNKNOWN"}:
+        return None
+    # keep digits and possible decimal point
+    m = re.search(r"(\d+(?:\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_up = str(v).strip().upper()
+    if v_up in {"F", "FEMALE", "WOMAN", "WOMEN"}:
+        return 0
+    if v_up 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
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = False")
+
+# Step 5: 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 dataframe is available (fallback to file if needed)
+if 'selected_clinical_df' not in globals():
+    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. 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 metadata
+note = "INFO: NanoString nCounter Immunology panel (~579 targeted genes); PBMC samples; trait encoded as 1=C19+, 0=NEG."
+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/COVID-19/code/GSE243348.py

@@ -0,0 +1,186 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE243348"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE243348"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE243348.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE243348.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE243348.csv"
+json_path = "./output/z2/preprocess/COVID-19/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Gene expression data availability
+is_gene_available = True  # nCounter profiling of 773 host response genes (mRNA) indicates gene expression data
+
+# 2) Variable availability and converters based on the provided Sample Characteristics Dictionary
+trait_row = 0      # 'disease status: COVID-19+' vs 'Healthy uninfected'
+age_row = 3        # 'age: <number>'
+gender_row = 2     # 'Sex: female' / 'Sex: male'
+
+def _after_colon(x):
+    if x is None:
+        return None
+    try:
+        part = str(x).split(":", 1)[1].strip()
+    except Exception:
+        return None
+    return part if part != "" else None
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = v.strip().lower()
+    # Map COVID-19 positive to 1; healthy/uninfected/controls to 0
+    if "covid" in s or "sars-cov-2" in s:
+        if "+" in s or "positive" in s:
+            return 1
+        if "uninfected" in s or "healthy" in s or "control" in s or "negative" in s:
+            return 0
+    if "healthy" in s or "uninfected" in s or "control" in s:
+        return 0
+    if "positive" in s:
+        return 1
+    if "negative" in s:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v = v.strip()
+    # Extract numeric age
+    try:
+        return float(v)
+    except Exception:
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", v)
+        if m:
+            try:
+                return float(m.group(0))
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = v.strip().lower()
+    if s in ["female", "f", "woman", "women"]:
+        return 0
+    if s in ["male", "m", "man", "men"]:
+        return 1
+    return None
+
+# 3) Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if clinical data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    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
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+try:
+    selected_clinical_df
+except NameError:
+    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 in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Availability flags (cast to native Python bool to avoid JSON serialization issues)
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
+
+# 5. Conduct quality check and save the cohort information.
+note = "INFO: nCounter panel (~773 genes); longitudinal/self-collected capillary blood; multiple timepoints per participant."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/COVID-19/code/GSE273225.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE273225"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE273225"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE273225.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE273225.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE273225.csv"
+json_path = "./output/z2/preprocess/COVID-19/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 with a capped number of unique values
+sample_characteristics_dict = get_unique_values_by_row(clinical_data, max_len=20)
+
+# 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  # NanoString nCounter transcriptome panel (mRNA), not miRNA/methylation
+
+# Identify rows for variables in the sample characteristics
+trait_row = None  # No COVID-19 status available in this dataset
+age_row = 3       # 'donor age (y): ...'
+gender_row = 4    # 'donor sex: male/female'
+
+# Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str) and ':' in x:
+        return x.split(':', 1)[1].strip()
+    return x
+
+def convert_trait(x):
+    # COVID-19 status is not available in this dataset
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or str(v).strip().lower() in {"na", "nan", ""}:
+        return None
+    try:
+        return float(str(v).strip())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {"female", "f"}:
+        return 0
+    if s in {"male", "m"}:
+        return 1
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, 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 = False
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Ensure output directory exists before saving gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# If trait is unavailable, skip linking and record metadata as unavailable
+if 'trait_row' in globals() and trait_row is None:
+    linked_data = None
+    # Record metadata as unavailable (final linking not possible without trait)
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+else:
+    # 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+    # Use existing selected_clinical_df if available; otherwise, create it.
+    if 'selected_clinical_df' not 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
+        )
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Conduct quality check and save the cohort information.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data
+    )
+
+    # 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)

output/preprocess/COVID-19/code/GSE275334.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE275334"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE275334"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/GSE275334.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/GSE275334.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/GSE275334.csv"
+json_path = "./output/z2/preprocess/COVID-19/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 availability
+is_gene_available = True  # NanoString nCounter gene expression panel (mRNA), suitable for analysis.
+
+# 2) Variable availability
+trait_row = 3     # 'disease: Healthy control/Long COVID/ME/CFS'
+age_row = 1       # 'age (years): <number>'
+gender_row = 2    # 'Sex: Female/Male'
+
+# 2.2) Converters
+def _after_colon(value: str) -> str:
+    if value is None or (isinstance(value, float) and np.isnan(value)):
+        return ""
+    parts = str(value).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(value).strip()
+
+def convert_trait(value):
+    v = _after_colon(value).strip().lower()
+    if not v:
+        return None
+    # Map Long COVID as COVID-19 positive; HC and ME/CFS as 0 (not COVID-19)
+    if "long covid" in v or "post covid" in v:
+        return 1
+    if "healthy" in v or "control" in v:
+        return 0
+    if "me/cfs" in v or "mecfs" in v or "me-cfs" in v:
+        return 0
+    if "covid" in v and "long" in v:
+        return 1
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if not v:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group())
+        except:
+            return None
+    return None
+
+def convert_gender(value):
+    v = _after_colon(value).strip().lower()
+    if not v:
+        return None
+    if v.startswith("f"):
+        return 0
+    if v.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 clinical data available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    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
+print("requires_gene_mapping = False")
+
+# Step 5: 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
+# Ensure the clinical dataframe exists in the environment; if not, reload from file saved in Step 2
+if 'selected_clinical_df' not in globals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 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 quality validation and save cohort info
+note = ("INFO: NanoString nCounter Immune Exhaustion panel (~785 genes); matrix contained human gene symbols; "
+        "applied NCBI synonym normalization and aggregated duplicates; no probe-to-gene mapping needed.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/COVID-19/code/TCGA.py

@@ -0,0 +1,64 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/COVID-19/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/COVID-19/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/COVID-19/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/COVID-19/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Find cohort directory relevant to the trait (COVID-19) — TCGA cancer cohorts have no COVID-related cohort
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+keywords = ['covid', 'covid-19', 'sars', 'sars-cov-2', 'corona', 'coronavirus']
+matches = [d for d in subdirs if any(k in d.lower() for k in keywords)]
+
+selected_cohort_dir = None
+if matches:
+    # Choose the most specific match (longest name heuristic)
+    selected_cohort_dir = sorted(matches, key=lambda x: len(x), reverse=True)[0]
+
+if not selected_cohort_dir:
+    print("No suitable TCGA cohort found for trait 'COVID-19'. Skipping this trait.")
+    # Record unusable dataset at initial filtering stage
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    preprocessing_skipped = True
+else:
+    preprocessing_skipped = False
+    cohort_path = os.path.join(tcga_root_dir, selected_cohort_dir)
+    print(f"Selected cohort directory: {cohort_path}")
+
+    # Step 2: Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_path)
+    clinical_file_path = os.path.join(cohort_path, os.path.basename(clinical_file_path))
+    genetic_file_path = os.path.join(cohort_path, os.path.basename(genetic_file_path))
+
+    # Step 3: Load both files as DataFrames
+    def load_tcga_table(fp: str) -> pd.DataFrame:
+        try:
+            return pd.read_csv(fp, sep='\t', index_col=0, compression='infer', low_memory=False)
+        except Exception:
+            return pd.read_csv(fp, sep='\t', index_col=0, engine='python', low_memory=False)
+
+    clinical_df = load_tcga_table(clinical_file_path)
+    genetic_df = load_tcga_table(genetic_file_path)
+
+    # Step 4: Print the column names of the clinical data
+    print("Clinical data columns:")
+    print(clinical_df.columns.tolist())

output/preprocess/COVID-19/cohort_info.json

@@ -1,102 +1 @@
-{
-  "GSE275334": {
-    "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": 46
-  },
-  "GSE273225": {
-    "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
-  },
-  "GSE243348": {
-    "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
-  },
-  "GSE227080": {
-    "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": 119
-  },
-  "GSE216705": {
-    "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
-  },
-  "GSE213313": {
-    "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": 94
-  },
-  "GSE212865": {
-    "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": 86
-  },
-  "GSE211378": {
-    "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
-  },
-  "GSE185658": {
-    "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
-  }
-}
+{"GSE275334": {"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": 47, "note": "INFO: NanoString nCounter Immune Exhaustion panel (~785 genes); matrix contained human gene symbols; applied NCBI synonym normalization and aggregated duplicates; no probe-to-gene mapping needed."}, "GSE273225": {"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}, "GSE243348": {"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": 237, "note": "INFO: nCounter panel (~773 genes); longitudinal/self-collected capillary blood; multiple timepoints per participant."}, "GSE227080": {"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": 119, "note": "INFO: NanoString nCounter Immunology panel (~579 targeted genes); PBMC samples; trait encoded as 1=C19+, 0=NEG."}, "GSE216705": {"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}, "GSE213313": {"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": 94, "note": "INFO: Only disease state available per sample; Age and Gender not provided in sample characteristics."}, "GSE212866": {"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": 137, "note": "INFO: Normalized gene symbols: 85633 -> 19975 genes (dropped 65658, 76.7%)."}, "GSE212865": {"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": 137, "note": "INFO: Gene rows pre-norm=85633, post-norm=19975; samples post-QC=137; features post-QC=19976"}, "GSE211378": {"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": 304, "note": "INFO: Trait (COVID-19) inferred from 'nanostring_id' field ('Healthy' as controls); Age/Gender not available."}, "GSE185658": {"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/COVID-19/gene_data/GSE275334.csv

@@ -2,7 +2,6 @@ ID,GSM8475033,GSM8475034,GSM8475035,GSM8475036,GSM8475037,GSM8475038,GSM8475039,
 ACACA,5.053499026,5.61759468,5.848362876,6.001664137,4.677176747,4.142635363,4.909480871,4.194120928,4.288927189,5.585403993,5.512302277,5.588805945,5.033500253,5.371794336,5.640135011,4.809492931,5.53100706,5.687656351,5.872504462,5.286716603,6.082970588,5.531309131,4.716622383,5.236023286,4.8631674,5.366902723,5.455920286,5.509410237,5.455260785,5.647497982,5.324415977,6.045475287,4.985590041,5.864546811,5.500456168,5.288482241,5.039519696,5.355034852,4.488420336,5.200545255,5.490906879,5.265386604,5.604202242,5.631610739,5.934476939,5.471953276,6.150709504
 ACADVL,10.45896782,10.42901163,10.32206863,10.4901638,10.25897197,10.00061636,10.11781976,10.31454397,10.07519682,10.46418095,10.18406961,10.60240355,10.39202936,10.44609324,10.15183038,10.02623879,10.2034324,10.10656308,10.01765596,10.62007528,10.32836483,10.44272939,9.945441073,10.17039462,10.26253958,10.25767365,10.331252,10.39951938,10.18747285,10.0563413,10.37355425,10.00133273,10.090211,10.08358958,10.69732171,10.45087357,10.31445031,10.22579728,10.92008643,10.65400297,10.37533802,10.44509263,10.40798171,10.29503867,10.74111606,9.694345698,10.32154501
 ACAT2,8.147475174,8.18665079,8.152143624,8.590210975,8.029162075,4.142635363,8.342440278,8.779083428,8.271920763,8.387325029,8.443589527,8.469224329,8.420081307,8.176115198,8.138940868,8.481918272,8.866397414,8.621347006,8.669852211,8.075212498,8.50699687,8.633111412,8.301584883,8.019399345,8.451661591,8.301576475,8.741971344,8.337643889,8.284148869,8.477936517,8.340012832,8.535036572,8.305070815,8.546127502,8.353691452,8.380182076,8.518989068,8.037463162,7.567647027,7.629856886,8.482083028,8.193708581,8.43583872,8.350874331,8.397547135,8.575046769,8.234309705
-ACOT1/2,6.522984309,5.939522775,5.603250378,5.818217996,5.287230228,6.557672863,6.155237285,6.672168225,6.016847643,6.29167279,6.275862081,5.753865191,6.053399811,5.710596249,7.312970268,6.057420444,6.145716904,6.179509448,5.895588076,6.149213079,6.385533358,6.326950633,6.038550478,6.150293412,6.079485307,6.121790225,6.404389985,6.123519084,5.962220774,5.99708242,5.909378478,6.120763414,5.702859834,6.528497608,6.027001983,5.86137191,6.316359901,6.610291907,5.178080215,5.52247335,6.437135622,5.917463301,5.94599076,5.859021235,6.15686936,5.62395637,6.230436696
 ACSL3,6.522984309,6.498950184,6.492219066,6.085322066,5.821566656,7.142635363,5.734394164,6.00147585,5.498380554,5.796908098,7.051821807,5.631874667,6.528911169,6.215068832,6.175466744,6.131421025,6.508286983,6.113921106,7.068055272,6.149213079,6.761042493,6.461600159,6.301584883,6.150293412,6.061106777,6.257673653,6.19288588,6.180102612,6.196017959,5.941229185,6.459065504,6.896331848,7.312400357,6.007151206,6.184954343,5.977781402,6.137821769,5.610291907,6.30240835,6.044894385,6.323179433,6.008228765,6.17480945,6.340147925,6.408408127,7.232765612,5.976680104
 ACSL4,8.581877998,8.576681487,8.655717798,8.793555942,8.896854783,6.557672863,8.480637572,8.066246105,9.02024622,8.825316513,8.69706232,9.114267434,9.08153295,9.24288421,8.957875309,8.495993458,8.591702991,8.327901288,9.43597754,8.131065733,8.675925879,8.443633613,8.497982096,8.569006569,8.097632653,8.658457168,8.899154677,8.567125735,9.049683615,8.228577048,9.058241864,9.011259572,9.143236713,8.644884182,9.076805539,8.870435992,8.875650994,8.252587583,8.748543146,8.888318562,8.009871821,8.810548097,8.872286754,8.881389048,9.52610317,8.928954069,8.044424711
 ACSL6,6.812490926,7.722813992,7.562608394,7.427428043,7.029162075,4.142635363,7.508118308,8.479523147,7.45885219,7.86885794,7.569853299,7.244851544,7.185503347,6.541719337,7.537235104,7.907525013,7.730679404,8.191482089,7.166235665,7.03673835,7.568397415,8.012842042,7.038550478,7.664866585,7.925451678,7.35179583,7.553767609,7.676060107,7.173117557,7.812242744,7.210244957,7.617498732,6.864498224,8.205090584,7.247690098,8.013678059,7.664010561,7.818691056,6.315583739,6.026515856,8.122039561,7.874678434,7.750121781,7.866515772,7.040676343,7.294494602,7.773426926
@@ -37,7 +36,6 @@ BCL2L1,8.461583764,8.397573163,8.067531397,8.012390544,7.686637076,8.843075082,8
 BCL6,8.49261066,9.226092695,8.609914109,8.787323988,9.06224443,9.000616359,8.799788582,7.925924817,9.877641824,8.948501277,9.054704315,9.605107757,9.012051834,9.907298439,9.484351802,7.907525013,8.388988055,7.775119193,10.62048084,9.605362114,9.511470347,8.338664053,9.271211234,9.85073313,8.142026773,8.893075892,9.187179762,8.866243947,9.763839327,9.001640702,10.29570961,9.120763414,10.29153584,8.961894925,9.667515836,9.837689331,10.18978774,8.477654138,9.338355047,9.50100182,7.755406693,9.003823586,9.376443311,9.461410808,9.951904011,10.42096935,8.113342651
 BID,9.7367636,10.00900788,9.74318064,9.882481221,10.28099827,9.932712294,10.17466055,9.346767938,10.75449359,10.1367581,9.4169544,10.65293628,10.30600045,10.5016834,10.21460514,9.464128959,9.357555547,9.551842496,10.05913792,9.385705698,9.707461453,9.817409147,10.60536563,9.944974504,9.501922718,9.868076447,10.11087031,9.595140111,10.15643043,9.089906453,10.10919305,9.558920914,9.990929125,9.969536914,10.51752968,10.3388751,10.24647142,10.13554816,10.03705583,9.903504965,9.022098123,10.09879307,10.20514553,9.822880643,10.25540281,9.471953276,8.564721998
 BLK,6.353059308,7.196215654,6.46307272,7.736474847,7.129688951,7.365027785,6.857013451,7.232256057,6.418210205,7.686204633,7.956402242,7.652936282,7.272966188,7.371794336,7.238969687,5.979417932,6.75982575,6.9914371,7.61874787,6.914747826,8.404898683,7.134305555,7.761016502,7.368473582,7.375617401,7.305502178,6.678312707,8.123519084,7.411904416,7.687655108,7.58875658,7.192316675,7.252694111,6.990077693,6.863026248,6.98892196,6.361447791,6.208193463,6.856152121,6.706897921,6.437135622,6.926801166,7.993114672,8.081413656,7.173942874,7.682850059,7.261133493
-BMP8A/B,5.600986821,4.795931921,5.170290971,6.866312284,5.383445544,6.365027785,5.028125367,5.556691007,5.498380554,4.659404574,5.640626374,5.003843444,4.972099709,4.49378486,5.055172511,5.564380433,4.618469901,4.816939368,4.61874787,5.871679104,6.263542834,5.357977528,6.301584883,5.179439757,5.33029341,5.174257645,5.350950726,5.449289245,4.761922124,5.474661385,5.292707117,4.966040819,4.80338671,4.642154389,4.780564088,4.966554147,4.368827321,5.793155964,4.578618145,4.599094632,4.888242376,5.432036474,5.515846368,5.18094933,5.086480032,4.886990775,5.861202887
 BPI,5.353059308,5.478191624,5.935825717,7.192962789,6.359986571,7.081234819,6.30375981,4.474228847,5.646479193,6.381870599,5.512302277,6.714336827,6.347391029,6.823490306,4.991042173,4.509932649,3.53100706,4.775119193,6.005770993,5.997209986,5.568397415,5.82575649,6.176054001,5.92790099,5.061106777,5.911223239,5.600310195,6.142627906,6.077697992,4.551282667,7.725666524,5.979587351,5.477986423,5.706284727,6.551082241,4.358871569,7.773708205,5.355034852,3.704149027,4.870396654,4.220817715,6.308888243,4.923270683,5.4835121,7.173942874,5.761459893,4.900731251
 BRWD1,9.728098739,10.19462591,9.892756996,9.718573156,9.11574976,7.879600958,8.962592207,9.81303076,9.251823194,10.03270477,10.69336784,9.972771998,9.759150534,9.702184009,9.861406893,9.648696719,10.467645,10.3023662,10.88192677,9.896063262,10.29761351,10.18493163,9.856173735,10.02943902,9.833696281,10.23829464,10.07114658,10.03680745,10.0330871,10.37014028,10.23548225,10.39925225,10.43442381,10.41474389,10.33935993,10.00066529,9.967221697,9.580747631,10.12106381,9.705454505,10.18184175,9.95559843,9.928271364,10.17944574,10.44403204,10.77099166,10.43358732
 BST2,11.26893869,11.30814411,11.27042764,11.2737542,11.58406734,10.17972468,11.74562142,11.48742834,11.96046826,11.73782228,11.21789448,11.32822859,11.62325542,11.76915439,11.64144121,11.30834214,11.40905797,11.21464456,10.90272661,11.07995042,11.08747198,11.36180939,11.29026957,11.03243734,11.28547956,11.35889689,11.28079531,11.11930507,11.38194692,10.87383248,11.57728705,11.30757013,11.72819921,11.0770353,11.34025983,11.23334069,10.96691781,11.08207695,10.91900778,11.22291307,11.01905125,11.68030909,11.61576165,11.3674763,11.67481989,11.10830936,10.47099724
@@ -55,8 +53,6 @@ CASP9,7.710611312,7.920413952,7.68125289,7.411308378,7.50606483,7.879600958,7.40
 CBL,9.615154153,9.744706598,9.619852346,9.683767796,9.495338424,9.065467503,9.204364142,9.203749629,9.597266219,9.861038435,9.478354945,9.541591307,9.626787619,9.896278436,9.651848432,9.645543286,9.695913986,9.763135501,9.622211725,9.438309782,9.209502994,9.783528254,9.523977305,9.585432117,9.265552519,9.405821712,9.575771519,9.538556583,9.839419685,9.624039009,9.668132761,9.543042034,9.730480876,9.743692416,9.593968054,9.659169648,9.660256473,8.690857388,9.376574369,9.322314219,9.352523394,9.440210405,9.88336458,9.817090555,9.814400487,9.403566301,9.69268608
 CBLB,9.836875085,9.671163362,9.895486788,9.656674135,8.624006088,9.511869173,8.901731371,9.381119443,8.881384226,9.941102824,10.73349595,9.778031502,9.630932106,9.450494577,9.813540582,9.364081782,9.936148523,10.10445388,10.44871449,9.669635328,9.944264317,9.809013226,9.705307069,10.06696503,9.578220924,10.22902345,10.0701135,10.37649982,9.617784623,10.56058041,9.864249102,10.57127572,9.840194335,10.30339266,10.17288151,9.655678551,9.754821746,9.5906631,10.07241296,9.748140904,10.02968728,10.15946192,9.717480229,9.872111235,10.12759157,10.79120724,10.12068463
 CCL2,5.353059308,4.829098785,4.655717798,4.257503042,4.821566656,7.081234819,4.638178849,4.087205724,5.146908184,5.195457474,4.255962524,4.714336827,4.418164104,4.371794336,4.338965477,4.616847853,4.923324482,3.272618852,2.725663074,4.149213079,5.263542834,2.610743599,5.886547384,3.995015186,4.360667059,3.869403063,3.529920867,3.595140111,5.699186368,2.693301672,3.26028564,2.408045366,4.529211746,4.352647772,2.278063747,2.966554147,1.754117477,3.985801042,3.441114621,4.326076137,4.381282387,5.076941515,4.338308182,3.974498452,2.764551937,3.62395637,2.861202887
-CCL3/L1,7.10794681,6.901048627,7.837047563,7.220977166,6.838640169,8.390562877,7.80810385,7.11468646,7.947138671,8.451218645,6.443589527,10.17922288,9.88038354,7.085231394,7.377100605,8.43154475,7.639531516,6.785688434,6.240236247,7.660174999,10.27411208,8.147684584,7.716622383,6.893135572,6.718219064,9.088001911,9.575771519,8.362305943,8.459998704,7.698926221,7.276587452,7.314935962,8.879380024,7.840934253,9.718240809,6.195372837,5.522301802,6.073263883,6.534224026,6.930938196,6.687943726,7.972604855,7.193918273,7.657763027,6.190816692,5.471953276,7.432744872
-CCL4/L1,9.888334684,8.812610663,9.55916931,9.176366279,7.495338424,9.049525959,9.614668904,9.513470479,8.619844067,9.387325029,8.864114652,9.338827692,9.475522194,8.833153403,9.084279652,8.842915932,8.816409278,9.294986665,8.302012444,9.755062947,9.667933089,9.287204454,8.782711573,8.970211795,9.051828964,10.19662546,10.07217892,10.0671279,8.522482834,9.458173262,9.397043777,9.754805314,7.960927987,9.498627078,9.768134638,9.099696359,7.719901762,9.352123256,8.847106981,8.192324749,8.62520797,10.12031858,8.722423792,9.125807776,8.686392874,9.254284175,8.546146268
 CCL5,12.6749874,13.0075373,13.18486944,13.49877122,11.96306562,14.39884405,13.08967421,14.02316405,12.20181652,12.8792991,13.13673647,12.87672816,12.51024646,12.64504143,12.7691193,12.59330969,12.62310347,12.86351773,12.56621623,12.84618061,13.66165776,12.32228767,12.41185067,13.32307807,12.69083701,13.68186354,13.74593913,13.26946821,11.87111372,13.0479643,12.61543649,13.7017086,12.1298162,12.79865888,13.44840613,12.76613933,12.50252001,13.3302819,12.9723661,11.96662236,12.58482359,13.50924062,12.2978962,12.73477059,12.53686651,12.972537,12.22970935
 CCNA1,4.812490926,3.954629668,3.433325377,3.257503042,3.888680852,3.557672863,3.587552776,2.971728506,1.873889689,3.507401481,4.729893712,4.351766748,3.56401497,4.039218997,3.779538068,3.394455431,3.338361982,3.272618852,3.918308152,4.527724703,5.082970588,3.503828395,4.716622383,4.535583568,3.360667059,3.589295144,3.870957785,3.894700393,2.529261367,3.693301672,3.707744616,3.630437788,2.56237861,3.685223111,3.751994935,4.288482241,4.180382232,4.307729137,2.993655644,3.548468559,3.133354874,2.972604855,4.193918273,3.274058734,2.764551937,3.886990775,3.276240386
 CCNB1,6.10794681,6.124554669,5.935825717,7.03360703,6.073105423,6.142635363,6.22314135,6.194120928,6.498380554,6.466759496,6.609599478,6.631874667,6.306518748,6.588112243,6.131523397,5.896955772,5.990438678,5.897109717,6.258158155,6.412247485,6.498008087,6.107169425,6.623512978,6.090172419,6.079485307,5.759220145,5.812854831,6.402495033,6.161529582,5.119566426,6.621032983,5.910545707,6.360334834,6.298199988,6.393540964,5.897291484,6.390742098,5.793155964,5.534224026,5.741113637,5.220817715,6.042994183,6.231392979,6.274058734,6.5847309,6.302028275,5.841025005
@@ -89,8 +85,6 @@ CD3G,9.701787462,9.850457368,10.04803522,10.19178364,9.584067342,10.05751875,10.
 CD4,10.5794715,10.9532201,10.97506735,10.56584207,11.43837802,11.96918385,11.14870432,11.05210192,11.45822588,11.03531284,10.68036213,10.61587419,10.79024188,11.04463562,10.76283174,11.03590512,10.97187623,10.63163627,10.69309521,10.85795212,10.82353842,10.66602603,10.84074369,10.9127941,10.91717311,10.60928293,10.94945976,10.32932862,11.13585165,10.07159653,10.52962487,10.57239365,10.84399257,10.99970639,10.98350415,11.05302214,11.02002549,10.98203912,10.16721922,10.7169616,11.08318158,10.6667886,11.10995485,10.89976758,10.99216788,10.16621442,10.841025
 CD40,6.779324062,6.673447915,6.914452067,6.755753909,7.299613953,8.645135704,6.722712359,6.474228847,7.121817203,7.695028484,7.648279946,7.37730184,6.746218301,7.574350341,6.871460557,6.831860744,7.231446778,6.582474115,7.147126843,6.412247485,8.252895589,6.890851518,7.523977305,6.820985786,7.193557073,7.1128571,6.935913227,7.044447513,7.492735491,7.337157861,7.113736976,7.254928653,7.900608517,7.040703766,7.141561747,7.238017174,7.308706329,6.530121558,6.26214448,6.671850974,6.805780216,6.470855723,7.496737545,7.28528599,7.02193978,8.130693703,6.881102444
 CD40LG,9.067304825,8.581555461,9.16577549,8.524289582,8.324066996,9.172382707,8.42044279,8.886611893,8.365742786,9.253355858,8.693367836,7.714336827,8.294027804,8.11405423,8.559516551,9.513396504,9.823328692,9.272618852,8.689797622,8.548384173,8.667933089,8.652011617,8.208475479,9.183041994,8.697545496,8.753483776,8.490309011,8.807589729,8.664714151,9.340760098,8.84524814,8.763641174,8.102946992,9.270185612,8.470082519,8.788328129,8.747055813,8.55565665,6.600313216,7.007900177,9.473204877,8.76181243,8.995839309,9.290867022,8.049954156,8.556269562,8.719183882
-CD45R0,11.43121012,11.78962734,12.11631996,11.59155281,11.78735094,12.1026373,11.82493348,11.86856544,12.03921546,12.06113742,11.81438324,12.18012995,12.04845059,12.08219563,11.51032101,12.23994548,11.96979891,11.53236212,12.04791316,12.27022848,11.90824742,11.08821278,11.78540066,12.32511721,11.15128707,11.89633328,11.4638082,11.67565019,11.92992737,11.36572701,12.50042647,12.16941191,12.32014741,11.98658481,11.55134132,12.26576216,11.49431987,11.69200924,10.76244147,11.10250917,11.38720236,12.15027354,12.43433304,11.66207602,12.1010585,11.87409685,11.07767931
-CD45RA,11.23875568,10.8229378,10.82134266,11.14502831,10.22041212,10.80084685,10.57704865,11.17466857,10.42463647,10.99836375,11.13860557,11.32986429,10.69962561,10.51555268,11.17230739,10.99064519,11.2733165,11.24032545,10.87903415,10.79595178,11.16901833,11.84162181,11.10893981,10.16676062,11.12166415,11.35889689,11.38707249,11.11870207,10.62465839,11.29646435,11.03686821,11.02070912,10.44890659,11.38198613,11.34743892,10.68594297,11.21636411,10.50805562,11.10597667,10.37410883,11.37414621,10.89027529,11.03907815,11.31434846,10.97522328,11.45515803,11.47673035
 CD48,11.7960028,12.12497206,11.65652265,11.84509306,11.65144037,12.27706168,12.24499884,12.27474814,11.87951424,12.19078603,12.36970469,12.29762857,11.97128273,11.86010707,11.79575989,12.21463439,12.39789959,11.93142538,12.38106602,11.80026477,11.6272911,12.39981141,12.05869705,12.08681117,12.08802031,11.70415294,12.08527691,11.59297097,11.79323472,11.93558262,12.17589345,12.52511122,12.61741449,12.32333955,12.1554331,12.24819445,12.23212865,11.93562775,11.25104349,11.4471779,11.88865976,12.11522251,12.15635771,12.29017057,12.05399451,12.81229795,12.02331443
 CD5,10.03955983,10.29002002,10.51192098,10.2997096,9.900924448,10.70741998,10.25593128,11.20294969,10.35097333,9.931801083,9.704423024,9.89313998,9.612606634,9.302531673,9.673717612,11.05504564,10.78485454,10.84057493,10.67819634,10.18703454,10.53143109,10.46160016,10.5495124,10.56072313,10.36722691,10.63446973,10.2989873,9.90172766,9.931847125,10.32811272,9.593644315,10.14049548,9.193059411,11.03655212,10.38193434,10.30696064,10.66775491,10.7238933,9.50576758,9.406449554,11.36936907,9.91511936,9.934675446,10.23116078,9.381100781,9.71988079,10.84862482
 CD6,9.732437675,10.68368387,9.077181567,10.24732326,9.643568354,10.76712623,9.748864647,10.71992136,9.569697959,8.487223599,10.22046384,9.850473515,9.154745951,8.665874446,8.520295241,9.67677167,9.360729795,10.45666767,9.881204157,9.131065733,10.63755944,9.264868124,9.935790903,10.2115406,10.73135447,10.63134532,9.874512682,9.679335239,9.335327593,9.216863628,9.790957985,9.758739045,7.897362858,10.36346076,8.677994354,9.74791386,8.858716231,10.01370704,10.1012294,9.673328389,10.7879909,8.779959777,9.329695052,9.702418907,7.830641128,8.424424906,10.97146868
@@ -134,14 +128,12 @@ CRTAM,7.922914916,7.020718858,6.977645893,8.257503042,7.249428196,8.285593317,7.
 CSF1,5.812490926,6.164083033,6.240680299,5.742929869,5.638702599,3.557672863,6.083978602,6.317503343,5.536854702,5.892065331,5.758462864,5.188268015,5.339308683,5.928187684,4.991042173,6.69213598,6.338361982,6.062695783,5.203710371,6.112687203,5.263542834,5.262820295,5.523977305,6.579977687,5.633685554,5.454365564,5.908432491,6.402495033,5.699186368,6.169035103,5.582213734,5.838033207,5.960927987,5.528497608,4.94102876,5.341593578,4.982936167,5.445232661,5.26214448,4.870396654,5.381282387,5.378597215,5.604202242,5.028946237,3.223983556,5.417505492,5.498632807
 CSF1R,10.49771755,10.96072194,11.33921375,10.980652,11.35480367,10.77684138,11.12671159,9.660561605,11.90685375,11.38732503,9.864114652,11.4301082,11.44595249,11.51014144,11.46308679,10.58757006,9.837068749,9.677760315,10.1459241,10.40660092,10.23677592,10.40608754,11.23625864,10.25325999,10.15832859,10.50900978,11.06286116,10.71008369,11.56240902,8.667716261,10.56738293,10.40679248,10.94765276,10.21062877,10.89644925,11.26547589,10.52560695,11.02654738,11.48368601,11.95452168,9.607060624,11.06589036,11.23485683,10.72629997,11.43874421,10.02426658,8.960404564
 CSF3R,10.31884359,10.83012306,10.24282557,10.43242872,11.07670766,10.84769171,10.19593962,8.817218557,11.1913023,10.98583406,10.44688461,11.52383702,10.64247373,11.93740047,10.69526023,9.812307946,10.20696409,9.36184862,12.12440677,11.58300714,10.69964195,10.07017522,10.89154807,11.65192753,9.873736641,10.01915018,10.55006744,10.18356647,11.58752374,10.44400866,11.95440605,10.90345628,12.15873781,10.45028978,11.37609583,12.23830895,11.69864654,10.56448822,11.19733733,11.13450409,8.884898933,10.81934888,10.88078735,11.01046067,11.74504857,11.64558415,9.897071135
-CSH2/GH2,5.259949903,4.457130008,4.848362876,3.520537447,4.751177328,4.879600958,2.587552776,3.386766006,2.610855283,5.244367075,4.478354945,3.714336827,3.255892675,3.743763113,3.531610555,3.616847853,3.700932061,4.211218307,3.377739771,5.075212498,6.761042493,4.803388676,2.716622383,4.623046409,4.419560748,4.174257645,4.207992772,4.595140111,3.851189462,5.119566426,4.608208943,3.238120365,3.350874505,4.24161646,2.567570364,2.666993865,2.339079978,4.307729137,4.289111528,3.548468559,4.65691683,3.898604274,3.338308182,3.859021235,2.571906859,3.302028275,2.691277885
 CTBP2,7.844912404,7.687079781,7.46307272,6.842465542,7.223665099,6.464563458,7.239629472,6.536513125,7.333321308,7.829329576,7.585883409,7.020145257,7.16278327,7.334180044,7.286498057,5.809492931,6.870857062,7.130599847,7.175695995,7.893374175,7.87651971,7.060776519,6.886547384,7.316943281,6.985157924,6.700803459,7.023909708,7.410400231,7.336616289,6.950689514,7.914596187,7.089869406,7.61864683,6.480403319,6.882925805,7.080296313,7.025580505,6.530121558,7.689042135,7.574003651,6.54274581,7.110108379,6.968358573,7.204796072,7.272346578,6.92651914,6.573920934
 CTF1,4.938021808,3.070106885,4.018287878,5.32789237,4.429249233,4.557672863,2.102125949,3.846197624,3.288927189,3.337476479,4.478354945,4.631874667,3.323006871,3.606259589,3.531610555,4.509932649,3.990438678,3.38809607,3.61874787,4.412247485,4.176079992,3.803388676,5.523977305,3.995015186,3.235136177,3.688830817,4.481011267,4.064625394,3.207333272,3.500656594,3.608208943,3.325583206,2.56237861,3.504650866,3.108138746,3.358871569,2.339079978,4.400838541,4.234663744,3.326076137,2.940709796,4.458031682,3.193918273,3.595986829,1.764551937,3.524420696,3.778740726
 CTLA4,8.69290931,9.561959981,8.700111918,8.374366799,8.511398274,8.049525959,8.243722227,10.06272304,9.143795572,8.841302217,9.238322365,8.216837168,8.467201597,8.617486845,8.335741576,9.076279471,10.48083377,9.088535788,8.725663074,9.153714472,9.801788835,9.380817505,8.866369502,8.783511081,9.397756378,8.869403063,9.194782915,8.822478355,9.326274345,8.778110059,9.473279363,9.372546685,9.513398343,9.504650866,8.700595783,8.767454046,8.509004979,9.008168855,8.551032313,7.817929234,9.5804381,9.542460463,7.816355479,9.144423454,8.122103942,9.407063745,10.05431752
 CTSW,11.85091114,11.22336659,10.85258334,11.60445993,9.979996019,12.23715296,11.67653026,11.34966201,10.77375008,11.12701012,11.5434184,10.6711163,11.25857377,10.40974497,9.400433109,10.62807511,10.51542552,11.66242942,10.50045013,11.58133735,10.93596818,10.94677189,10.86128063,11.37399964,11.04541568,12.0402118,11.75713567,11.86156629,10.81835807,11.90518997,11.04224138,11.45000866,10.187091,11.4278793,11.68816171,11.19326737,10.17723339,11.65468603,11.2527569,10.35306058,11.39368639,11.7022256,10.94458119,11.34283701,9.77996699,10.77302601,10.64329105
 CUL1,9.986054505,10.12538932,9.853987425,10.02833209,9.698375912,8.932712294,9.896541815,10.13831006,9.930978881,10.02572679,10.15212671,9.753865191,10.02785369,9.929089087,9.892457639,10.06923619,10.54083568,10.23647826,9.817665075,9.896063262,10.14762355,10.1554321,9.549512397,10.011317,9.943122704,9.961849312,9.87923896,9.994048203,9.870891376,10.05524545,10.28063428,10.05909706,10.03516174,10.19208487,9.917906874,9.919295394,9.710493634,9.50412635,9.424488303,9.519190766,10.13053435,10.09258346,10.1077525,10.17341566,9.875687608,10.164149,10.03224065
 CUL2,9.42452167,9.543344303,9.4106053,9.468904679,9.397916026,11.24417339,9.166060254,9.574118079,9.23729442,9.643538169,10.04603945,9.558432296,9.387137208,9.37046772,9.456423058,9.364081782,10.16400226,9.62429429,10.54675369,9.578829044,9.970495859,9.754673391,9.458089369,9.596279499,9.397756378,9.590904397,9.632858889,9.533136307,9.387242362,9.659085956,9.949330603,10.0268387,9.925517823,9.838551602,9.719163543,9.631890064,9.687070369,9.511974211,9.476738531,9.44501721,9.768165924,9.635095219,9.620183028,9.765911831,10.04067634,10.54758098,9.768093482
-CXCL1/2/3,5.440522149,4.691595262,4.548802594,4.18350246,3.888680852,2.557672863,3.687088449,4.708694101,4.37639003,4.244367075,4.840925024,4.866339921,5.478285096,5.474156053,3.116573055,4.809492931,5.054569016,3.272618852,4.310625575,5.73417558,6.533631997,4.503828395,5.523977305,5.393564563,4.023632072,5.305502178,5.237740116,4.142627906,5.184613195,5.216863628,5.555741523,3.56004846,5.312400357,4.883162489,4.062335056,5.21448166,5.323973085,5.258819536,4.926541448,4.384969827,4.381282387,5.323102102,4.618416102,4.081413656,2.764551937,4.846348791,3.691277885
 CXCL11,4.938021808,4.013523357,4.307794495,3.257503042,4.014211734,5.142635363,4.028125367,2.708694101,3.288927189,4.144831401,4.609599478,4.244851544,2.863575252,3.807893451,3.923927977,3.394455431,3.338361982,3.009584446,2.918308152,4.914747826,4.983434914,3.973313678,6.301584883,3.535583568,3.097632653,4.48237994,4.266886461,3.982163234,3.114223868,3.15273329,3.501293739,3.145010961,3.910301914,2.405115192,3.356066259,2.136479148,2.213549096,4.400838541,3.234663744,3.911038638,4.220817715,4.65067676,3.115915761,3.733490353,3.671442533,3.417505492,3.778740726
 CXCL8,7.235702357,9.114501004,5.892756996,6.310614378,7.58912057,7.645135704,8.932482696,9.433889918,8.083343055,8.131892346,5.443589527,8.020145257,11.85172735,9.760064926,4.779538068,10.31133012,9.248683483,5.045208356,6.659775138,7.935809441,12.5203759,9.039402868,6.716622383,7.027436664,7.576604458,10.73030745,11.32563587,8.685863282,10.07383948,8.274502254,7.980763111,7.717900629,9.316051211,10.65575199,6.174227936,6.885417384,6.765344732,6.044694731,8.917928318,6.880664989,6.733267716,10.68547572,8.506323594,6.649098166,5.256405034,5.62395637,6.713645698
 CXCR3,7.812490926,7.472954975,8.057816242,8.164393637,7.58912057,9.285593317,7.76888254,7.690546754,7.300154444,7.703798693,7.141319958,6.631874667,7.521786735,7.317754496,7.116573055,8.467704413,7.982218171,7.837403471,7.344572907,7.27022848,8.021570043,7.365631101,7.761016502,7.745036933,7.42675625,7.321099033,7.430385194,7.456942817,6.582372703,7.726724673,7.029672711,7.156983602,6.837656612,7.653042705,6.902554612,7.32849792,6.383474097,8.182198255,7.163580646,6.83914572,7.664736334,7.779959777,7.571699603,6.960559262,6.597441952,6.846348791,6.830829238
@@ -149,7 +141,6 @@ CXCR6,7.330339231,6.435756357,6.870730689,7.474733758,7.11574976,8.142635363,7.1
 CYLD,10.54041138,10.83063493,10.72641379,10.55579477,10.03842797,11.41565386,10.18958879,10.98946487,10.3251008,10.84824065,10.744249,10.49423854,10.38566632,10.17383546,10.48362372,10.93877595,11.30249653,11.06973994,10.93789888,10.77272882,10.89204198,11.19140596,10.80937952,10.94178857,10.80843909,10.76920423,10.74326815,10.41040023,10.6487213,11.16700742,10.91264176,10.89096534,10.55364276,11.4063223,10.78761885,10.85339385,11.05074695,10.25570692,10.34399215,10.29761969,10.97884492,10.60560005,10.60687803,11.03228195,10.73968339,10.90689033,11.15939277
 CYP26A1,4.674987402,3.539592168,3.655717798,3.395006565,4.336139829,4.142635363,3.587552776,3.708694101,2.610855283,4.337476479,4.78647724,4.129374326,3.185503347,4.039218997,3.43850115,4.268924549,4.338361982,3.687656351,3.61874787,4.527724703,5.424007506,4.125316771,4.716622383,4.34293849,3.775704558,4.366902723,4.08246189,4.287017816,3.666764891,4.015229767,3.385816522,3.56004846,2.424875086,1.59776027,2.915493668,1.966554147,2.661008073,4.400838541,3.704149027,3.911038638,3.833794592,4.042994183,2.267918855,3.595986829,2.349514438,3.62395637,3.391717603
 CYP26C1,4.353059308,3.457130008,4.018287878,2.257503042,3.014211734,2.557672863,2.780197854,1.971728506,2.288927189,4.144831401,4.255962524,2.866339921,3.185503347,2.869293995,3.338965477,2.809492931,3.990438678,3.935583865,3.240236247,3.634639907,4.761042493,4.125316771,3.716622383,3.120546068,3.235136177,2.951865223,3.710493113,3.894700393,3.792295773,3.863226673,3.888316862,3.630437788,3.960927987,3.59776027,3.195601587,1.773909069,2.661008073,3.208193463,4.178080215,4.063041732,3.718317375,3.110108379,3.267918855,3.081413656,1.764551937,2.524420696,4.01320598
-CYP4A11/22,5.876621264,4.070106885,4.170290971,2.742929869,4.429249233,3.557672863,3.587552776,4.194120928,1.873889689,5.29167279,4.893392444,4.003843444,3.448537753,3.606259589,3.43850115,4.268924549,3.990438678,3.495011273,3.503270653,4.914747826,6.346004994,3.803388676,6.301584883,4.857511662,3.476144277,3.951865223,4.015347694,3.354132012,4.014688194,4.863226673,3.888316862,3.145010961,2.687909492,2.59776027,2.808578464,2.551516647,1.754117477,5.155726043,3.782151539,3.326076137,2.940709796,3.458031682,3.115915761,3.595986829,3.223983556,3.417505492,3.778740726
 DAPP1,9.661398338,10.15675349,9.593196714,9.872212886,9.948885486,10.23715296,9.684079699,8.86654627,9.826145589,9.933666235,9.977821225,10.06208121,10.2288041,10.56799866,9.932489991,9.409405773,10.28923027,9.70028089,10.73928484,9.286716603,10.0371669,9.42909752,9.856173735,9.402862307,9.22691567,9.535156971,9.359432203,9.460754431,9.878874339,8.822584689,10.21342387,9.706744408,10.2577651,9.859855115,9.599991842,9.829966106,10.57788276,9.414859012,8.511503949,9.712657191,9.006798986,9.601961475,10.20691029,10.06359238,10.58393273,10.05133906,9.104905814
 DCTN6,8.804270419,9.164083033,8.693852927,8.90424174,8.429249233,9.299139849,8.511516885,8.994096319,8.862574376,8.556645,9.245908859,9.181036446,8.901122206,8.72934634,8.765230231,8.792486505,8.948859574,9.124367893,9.972234034,8.716028233,9.380651137,9.336718409,9.316535225,8.910622999,9.176583995,9.058064627,9.130824912,8.807589729,8.918139706,8.948330241,9.239645873,9.066256849,9.0038138,9.507653354,9.739315801,9.145218998,9.374337303,9.42042927,9.113539963,8.782772788,9.400141415,8.940695607,8.866343615,9.025602793,9.19291211,9.426149587,9.234309705
 DGKA,9.600986821,9.408586156,9.444552632,9.100481873,9.133152807,10.17238271,8.974954708,9.601085126,8.918283809,9.400871561,9.573877556,8.987355322,9.146663605,8.641883499,9.202266804,9.58427999,10.11784685,10.04080318,9.414163179,9.909434026,9.456850762,9.82714303,8.716622383,9.421279941,9.844482836,8.903473739,9.185272696,9.354132012,9.210148288,9.768780821,9.154000846,9.349256035,8.897362858,9.899256465,8.717317484,9.104825947,9.384384602,9.214619732,8.556591839,9.089841791,10.36457605,8.900975178,9.307934533,9.72320738,8.707066443,9.346422394,10.21091414
@@ -186,9 +177,7 @@ FBXW4,8.804270419,8.627055009,8.802559187,8.361839701,7.944949071,9.325857188,8.
 FCAR,7.353059308,7.61284115,7.340215973,7.32789237,8.262139247,7.767126228,7.111114732,5.971728506,8.109106151,7.738355916,7.235784642,8.615874188,8.777960384,8.739658715,7.345391746,7.786772854,7.888559064,6.331512541,8.860822657,8.758022322,9.176079992,7.77493223,7.301584883,8.215063667,7.419560748,8.174257645,8.741971344,7.649587895,8.719085926,7.247890523,9.062948866,7.564549852,9.34968759,7.928677148,8.50260144,8.566466989,9.17933338,7.68624076,8.441114621,8.026515856,5.65691683,8.454807781,7.697534819,8.174925542,9.02193978,8.725494396,6.214839841
 FCER1G,10.47459282,10.29522161,10.47953993,10.31603601,10.66167016,11.50895758,10.63506624,9.151637596,10.91372264,10.10896595,10.33159023,10.53309651,10.63423914,10.66965467,10.38101566,9.648696719,9.774181043,9.526860139,10.106897,9.841305455,9.502509479,9.666026034,10.83556346,9.695454904,9.754415018,10.06387025,10.47008762,10.22337532,10.67136842,8.468088731,10.54400541,9.631510822,10.29338901,9.73218659,10.31759211,10.19115883,10.28598625,10.23530959,10.55936359,11.08096364,9.328111728,10.21291918,10.24577116,10.2641627,10.04995416,9.068004956,8.929682624
 FCER2,7.211040303,7.593668841,5.706343871,7.230195696,7.336139829,8.365027785,7.319356665,7.420189007,6.219664526,8.144831401,8.250944449,8.475149163,7.518211281,8.170027868,7.537235104,7.394455431,7.807131465,6.764471948,6.673195654,7.350846941,9.088969329,7.125316771,8.074174387,7.249829085,7.786931814,7.831788771,7.023909708,7.636167379,8.612474735,8.063989078,7.363096445,7.336152448,7.910301914,7.284260797,7.470082519,8.288482241,7.982936167,6.182198255,6.441114621,5.969932327,7.303279875,6.69507088,8.40131798,8.32934117,7.42276342,8.043495261,7.622015223
-FCGR1A/B,7.860853948,7.829098785,7.767226113,8.059696259,8.944949071,8.557672863,8.613087868,6.409133819,9.929172125,8.763741234,7.751373439,8.763580346,9.316845886,9.195723482,9.078505014,7.467704413,8.331906959,7.442543854,8.601302736,7.112687203,7.970495859,8.242180529,8.8459054,8.283776417,7.299266515,8.103868317,8.219220028,8.156795238,8.562684368,6.185154768,8.52195321,8.571275715,8.52711328,7.136919081,8.737495366,8.409497642,8.89877572,7.319701779,6.90054624,8.96752583,6.559619629,8.154502498,9.032126985,8.859021235,10.1010585,7.916738119,5.920096576
 FCGR2B,8.532968398,7.988052669,7.859590132,8.474733758,8.972281053,9.299139849,8.137749858,7.761805437,8.892553534,8.772104707,8.807136711,9.223891925,8.500199873,8.865532076,7.867000909,7.523738448,8.108435888,7.882413206,9.304170553,7.397140593,9.385533358,7.953135796,8.471509885,7.901905782,7.741488843,8.374397259,7.731554728,8.456942817,8.146912486,8.538791723,8.809282643,8.459707486,8.95937252,8.057191889,7.791133329,8.256572993,7.903864597,7.600510886,7.001829576,6.950567002,7.177748993,7.981593638,9.273543404,8.68344967,8.841367534,9.168276886,7.356613802
-FCGR3A/B,11.76245024,12.01239581,12.72832896,12.26368884,11.21878288,12.80678632,12.35833464,11.91765999,12.48829281,12.50363956,12.07897586,12.25693883,12.32095613,12.58639917,12.5532846,11.27607927,11.25892751,11.39717101,11.57473302,12.20936857,11.34098692,11.78745105,11.89653147,11.75172312,11.83640049,12.23495358,13.11363048,13.22603725,12.18228555,11.54778051,12.45078378,11.95515411,12.33311758,11.50013538,12.93905003,12.7705843,11.46277031,11.91505945,12.5698823,12.13235723,10.94231905,12.54302984,11.92720125,11.77838118,11.75607378,11.96023976,10.28186494
 FCRL2,8.259949903,8.031445265,7.170290971,8.39909932,7.777977387,9.27191838,7.662840903,7.526317358,7.003172706,8.977721415,9.461076954,8.987355322,8.481960754,9.003720315,8.269702814,7.438849551,8.6237642,7.501437543,8.649627183,7.660174999,9.993063616,7.998145531,8.804085224,7.79297141,8.106621437,8.05340325,7.812854831,8.939094512,8.374751418,8.985623304,9.029672711,9.004649274,8.937418042,7.46812499,7.987721995,8.299709497,7.814813409,7.343353047,7.213704125,7.509297962,7.861275329,7.732825802,9.68070038,9.374721073,8.867839746,10.29826636,8.183130981
 FGF10,5.259949903,3.829098785,5.548802594,4.636014665,4.751177328,5.727597864,5.206462608,5.194120928,4.812489145,4.659404574,5.609599478,5.129374326,3.670930174,3.454256496,4.232050273,4.979417932,4.990438678,3.38809607,4.088233153,5.219602407,5.498008087,4.447244866,6.623512978,4.535583568,4.299266515,4.029867735,4.377917774,4.982163234,3.792295773,3.941229185,4.26028564,3.325583206,3.424875086,4.352647772,3.751994935,3.054016988,3.561472399,5.68624076,4.819626245,4.785507756,4.718317375,4.405564262,3.700878262,3.595986829,3.764551937,3.524420696,3.691277885
 FOS,8.419148498,9.939522775,8.371924832,8.378518443,9.728457252,10.39056288,10.94258918,9.436614555,9.344209624,9.262288983,7.343425365,7.067973782,11.95699282,9.710596249,7.880338709,11.01406407,9.370210848,6.785688434,7.951731154,9.210989277,11.56732358,9.470500219,7.240184339,8.200919485,9.423162985,11.38834543,11.63285889,10.14499881,10.74358049,9.17913098,8.03925876,7.863829208,9.474723636,10.11542966,7.91871038,7.711387984,7.924042478,8.400838541,10.48623719,10.40110953,9.228751897,10.43203647,8.998558811,7.568679483,8.068332686,7.302028275,8.861202887
@@ -208,7 +197,6 @@ GBP3,8.067304825,8.46769925,8.038187435,8.568570144,7.991491657,9.737581953,8.17
 GK,6.967769152,7.46769925,7.133765095,7.154743467,7.609158323,7.68695588,6.950122855,6.726616009,7.84351604,7.507401481,7.561770953,8.15912167,7.894242388,8.431536419,7.839949585,7.018946296,7.449870297,6.954442892,9.558553088,7.946226057,7.498008087,7.295241773,7.761016502,8.4955855,6.830986994,7.156979653,7.571397503,7.362305943,7.90777299,7.300631985,8.595269887,8.00630469,8.935837006,7.592113707,8.050653251,8.900244802,8.822895755,7.434261543,7.170848646,8.053996592,6.62520797,7.43857932,7.632491287,7.988304252,8.808946057,9.294494602,6.995058633
 GLS,11.12949234,11.18745029,11.02204001,11.16919462,10.34811247,11.54066644,10.74381647,11.14248816,10.32921691,11.52049148,11.80157059,10.91073404,11.11712731,10.98563796,11.17004647,11.03108005,12.03731464,11.45832024,11.65808876,11.3201396,11.3092222,11.29273492,10.53680135,11.31940033,10.80912756,11.25463106,11.27587524,11.2571922,11.1209707,11.7539976,11.57399323,11.57546351,11.38052499,11.52627865,10.93150599,10.96831417,11.00085808,10.4975537,10.9625842,10.39843209,11.07660227,11.39256503,11.30216759,11.49504159,11.15525831,11.87630642,11.27153645
 GLS2,5.674987402,5.070106885,5.09629039,4.842465542,4.952811189,5.557672863,5.334786705,6.21965602,4.45885219,5.381870599,5.893392444,4.631874667,4.507431442,5.012251949,4.779538068,5.333054887,5.48520337,5.570299401,4.441870108,5.471141174,5.568397415,5.012842042,5.716622383,5.120546068,6.079485307,5.273793318,4.832483637,5.449289245,5.207333272,5.448189174,4.193171444,5.09610136,4.56237861,4.919688365,4.015029341,4.799444161,5.00204499,5.488301383,4.782151539,4.870396654,5.559619629,4.65067676,5.155444125,5.666376157,3.464991656,4.24062773,5.305987729
-GLUD1/2,10.61044715,10.79278308,10.68283393,10.67872634,10.69603577,9.824459403,10.95699433,11.04497749,11.09708696,10.98313491,11.01381548,11.05715229,10.81485997,10.85703172,10.77894473,10.68600988,10.83135962,10.66993696,11.096973,10.61681863,10.73066884,10.741498,10.78271157,10.71300311,10.58547269,10.71931404,10.81840248,10.52132205,10.7800335,10.54192461,11.09375078,10.88422901,10.95508632,10.90211159,10.9865729,10.89025303,10.9374427,10.5906631,10.17265994,10.25582769,10.59483432,10.77479807,10.82672219,10.825767,11.17817987,11.01570118,10.66569247
 GNLY,12.94971498,12.20571752,12.26357647,13.42692702,11.61288461,12.0614986,12.52713199,12.7688496,12.17004115,12.31368257,12.87291972,11.09804112,12.10093461,11.7215634,11.66314602,11.43976006,10.71959591,12.45377111,11.69062273,13.02081035,11.78433481,12.30099106,11.03855048,11.16037902,11.58225418,13.56637059,12.90915481,12.95522746,10.81835807,12.2158832,11.77131946,11.77096328,10.86075366,11.605138,12.49682877,11.96496828,11.14723975,11.7910075,12.97366536,12.2503938,11.77540657,12.69780067,11.03643403,11.64746769,11.29447047,12.37706167,11.78072837
 GOT2,7.812490926,7.608071906,7.548802594,7.968997948,7.760166111,5.365027785,7.791425109,8.016122626,7.780780285,8.170366493,7.295490888,7.533096512,7.743158502,7.689472958,7.858040042,8.104113679,7.778934573,7.753745542,7.016340235,7.366443796,7.790789836,7.963260013,7.360478572,7.91928786,8.065723405,7.509860676,7.547842775,7.708481584,7.603938053,8.072680039,7.601753921,7.57351072,7.2062348,7.981464562,7.677994354,7.691068,7.705402192,7.908633181,6.791611868,6.611478356,7.894906106,7.845433615,8.041387782,7.789758573,7.264397825,7.176497393,7.432744872
 GRAP2,9.820664858,10.03589117,9.579681907,9.557626766,8.760166111,10.22300878,9.018602593,9.323403945,8.659614595,9.303260764,9.549561896,9.478102481,9.27931702,9.209143998,9.460868963,9.534006784,10.07982897,10.01857323,9.269247334,9.615187544,9.798131812,9.807609143,9.511038249,9.68024181,9.397756378,9.744836227,9.367864109,9.558258497,9.077697992,9.767443134,9.312606675,9.316271173,8.234803952,10.00502803,9.211426554,8.997221283,9.978119151,9.243188885,8.943614962,8.913541145,9.945532179,9.446716369,9.363843275,9.618354642,9.188718226,8.889493283,10.01545843
@@ -237,7 +225,6 @@ HLA-DQA1,10.66594226,9.788573898,11.2015097,10.3171179,11.83226126,11.35857276,3
 HLA-DQB1,10.38648231,8.323863477,9.649262776,8.901359231,11.60978004,7.949990286,2.365160354,8.073266533,11.8149373,1.92243898,10.45235974,9.696696668,1.670930174,10.64517858,8.762831735,9.710359739,10.21575568,9.781734037,2.240236247,3.412247485,3.498008087,9.512472489,4.716622383,1.535583568,3.235136177,2.951865223,8.978381368,3.354132012,11.09835746,7.647497982,8.462632119,-0.761879635,10.6425905,2.59776027,2.278063747,10.30584745,1.754117477,9.81446647,1.534224026,10.38406786,3.303279875,9.852800584,10.40951513,10.948251,0.764551937,2.038993869,9.561642605
 HLA-DQB2,10.02194429,9.902996899,9.977645893,10.89485245,11.58153408,11.03340629,10.42855544,11.16237629,11.19956992,12.11678545,10.53518617,12.21107967,10.25964481,10.62473799,11.56744063,11.20823662,9.567180672,8.911658026,10.29660301,10.65383347,11.26221622,8.440013297,11.85617374,9.15396907,11.52599283,10.95811337,11.16267378,10.60550683,9.000123566,9.238266104,9.523661552,10.03578189,10.16566776,10.43381063,11.26390568,9.878695003,9.909568208,9.464233623,10.78866217,12.04118015,10.97098281,10.45803168,9.903092801,9.564077581,11.96636955,9.874412709,9.843562727
 HLA-DRA,12.12619851,12.54506078,12.44128703,12.96382637,13.27787217,13.37944684,13.19091419,12.37686997,13.81921159,13.37296293,12.72628246,13.6207264,12.88802238,13.16926514,12.94735232,12.3197626,12.39944382,11.89863203,13.36280544,12.13789777,13.1876567,12.53449553,13.39434202,12.21418371,12.26630477,12.45259647,13.03480605,11.94182431,12.97975095,11.55593903,13.29620941,12.77242325,13.22593671,12.48269392,13.10840289,13.31756237,12.87167942,12.67259335,12.28723179,12.92086726,11.99405391,12.75298058,13.3622605,12.83963687,13.48062114,13.20188867,11.87584334
-HLA-DRB1/3,12.25090476,11.79593192,11.82778807,12.02336925,13.65956995,13.29576512,11.37218762,11.69168768,13.8469891,12.22469709,12.37634442,12.32413118,10.57610225,11.44104664,12.0595762,11.31431192,11.86974344,11.38505836,11.13323434,10.86483194,11.75256434,11.63627869,11.96928782,10.34132744,11.16802198,10.3470423,12.27923165,9.921223836,12.61924142,10.33535336,11.9061078,11.92920955,13.17759023,11.02612044,12.02756077,12.61682058,11.13484909,12.53365955,12.03606121,13.28462885,11.20011681,12.12742296,12.16833286,12.55250819,11.64681287,11.57426925,11.63048977
 HLA-E,13.4736212,13.83841847,13.68815717,13.66508948,13.55207723,14.5281383,13.82005936,14.08319025,13.81150872,13.57154543,13.88683633,13.68108259,13.55809638,13.53032978,13.27665456,13.75200744,13.68408403,13.86273803,13.76751775,13.79252816,13.78917879,13.89507198,13.69031975,13.93619621,13.91357627,13.83527221,13.82813621,13.71148407,13.39900942,13.6832283,13.50161883,13.72250273,13.4250864,14.01534911,13.9769613,13.90447963,13.79029109,13.96320023,13.84611422,13.66100115,14.263845,13.62293861,13.51263945,13.61557756,13.23914515,13.69156999,13.85162206
 HMGB1,9.975111127,10.2885304,9.706343871,9.876622553,9.432064249,10.54066644,9.685584859,10.02701094,9.384324211,9.247369563,11.21982193,9.721831364,9.872003874,9.469206837,9.276444392,9.642382945,10.11596956,9.985718919,9.865214426,10.13106573,10.17325947,9.842307666,9.738990196,9.63098059,10.03191325,10.0510669,10.01749297,10.12951782,9.766710362,9.710109959,9.873656555,9.554401897,9.468175841,10.04587658,10.01126742,9.676499527,9.56227725,10.19525441,9.692833714,9.408225179,10.21017047,9.594656675,9.627229171,10.00537777,8.814400487,9.871883883,10.03779162
 HRAS,6.638461526,6.202557181,6.576283331,6.361839701,6.429249233,7.081234819,5.929944973,6.452855196,5.989366907,6.195457474,6.040233833,6.188268015,6.139699657,6.166974544,5.957875309,6.740230268,6.338361982,6.442543854,6.166235665,6.608644698,6.305363009,6.178428108,6.716622383,6.179439757,6.405061179,6.305502178,6.177619123,6.370433824,6.184613195,6.169035103,5.800854021,5.867476985,5.80338671,6.152349122,5.808578464,6.010948266,5.765344732,5.924400497,6.26214448,6.116153068,6.064092212,6.093620256,5.990384879,5.946484076,5.597441952,5.846348791,6.363703227
@@ -248,7 +235,6 @@ ID2,11.65341187,11.59427185,11.41726903,11.40267098,10.93704387,11.53781244,11.6
 IDO1,4.674987402,4.276557762,4.655717798,5.95794276,5.516712074,4.142635363,4.989651219,3.971728506,5.043814691,5.659404574,5.371439741,5.451302421,5.185503347,5.012251949,5.817012773,4.616847853,4.700932061,4.732050471,4.377739771,5.350846941,5.263542834,4.326950633,5.523977305,4.535583568,3.775704558,5.454365564,5.350950726,5.064625394,4.90777299,3.278264172,5.227118776,3.938560083,4.857834494,4.59776027,4.152532865,3.824535142,3.397973667,5.101278259,4.819626245,4.870396654,3.592786493,5.235639261,5.604202242,3.974498452,4.718748248,4.761459893,4.778740726
 IFIT1,6.397453427,6.636453707,6.492219066,6.690462449,7.014211734,9.049525959,7.57461372,6.059191348,7.171570238,8.037916197,6.130431642,6.351766748,7.190004739,7.377088635,7.153662374,6.364081782,6.660290076,7.045208356,5.962702271,6.381873836,7.198447805,6.745903182,7.623512978,6.20800891,7.322599018,7.139492226,6.114883368,5.752681388,7.161529582,6.169035103,7.131770899,5.777279176,8.392965839,6.255971753,5.794639273,6.450369924,6.331546305,6.015548385,5.926541448,6.960282157,5.777211064,8.402220819,7.879681415,7.081413656,7.190816692,6.389491116,4.735672004
 IFIT3,6.397453427,6.403090168,5.492219066,6.293126951,6.262139247,7.727597864,6.878229937,5.971728506,6.748358807,7.487223599,6.040233833,6.325771539,7.038500935,7.215068832,6.608426152,4.979417932,6.680754179,6.317012971,5.84904549,6.112687203,6.819936182,6.229653431,7.471509885,6.027436664,7.193557073,6.879972305,6.130824912,6.02398341,6.754227732,5.474661385,6.415563865,5.938560083,8.006828837,6.089613366,6.195601587,6.332876361,5.87305855,5.955427393,5.943614962,6.729798324,5.687943726,7.627956684,7.070112072,6.382583191,6.671442533,6.12645671,4.391717603
-IFNA4/7/10/17/21,4.938021808,5.013523357,5.371924832,2.257503042,3.236604155,4.879600958,3.480637572,2.708694101,2.288927189,5.244367075,4.609599478,2.544411826,3.033500253,3.371794336,2.991042173,3.979417932,2.852935154,4.442543854,3.240236247,5.075212498,6.176079992,4.326950633,5.038550478,3.34293849,3.682595154,3.589295144,2.944958367,4.142627906,3.90777299,4.600192267,3.501293739,3.045475287,2.80338671,2.405115192,1.915493668,2.136479148,1.561472399,3.570763543,3.782151539,3.741113637,4.133354874,2.820601762,3.469552716,3.274058734,1.764551937,3.176497393,3.150709504
 IFNA5,5.522984309,4.323863477,4.848362876,4.105499948,3.599174235,2.557672863,3.102125949,4.194120928,3.610855283,4.796908098,4.893392444,4.003843444,3.670930174,3.19122209,3.853538649,4.509932649,4.11596956,4.079973774,4.776289147,4.634639907,5.761042493,3.610743599,5.038550478,3.995015186,4.235136177,3.48237994,3.32346999,4.142627906,3.529261367,3.863226673,3.970779022,3.993007867,3.626508948,3.298199988,3.356066259,3.773909069,3.397973667,4.400838541,3.704149027,4.063041732,3.718317375,3.738139602,3.589846949,3.974498452,3.349514438,4.524420696,3.691277885
 IFNA6,6.710611312,6.240031886,5.706343871,2.520537447,5.014211734,4.142635363,3.239629472,4.634693519,3.288927189,7.314756403,6.01677486,4.003843444,3.033500253,3.454256496,4.175466744,4.394455431,4.53100706,5.009584446,3.918308152,6.555205439,7.442866533,5.890851518,5.038550478,5.393564563,4.476144277,3.869403063,4.32346999,4.538556583,3.666764891,5.759390862,4.501293739,2.56004846,3.424875086,4.121322226,2.693101246,2.136479148,1.561472399,4.924400497,3.441114621,4.200545255,4.455282969,4.351116478,3.405422378,3.274058734,4.012479451,4.109383197,4.391717603
 IFNG,6.053499026,5.893229123,6.340215973,6.924259634,5.073105423,5.727597864,5.909480871,8.044977488,5.043814691,5.952186323,5.577890619,5.866339921,5.605042238,6.039218997,6.086199406,6.201810353,7.629039142,5.009584446,3.61874787,5.634639907,7.10681733,5.262820295,5.523977305,6.418226617,5.530592061,6.627430273,7.281241754,5.982163234,5.962220774,4.693301672,5.158406025,6.24934762,5.897362858,5.352647772,5.130506559,4.721441649,4.509004979,4.488301383,6.354402988,4.13343106,5.04024547,7.563566096,5.338308182,5.666376157,4.51943944,5.331775618,4.214839841
@@ -316,15 +302,9 @@ JUN,6.482342324,8.61284115,6.09629039,6.427428043,6.287230228,7.464563458,8.6628
 JUNB,9.301426539,10.85197095,9.356157516,9.135247292,10.17240063,10.0654675,11.26954409,11.06360461,10.56853777,9.751104408,11.07472221,9.991495052,11.18747443,10.2651851,9.062992015,11.52373845,9.605148522,9.802700002,10.42709331,10.34692124,11.59709285,10.11627163,10.3239527,10.84846652,10.9698458,11.30795033,11.50796392,10.61836134,11.02351695,10.6911868,10.13065036,10.18794708,10.68555944,11.21660457,10.81117713,10.87906929,10.30870633,10.66528114,10.73512263,10.90287538,11.20615741,10.75658527,9.840050606,9.795659174,9.246351369,10.67634328,10.52337673
 JUND,10.38648231,11.37494082,10.49402131,10.28982433,10.61907379,11.53781244,11.24678419,11.94948543,10.94445791,10.51738557,11.11186919,10.83443067,10.80645625,10.57780314,10.3565693,11.30034381,10.78721575,10.59229097,11.42411971,10.80742456,11.76573114,10.71552953,11.03855048,11.00928932,11.4000431,11.43433708,11.67355918,10.95201527,11.15861799,11.47670921,10.6170377,10.87111556,11.3541335,11.23936049,11.01653137,10.96302764,11.05189354,11.00168066,11.70883767,11.47624652,11.53955988,10.66957238,10.50775602,10.55018314,10.15041434,11.35278389,11.14557718
 KDM3A,8.01127079,8.509218519,8.291306372,8.070001267,8.043959077,8.440315912,7.965073197,8.74431801,7.925551809,8.105660804,9.341066092,8.370960313,8.137370851,7.977818452,8.157319398,8.057420444,8.923324482,8.633100188,9.125322472,8.615187544,8.745935601,8.330865683,8.108939805,8.552391855,8.602253046,8.550124547,8.455920286,8.325152945,8.351991515,8.732220661,8.658835016,8.84174671,8.667096482,8.935382172,8.700595783,8.511518579,8.668502609,8.434261543,9.071442426,8.732635483,8.759063717,8.290921696,8.346870196,8.670663516,8.341980765,9.339117593,8.846095994
-KIR2DL1/2,7.259949903,6.124554669,5.755253472,5.979969066,4.751177328,4.142635363,6.734394164,6.244747001,4.610855283,5.381870599,5.700747366,5.498608136,5.167356,4.743763113,4.957875309,4.509932649,3.852935154,6.195450992,4.673195654,5.997209986,4.87651971,5.262820295,6.523977305,5.820985786,3.8631674,6.961849312,6.553767609,6.676060107,6.284148869,6.791333755,6.140704024,6.226805052,4.312400357,5.379119984,5.216663202,6.043369744,6.111669482,5.793155964,6.119186526,5.441553355,5.04024547,5.972604855,4.560700604,5.361521576,4.288113893,5.143330529,4.900731251
-KIR2DL3/4,9.532968398,8.077026299,8.161245832,8.723477506,6.714651452,7.949990286,8.214826081,8.329280511,8.231441694,8.599571325,8.561770953,8.305963058,7.414322037,6.606259589,8.395796699,7.094895149,7.022860156,7.618393689,6.776289147,8.88256742,8.442866533,7.564939909,7.804085224,7.222084095,7.004523249,8.492746656,8.347544125,8.891879874,7.810959617,8.077005964,9.167176235,8.597869925,7.735942189,8.93091562,8.818945179,8.158846961,7.89877572,8.81446647,8.192435508,7.341026479,7.499677088,8.358035892,7.372255514,8.498060409,7.622532933,8.052456129,7.238172345
-KIR2DL5A/B,4.674987402,3.276557762,4.018287878,3.935574947,3.429249233,2.557672863,3.239629472,5.846197624,1.873889689,3.796908098,4.545469141,3.866339921,4.355428348,3.19122209,2.991042173,4.268924549,3.990438678,4.14708797,3.240236247,5.871679104,6.130276303,4.890851518,6.038550478,3.120546068,2.583059481,6.35179583,3.08246189,5.939094512,4.416786638,6.461485996,7.201733457,1.56004846,2.102946992,3.405115192,2.693101246,4.694474601,2.561472399,3.985801042,6.556591839,3.441553355,3.133354874,4.557567356,2.94599076,4.443983736,3.671442533,3.038993869,3.498632807
-KIR2DS1/4,7.461583764,7.157569726,5.655717798,5.636014665,4.516712074,3.557672863,7.295897692,7.087205724,2.610855283,4.037916197,6.063317446,5.90196383,5.492931872,5.091686417,3.923927977,4.394455431,3.53100706,6.618393689,5.047591169,6.286716603,5.424007506,5.229653431,7.360478572,6.764402258,2.775704558,7.976697076,7.185272696,7.695600293,7.22414156,7.715669485,6.909378478,6.745915005,3.190409833,4.504650866,4.808578464,6.920750457,6.992522216,4.307729137,6.819626245,6.044894385,6.262637891,6.759201217,5.249771508,5.888768578,4.464991656,4.524420696,4.976680104
-KIR3DL1/2,8.7367636,7.403090168,7.433325377,7.710362006,6.336139829,7.879600958,7.76888254,8.420189007,7.083343055,7.712515911,7.130431642,7.299299328,7.351415286,6.215068832,7.116573055,6.979417932,7.16036368,8.265085179,6.872504462,8.709928034,8.034060987,7.661369672,6.886547384,7.725408126,6.267557655,8.834508273,7.499547218,8.900324942,8.059082313,8.410978095,9.116003583,8.159961302,7.027759495,8.121322226,8.250245803,7.238017174,7.171969992,7.985801042,8.584072575,7.216847068,6.940709796,8.138516794,6.53095326,7.318452854,6.78691975,7.256224585,6.167011316
 KLF2,11.92291492,11.8900403,12.10102831,12.00132765,11.5312249,12.78649155,11.93057982,12.46591741,11.95292995,11.94526912,11.8653465,11.7780315,11.42986718,11.51375118,11.82852676,12.08561734,12.02686209,12.19718396,11.2879055,12.07045897,12.23610035,12.31093558,11.60536563,12.04486374,12.31752718,11.69632535,11.97175843,11.98679681,11.82525983,12.01410222,11.81278009,11.99467669,11.43722028,12.59599809,12.00806206,12.10466592,11.72691781,11.78454283,11.57827656,11.10524829,12.72511095,12.14802965,11.93378769,12.12931656,11.34339633,11.9102831,12.63104073
 KLRB1,12.04741719,11.39895439,11.14642544,11.06807468,10.54819031,11.53781244,12.05224438,11.89703568,11.0702869,12.64439259,12.62882758,10.59153574,11.29794285,10.70060124,11.0063305,12.10066363,11.50828698,12.22244556,12.14742737,11.98841687,11.19288828,11.76845143,11.27886481,11.62966125,11.24178102,11.22384693,11.12337424,11.99667606,10.90560385,12.6494963,11.87833738,11.85834019,11.04729303,12.07450647,11.98042886,11.66829379,10.65573864,11.64639125,10.03207586,10.16002037,11.20280464,11.07903693,11.7426388,11.74674957,11.59229159,11.67887718,11.49742469
 KLRC1,9.665942263,7.61759468,7.09629039,7.768464961,6.999104841,7.312560365,7.554984913,7.829709502,7.397451645,8.461597791,8.452359736,7.928116118,8.82791612,9.132954435,7.966238782,7.149342933,7.769411799,8.60353573,7.93511644,8.397140593,7.220474112,8.311183317,7.716622383,7.329999434,7.557064272,8.117330577,8.298987305,8.490890149,7.021114463,9.171059938,8.995610875,7.955796788,7.125314805,8.606188892,8.155262658,6.873444742,6.883400494,8.12193773,8.508638615,7.807202827,7.333027219,7.42546382,7.965581488,7.631610739,8.686392874,8.385950758,7.498632807
-KLRC2/3,8.542883866,7.137851492,7.562608394,9.478606767,5.599174235,7.017104481,5.710935191,6.495290462,7.365742786,6.92243898,8.371439741,7.285878812,8.020922187,8.874918544,8.89574521,6.667473926,6.870857062,6.582474115,6.813125915,7.471141174,8.683874633,7.287204454,7.240184339,9.498479573,6.80912756,10.02630406,9.040882787,3.702055315,6.6419615,8.588119435,2.970779022,8.613159796,7.0038138,4.405115192,8.32002704,5.505712958,5.323973085,7.412065797,8.318858871,6.817929234,6.418757093,7.228105588,6.707541992,9.58694169,8.169693401,9.023127463,6.757367076
 KLRD1,10.80220795,10.23075407,10.56946206,10.90639985,9.287230228,10.20872455,10.57866965,11.15908058,9.65743365,10.03964916,10.5768884,10.08163023,10.51776373,10.52869533,10.1071498,9.740230268,10.05456902,10.35919742,9.661459546,10.16935965,10.30278447,9.915828183,9.240184339,10.45146295,10.2795303,11.18441939,11.30996844,11.56136823,9.875331808,10.63947691,10.48209243,10.71284031,8.475812052,10.32823041,10.87508169,9.963027635,9.064730259,10.55312338,10.47673853,9.750942254,9.818103494,10.81872203,10.09510275,10.14442345,9.395728993,10.289766,9.001890667
 KLRG1,10.25090476,10.32313613,10.44641538,10.42240997,9.468168222,10.44641611,10.12671159,10.49659666,8.527331928,10.93180108,10.38981827,9.668533138,10.57653207,10.04005365,10.34539175,10.09951178,10.0388017,10.98629176,9.346668325,10.34101258,10.73832242,9.829912119,9.8459054,10.72417241,9.587881864,11.10274077,10.52916927,11.65458858,9.748429887,11.24645095,10.39983699,11.09571265,9.876086198,9.845687783,10.09184494,9.655678551,9.503986904,10.14398994,9.970935568,9.585034688,9.760888758,11.31956174,10.31613324,10.12736829,9.392085822,10.29826636,9.662821439
 KLRK1,12.10543995,11.39860921,11.59990694,12.09260602,10.43065743,11.02119724,11.49873073,11.86199278,10.7120431,11.52976929,11.75359263,11.83558253,11.65372008,11.672887,11.8439263,11.30134603,11.08559591,11.99798511,10.70376062,11.29387132,11.65687019,11.91843233,11.2596542,11.74198748,11.26655544,12.09907015,12.08987148,11.94420864,10.73676383,12.16370635,11.46441213,12.12229088,10.88717285,11.36553092,11.81614487,11.1086612,10.92997886,11.10127826,11.34640133,10.37773826,11.19405081,12.13927009,11.59861169,11.72114196,11.19970128,11.75838269,10.89462589
@@ -499,7 +479,6 @@ RHEB,10.00769534,9.968650138,9.853987425,9.931342098,9.991491657,11.11609358,10.
 RICTOR,9.307255618,9.249250416,9.077181567,9.463051953,8.55853225,8.707419982,8.466698381,8.854371556,8.527331928,9.502383406,9.333965036,9.351766748,9.13503828,9.264471072,9.322773443,9.085617336,9.807131465,9.457494195,10.57204893,8.925317067,9.209502994,9.317116272,9.316535225,9.186635259,8.744371352,8.99868608,9.070113498,9.203039423,8.948800258,9.463139515,9.432031077,9.453653365,9.228360462,9.834970231,9.768134638,9.504743079,9.993119235,9.115084059,8.570397638,8.870396654,9.280559799,9.066418529,9.503454461,9.458934077,9.934476939,9.883228857,8.858696031
 ROCK1,10.64538094,10.80689907,10.71564162,10.79044333,10.4193532,11.00888397,10.12116062,10.38253316,10.27192076,11.08650786,11.58388936,10.96962773,10.9423932,11.12077641,10.88310196,10.53059212,11.34879018,10.8700507,11.80819232,10.80456491,10.97049586,10.87216695,10.79343798,10.88762699,10.36441919,10.92978112,10.96454909,10.92743492,11.02870866,10.92752035,11.58426156,11.32955575,11.56800316,10.944274,10.96417515,10.82259055,11.00056119,10.26502469,10.81904813,10.54363,10.52995966,11.02190889,11.00601111,11.12814791,11.52651863,11.79388137,10.90073125
 RORA,8.907648159,9.047386808,8.848362876,8.875154161,7.872192729,7.312560365,8.133344679,9.174668565,8.050478421,9.144831401,9.545469141,8.806506671,8.615119311,8.323250484,8.701535556,8.769494863,9.085595911,9.568770312,9.613536444,9.022041839,9.305363009,8.917130843,8.825146839,9.67257468,8.764389245,9.767782159,9.656453273,9.50756889,8.872669189,9.538791723,9.029672711,9.637932324,8.755791965,9.702359024,9.437935084,9.300827432,8.908935586,9.019224044,9.055824465,8.844401445,9.369369066,9.531790721,8.648163445,9.084630369,8.90154305,9.766914323,9.164983635
-RORgt,7.796002803,6.056167694,7.534863403,6.690462449,6.888680852,7.602066982,6.675015617,7.087205724,6.681244611,6.56629517,7.758462864,6.216837168,5.657991118,5.942542977,6.286498057,7.667473926,6.618469901,6.618393689,6.293347583,7.76979949,7.305363009,5.911913133,8.716622383,7.090172419,6.925451678,6.381853064,6.114883368,7.243428547,5.92157879,6.941229185,6.029672711,5.260488178,5.169036182,6.352647772,5.647297557,5.392818901,5.039519696,7.445232661,6.464961363,6.648004232,6.847600392,6.220532369,6.203378602,6.79762069,4.808946057,5.783154965,6.320634505
 RRAGC,8.16041423,8.419515716,8.433325377,8.270965301,8.489945165,8.843075082,8.722712359,8.58152286,8.804627027,8.668393357,8.585883409,8.370960313,8.503820188,8.454256496,8.252709743,8.48897303,8.292558292,8.40875554,8.766931092,8.608644698,8.568397415,8.392103312,8.331332227,8.478098073,8.271559585,8.305502178,8.295455614,8.402495033,8.321075438,8.244048457,8.649476961,8.610985425,8.861170206,8.430650284,8.407346764,8.515376055,8.449345768,8.246328592,8.192435508,8.228953671,8.122039561,8.337177287,8.714175084,8.488377855,8.622532933,8.825590231,8.384764843
 RRAS,6.482342324,6.984377011,7.170290971,6.889771257,7.058605853,9.017104481,6.88872231,6.21965602,6.610855283,6.56629517,6.80028304,6.544411826,6.811942484,7.479088349,6.54283781,6.979417932,6.639531516,6.345867834,6.185094693,6.184836989,6.385533358,6.373244285,7.038550478,6.910622999,6.621194609,6.24137184,7.065973768,6.464556001,6.474119813,5.693301672,6.122782116,6.357061438,6.796433949,5.955312275,6.491843038,6.867420955,7.293276288,7.073263883,6.791611868,7.310969245,6.241879331,6.945297509,7.070112072,6.521986248,6.15686936,6.192799205,5.363703227
 RSAD2,7.375427121,7.34694709,7.152143624,7.18350246,7.029162075,9.500187368,7.535085356,6.653552546,7.171570238,8.343101028,6.944018517,6.866339921,7.891481249,7.815712955,7.537235104,6.201810353,7.586289495,7.801398517,6.84904549,6.914747826,7.442866533,7.667574821,7.038550478,6.802370108,7.868461699,7.966815565,7.146592228,7.225617271,7.426501792,7.255544096,7.292707117,6.952365883,9.128086554,6.946488424,6.700595783,7.185722667,6.803966026,6.423206354,6.545451281,6.890860756,6.940709796,8.986067115,7.982164372,7.307481736,7.730336222,7.444986229,6.645474196
@@ -520,7 +499,6 @@ SIGLEC5,6.907648159,6.252310216,6.402951728,6.164393637,6.999104841,8.172382707,
 SIGLEC6,5.812490926,6.358351854,5.371924832,6.805939666,5.516712074,8.258112581,6.30375981,6.779083428,6.070286902,6.381870599,6.235784642,6.566779639,6.709065303,6.659370926,5.923927977,5.564380433,5.778934573,6.163389782,6.047591169,5.781481295,7.058723042,6.686031726,6.623512978,5.961848322,6.097632653,5.848029412,5.114883368,6.595140111,5.416786638,6.247890523,5.292707117,6.058299327,5.789447519,6.24161646,6.027001983,5.694474601,5.277679433,6.015548385,6.248469543,5.870396654,5.525672297,6.110108379,7.120916442,6.081413656,6.753236624,6.498425488,5.622015223
 SLAMF6,10.69290931,10.26145087,10.76274801,11.04866593,9.868041086,11.52056887,10.63428703,11.02610671,9.989366907,10.75743666,10.77865302,10.10856132,10.63671446,10.52213897,10.59500564,10.67213029,11.04769201,10.83676834,9.985742513,10.58883622,10.85203703,10.72448576,9.599265432,10.85863833,10.57174417,10.65384546,10.5611396,11.04381238,10.40332057,11.06671063,10.73601907,11.24163828,10.54069906,10.51513235,9.995359361,10.48864522,9.842905716,9.955427393,9.564891162,9.063041732,10.25229595,11.0842622,10.77888077,10.76290308,10.34951444,10.88886806,10.22655326
 SLC2A12,3.938021808,4.369667167,4.655717798,2.935574947,3.236604155,5.365027785,2.587552776,2.386766006,3.748358807,3.796908098,4.729893712,3.544411826,3.387137208,3.606259589,2.701535556,2.809492931,3.53100706,2.272618852,3.725663074,3.827284985,5.87651971,3.890851518,5.301584883,4.120546068,2.775704558,2.589295144,3.430385194,3.064625394,2.377258273,2.693301672,3.888316862,2.238120365,3.626508948,3.057191889,3.015029341,3.288482241,3.076045572,3.722766636,2.704149027,3.063041732,3.303279875,3.820601762,3.53095326,2.859021235,3.464991656,2.886990775,2.691277885
-SLC2A3/14,10.09789315,11.10775943,10.60825106,10.23134388,9.935060787,10.3192241,10.32516229,11.02429656,10.67263148,10.7928037,10.98841264,11.27488196,11.21920165,10.9031758,10.20491963,10.74613087,10.29587865,10.81758328,11.58807904,10.83571361,11.50363264,10.72597308,10.94544107,11.06793949,10.73643055,11.42849893,11.49608982,10.65292361,10.9458478,10.88682703,10.60740364,10.83431012,10.87361088,11.74750739,11.61221286,11.25628489,11.82742226,10.99048969,10.40766814,10.5216534,11.34888787,10.8016933,10.38322266,10.62610216,11.37234458,11.01283471,10.66210279
 SLC2A6,7.762450244,7.881419821,7.767226113,7.579431137,8.051301053,8.390562877,7.535085356,6.862499437,7.796721829,7.650359435,8.215320539,8.144324668,7.456032289,7.710596249,7.716485897,7.167044935,7.388988055,7.079973774,8.244737639,7.709928034,7.424007506,7.538098297,8.623512978,7.303767892,7.383034872,7.174257645,7.741971344,7.56003631,7.851189462,6.407547189,7.760855953,8.132938128,8.670903067,7.048971382,7.77702373,7.646034246,7.542020036,8.101278259,9.382846966,9.100863197,7.133354874,7.337177287,7.189164743,7.674938171,8.207495433,8.267812559,6.96740229
 SLC7A5,7.053499026,7.783295096,7.205914881,7.774778735,6.999104841,8.415653858,7.102125949,7.75308822,7.344209624,7.131892346,7.992928118,7.577834827,7.448537753,7.484003839,7.138940868,7.831860744,7.100862668,7.62429429,9.512632756,7.094071525,8.274112075,8.022557197,8.389047725,7.512863491,8.128851384,8.34799426,8.330389404,7.960789584,7.149847777,7.926921348,7.131770899,7.685203591,7.130852988,9.03030217,9.328517073,8.188141268,7.841580318,9.366622826,8.683971145,7.496001139,8.427975623,8.130457024,7.160309881,7.296426547,7.671442533,7.945884465,7.905597006
 SLC8A1,8.770911822,9.419515716,9.399109662,9.049317113,9.892773606,10.5520263,9.17036681,7.986678848,10.03376103,9.5517956,9.67287731,10.26551101,9.798740302,10.4882912,10.04138556,8.317287571,9.338361982,7.721079353,10.42907232,9.193607199,9.130276303,9.053687094,9.611440146,9.427367271,8.318736379,9.305502178,9.763860042,9.149728963,9.843957892,7.892974016,10.05470151,9.69447478,10.57766694,8.799884094,9.61097055,9.920750457,9.711945038,9.125352394,9.938301162,10.25582769,7.499677088,9.69507088,9.865598998,9.677070758,10.85400242,9.81114346,8.1629531
@@ -584,11 +562,9 @@ TOX,8.375427121,7.83319154,8.249242312,8.349202876,7.129688951,5.879600958,7.613
 TOX2,5.053499026,3.276557762,3.170290971,4.257503042,3.429249233,2.557672863,3.239629472,3.708694101,3.748358807,3.659404574,3.893392444,3.351766748,4.073028618,3.454256496,3.338965477,4.268924549,3.990438678,4.009584446,2.918308152,3.827284985,4.761042493,3.262820295,4.716622383,2.120546068,3.94562956,3.951865223,3.529920867,3.064625394,3.014688194,4.216863628,1.800854021,2.56004846,3.102946992,4.24161646,2.108138746,2.773909069,2.076045572,4.570763543,3.534224026,2.741113637,4.718317375,4.042994183,3.405422378,3.081413656,2.349514438,3.302028275,3.598168481
 TOX4,9.375427121,9.648116625,9.395257336,9.330037641,9.146925656,10.0654675,9.433042827,9.596219371,9.407868261,9.729793902,9.648279946,9.420928772,9.404671867,9.366480536,9.193388652,9.59739549,9.660290076,9.528434275,9.516360652,9.691471129,9.593932507,9.77493223,8.886547384,9.649325734,9.489950076,9.449051764,9.463808195,9.360266798,9.414347592,9.67772013,9.351600806,9.489602776,9.603788871,9.519601207,9.47446096,9.604084698,9.484587614,9.076795574,9.577251309,9.307167675,9.475429542,9.554558606,9.578077173,9.443983736,9.494172681,9.493498806,9.894625888
 TPP2,10.68621466,10.82345222,10.5830725,10.75415712,10.02357384,10.84307508,9.880860193,10.52503781,9.920104244,10.76164277,11.16818656,10.80415509,10.86814687,10.60625959,10.75621524,10.59576256,11.15305888,11.12852552,11.28134778,10.15820186,11.02470293,11.01831486,10.58080853,10.83936432,10.68409718,11.04696916,11.06907968,10.86120584,10.9146924,11.15835784,10.93142458,11.17439022,10.98367843,11.34972268,11.00522787,10.79270536,10.89526668,10.14061915,10.78807151,10.542822,11.08245203,10.90038282,10.87709759,11.01130608,11.05457078,11.40662703,10.8301896
-TPSAB1/B2,3.353059308,4.727219171,4.018287878,4.459136903,3.751177328,2.557672863,4.239629472,3.708694101,5.873889689,4.337476479,4.545469141,3.129374326,3.721556247,3.284331495,4.779538068,4.896955772,3.700932061,3.38809607,4.725663074,3.412247485,4.983434914,5.326950633,6.176054001,2.535583568,4.235136177,5.589295144,4.32346999,4.595140111,3.529261367,3.600192267,6.324415977,4.09610136,3.80338671,5.66384946,3.108138746,1.551516647,5.64893524,5.610291907,3.119186526,3.98904115,2.940709796,4.235639261,4.618416102,5.361521576,2.764551937,3.717065774,4.01320598
 TRAC,12.581878,12.71936306,12.82206024,12.54452276,12.47977814,13.3682445,12.76212184,13.48166842,12.61170037,12.78997514,12.65541848,11.72680618,11.87217895,11.68787617,12.15274667,13.19751022,13.15099772,13.10168086,11.647079,12.73003693,12.58706126,12.75685764,11.85872944,12.80792175,12.90932845,12.21134696,12.23382444,12.36942034,12.04318631,12.85522084,12.24094455,12.68300584,11.89248064,13.09511316,12.22561952,12.5105526,12.45650664,12.42667021,11.69693004,11.76867412,13.39265679,12.51292666,12.40683057,12.84746924,11.14122,12.0638565,13.11754264
 TRAF2,7.762450244,7.51432541,7.324096307,7.395006565,7.11574976,8.534952786,7.155237285,7.516049023,6.796721829,7.466759496,7.225588875,6.673694843,7.16278327,6.984771213,7.338965477,7.603908797,7.870857062,7.764471948,6.699667866,7.568751971,7.424007506,7.544855663,7.417062101,7.524268254,7.483063691,7.08572097,7.295455614,7.552911876,7.021114463,7.807043838,7.113736976,6.999671597,6.710277305,7.136919081,6.984655692,7.027250078,6.943942036,7.319701779,7.435090834,7.124817929,7.551207389,7.1584714,7.272420247,7.262743421,6.571906859,6.836006847,7.479180445
 TRAT1,11.16523661,11.14568888,11.21140251,10.84321675,10.80428866,12.22300878,10.76532823,11.59682849,10.74938308,11.70434507,11.16218505,10.9063556,10.69290159,10.38827492,10.95262341,11.26173417,12.12019003,11.47637468,11.30039174,11.4493368,10.96071484,11.62516266,10.5495124,11.4499687,11.22433713,10.52677406,11.00186698,10.95066063,10.8086398,11.6608882,11.6435972,11.31192721,10.78242709,12.12009659,10.95166728,11.32273098,11.63255163,10.40926716,9.30240835,9.993779069,11.68890265,10.88788672,11.2212423,11.56522943,10.9727863,11.10884638,11.40723988
-TRBC1/2,13.93966457,13.56326953,14.01201255,13.9470643,13.49987336,15.35411812,14.10311183,14.89975332,13.67317131,13.8918255,14.05991423,13.15681962,13.15891372,12.75562567,13.11163352,14.59228643,14.3149261,14.31444909,12.93546454,14.14087664,14.02802419,14.20193198,13.25481131,14.11984146,14.09904085,13.53002822,13.71446566,13.26511536,12.84327807,14.07720842,13.54232101,13.7117627,12.9536222,14.39248073,13.42823413,13.58568512,13.81603709,13.65601468,12.27716498,12.20361591,14.45309755,13.82505634,13.72944946,14.10209602,12.75571944,13.24869091,14.24646614
 TRDC,11.71390639,11.19581838,10.67649936,11.27987085,10.67598983,10.80560038,11.87757162,11.7437681,10.367078,10.95034498,11.12427066,11.5730086,11.67072889,11.60259184,11.03791814,10.9503227,10.76702125,11.83803832,9.702942998,10.91607322,10.22592854,11.50209334,10.1344749,10.13735436,10.39317202,12.07426185,11.1048297,11.96482134,10.39034827,11.722934,11.28366198,11.08596072,9.38834921,11.26930134,10.58488495,10.67261841,9.8121092,11.42181848,10.52928449,10.0391762,10.07733479,10.86984528,10.78755511,11.00282958,10.48222836,10.99202625,10.02219476
 TRDV1,8.121243632,6.820878279,7.133765095,9.91285487,7.101674575,8.983937617,8.060968624,9.364045929,6.828086,7.729793902,7.074722209,8.067973782,6.896998254,8.28714651,7.686428664,7.831860744,7.425824823,8.187502238,6.604808679,7.303018415,7.442866533,7.648878727,7.761016502,7.29047107,7.033092401,9.555079429,7.185272696,8.370433824,6.161529582,7.888058526,6.877669618,9.094545894,7.068731276,8.182722771,6.615933386,7.021836582,6.708313787,7.319701779,7.814994796,6.116153068,7.517059166,7.53331981,7.694183609,8.696123501,7.031338478,8.736656502,7.398637017
 TRDV2,7.235702357,8.378095789,5.706343871,7.872212886,8.163958853,7.017104481,9.728565085,8.25095215,4.932783378,5.585403993,7.758462864,8.332314385,9.048450595,8.951443037,7.131523397,7.786772854,6.888559064,8.867565441,4.166235665,5.914747826,6.568397415,8.757584987,7.716622383,6.579977687,7.798072371,9.106120768,5.710493113,8.113868914,4.851189462,7.600192267,6.210244957,7.871115562,5.232230009,8.417939232,7.032951249,7.301944501,5.973285997,9.690857388,7.551032313,7.434600594,6.381282387,7.034381053,6.840808523,6.577839483,7.825247869,5.825590231,7.592144693
@@ -597,7 +573,6 @@ TREM2,4.522984309,3.893229123,3.655717798,3.842465542,3.236604155,2.557672863,2.
 TRGC1,8.45109139,7.48860824,8.853987425,9.779495998,7.495338424,7.201529053,8.94006919,7.717682884,6.989366907,8.365382476,9.054704315,8.704283162,7.943060036,9.551118035,9.168235175,7.69213598,7.720831618,8.573352725,8.666501002,7.793069269,7.533631997,8.258715897,8.761016502,8.524268254,7.873736641,8.79874851,7.787937198,9.069626076,7.235239269,8.88312623,8.062948866,9.454866223,7.994730695,9.670562805,8.168834677,7.734738471,7.285498938,9.298683997,8.279057863,7.752340892,7.649054712,8.402220819,8.340453454,8.866515772,8.606902281,8.854056886,7.398637017
 TRGC2,10.06730483,9.59246207,10.38363825,10.2140244,9.146925656,10.71249097,10.4508541,11.58950205,9.463852871,10.43155416,10.2496872,10.28924566,9.36839424,9.392855951,8.812381325,10.24203483,9.299191384,10.16541462,9.194432557,8.202324416,10.10681733,9.5210651,9.038550478,10.30542141,9.841793749,11.47586271,10.28124175,11.09927154,9.328866789,10.72810063,9.102350216,10.95622508,9.303845597,10.06841914,10.313932,9.19417009,9.083913815,10.5106692,8.057785982,9.069788564,9.335478698,10.88489544,10.65765773,10.43770841,8.830641128,10.07461778,8.272478467
 TRGV2,7.982415928,7.853482944,9.179279754,9.798212304,7.516712074,9.967063799,8.622548197,8.695105036,6.681244611,8.058575668,8.141319958,8.209747743,6.76444206,7.408452806,7.686428664,8.122375886,5.923324482,7.98686437,7.449689613,6.527724703,8.385533358,7.365631101,8.176054001,9.339714589,7.920362801,10.51834215,7.222943114,9.343849038,7.102151035,8.289491428,6.719717258,9.727968325,7.676594179,7.695792353,9.127729474,7.223941989,6.339079978,8.535264861,5.926541448,7.341026479,7.881547724,7.189835571,8.540315364,8.793694987,6.647194987,8.478617006,6.622015223
-TRGV3/5,8.899953767,8.141156636,8.307794495,9.747752252,7.462672235,7.511869173,8.133344679,9.389581021,7.748358807,8.320470054,8.91258601,8.37730184,7.724662158,8.270173432,8.109039383,6.509932649,8.272474046,8.816939368,7.137476672,7.094071525,8.699641948,6.178428108,7.8459054,8.105439176,7.42675625,9.787564771,9.204230854,9.610662383,7.402090126,8.726724673,6.755050331,8.59567237,7.2062348,7.121322226,9.050653251,5.920750457,5.587007491,8.258819536,6.416867075,7.875539957,8.116348449,8.70052531,8.651623147,8.4835121,7.539338997,8.644843736,7.517826372
 TRGV4,5.440522149,5.042092509,5.802559187,4.18350246,4.236604155,4.879600958,3.102125949,5.81303076,3.873889689,4.507401481,5.443589527,4.544411826,3.670930174,3.284331495,4.232050273,3.616847853,5.285894562,5.211218307,2.240236247,4.997209986,6.220474112,3.710279272,4.301584883,5.535583568,4.633685554,6.048726763,5.962880275,5.960789584,4.161529582,5.278264172,3.800854021,4.192316675,3.626508948,3.057191889,4.108138746,1.551516647,2.213549096,4.570763543,4.178080215,4.441553355,4.940709796,4.65067676,4.589846949,5.733490353,3.764551937,4.302028275,4.391717603
 TRGV8,8.283796645,7.691595262,8.115149417,8.435420834,6.55853225,8.172382707,7.710935191,8.556691007,6.498380554,8.466759496,7.77952448,7.37730184,7.092393943,7.827363315,7.613759596,7.550959917,6.437897655,7.959119379,6.763798203,6.03673835,6.790789836,7.204268113,7.301584883,8.270293188,7.176583995,7.956865904,7.266886461,8.676060107,6.6419615,8.319010515,7.08625624,8.546459395,6.6728026,7.592113707,7.206170829,6.909068652,6.509004979,8.195254408,6.865140904,7.232966733,7.110634797,9.095691606,7.762974383,8.061961294,6.532736262,7.896974864,6.066317316
 TRGV9,5.440522149,4.369667167,4.655717798,4.32789237,3.236604155,6.017104481,4.424054043,4.293656601,2.873889689,5.42493932,4.729893712,5.188268015,4.721556247,4.371794336,4.485806865,4.131421025,3.700932061,5.009584446,4.673195654,3.634639907,4.983434914,3.973313678,4.301584883,4.34293849,4.419560748,4.951865223,3.870957785,5.320965148,3.114223868,5.185154768,4.324415977,4.367403382,3.746803181,4.883162489,3.195601587,3.666993865,3.561472399,5.400838541,4.178080215,4.599094632,3.303279875,3.738139602,4.646430478,4.361521576,4.934476939,4.038993869,4.214839841
@@ -625,7 +600,6 @@ VSIR,9.69290931,9.668875185,9.817029669,9.514890884,10.09636078,10.23715296,9.82
 VTCN1,5.259949903,4.539592168,4.307794495,4.742929869,4.599174235,5.142635363,3.687088449,4.556691007,3.748358807,4.42493932,4.407965617,4.544411826,3.951038093,3.928187684,3.43850115,4.716383526,4.11596956,4.009584446,3.61874787,5.075212498,4.983434914,3.973313678,4.301584883,3.705508569,3.94562956,4.103868317,4.015347694,4.216628488,4.065314267,3.600192267,4.122782116,3.761682321,3.626508948,4.057191889,3.356066259,3.666993865,3.509004979,4.208193463,3.621686867,3.063041732,3.592786493,3.898604274,4.115915761,3.595986829,3.223983556,4.176497393,4.150709504
 WNT11,4.674987402,4.177022089,4.433325377,3.636014665,3.751177328,5.365027785,4.102125949,4.708694101,3.288927189,2.92243898,5.130431642,4.244851544,3.670930174,4.14231249,2.853538649,4.509932649,3.852935154,3.935583865,5.275860157,3.149213079,4.87651971,3.973313678,5.523977305,3.995015186,4.023632072,4.869403063,4.015347694,4.801590989,4.014688194,4.902755037,2.970779022,3.325583206,3.626508948,5.024025025,4.500456168,3.721441649,4.213549096,4.208193463,3.926541448,4.13343106,5.04024547,4.110108379,2.94599076,3.081413656,4.408408127,3.524420696,4.446165387
 XAF1,8.753938744,8.013523357,8.240680299,7.996270878,8.230149133,9.4765361,8.106627341,7.986678848,7.565051594,8.446000936,8.250944449,8.067973782,8.055868067,8.358472957,8.456423058,7.752007436,8.238366192,8.151180725,6.973590588,8.210989277,8.730668844,8.102596695,8.389047725,8.249829085,8.89204852,8.617990577,8.006734564,8.42997577,8.049683615,7.912470192,8.201733457,7.647511301,8.422619113,7.586444957,7.360804178,7.494031153,6.982936167,7.775877973,7.913602393,8.352138434,7.985103915,8.872263882,7.888505265,8.106948749,7.326794362,7.635929011,7.505059076
-XCL1/2,9.397453427,8.615219873,9.138381723,8.64638138,7.667653972,9.230098205,8.684079699,8.490053814,8.207790426,9.268213817,8.682227278,7.544411826,8.077894373,8.578952243,8.595005636,7.95924005,8.547815347,8.167436615,7.48816376,8.666488773,8.130276303,8.357977528,7.574603378,8.059145524,7.752984482,8.916366542,8.387901862,8.22112988,7.851189462,9.10481266,8.872316383,8.397991702,6.916728183,8.298199988,8.567570364,8.297471025,6.836266518,7.892691638,8.174468962,7.455359154,7.609088305,8.493027104,8.222115165,7.866515772,7.718748248,7.176497393,7.573920934
 XIAP,9.779324062,9.717288535,9.593196714,9.810556294,9.392145238,8.879600958,9.176802635,9.620385682,9.260470742,10.16402497,9.988412637,9.642443909,9.867093164,9.761077698,10.075609,9.55768578,10.33028868,9.947399615,10.22436984,9.513785512,9.606532544,9.682971926,9.208475479,9.68786841,9.397756378,9.473683741,9.340706695,9.84744164,9.748429887,9.938854378,10.17806455,9.865654249,9.920730113,9.785112343,9.478826152,9.751188992,9.673725716,8.843782037,9.111652854,9.226942945,9.52352383,9.76572041,10.1202923,9.899767577,10.04764029,10.01283471,9.386506177
 YES1,8.728098739,8.839308984,8.324096307,8.188240379,7.742132188,6.017104481,7.619401642,8.468915047,7.689806625,8.880508299,9.467580473,8.370960313,8.588089105,8.633059649,8.731282899,7.907525013,8.575401179,8.884873044,9.808572429,8.568751971,8.812704613,8.930093149,8.738990196,8.283776417,8.318736379,9.117330577,9.19288588,9.079575736,8.831214762,9.169035103,9.034473698,9.053503661,9.07017325,9.134978671,9.524936273,8.818303188,8.721343736,8.550585661,9.65705202,8.790962186,8.969405229,8.91511936,8.431820068,8.62720556,9.51943944,9.646324183,8.910446406
 YWHAZ,12.55457065,12.6962371,12.55572201,12.57898022,12.29768599,12.94891645,12.7814301,13.03197409,12.75372817,12.68786322,12.84384419,12.77202777,12.51608415,12.52705903,12.40908642,12.62687636,12.69685621,12.60893944,13.01123043,12.51027957,12.91248192,12.61013967,12.62050423,12.62867096,12.54450917,12.75024702,12.77118904,12.57352057,12.43027908,12.51427836,12.82755036,12.66726554,12.67564722,12.90325206,12.79944608,12.65031638,12.7337213,12.5943639,12.10313018,12.0276714,12.71290653,12.63953893,12.47200087,12.59570503,12.60434623,12.9161246,12.49782751

output/preprocess/Coronary_artery_disease/code/GSE109048.py

@@ -0,0 +1,293 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE109048"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE109048"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE109048.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE109048.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE109048.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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 based on background info
+is_gene_available = True  # Platelet gene expression profiling (not miRNA/methylation)
+
+# 2) Variable availability and converters
+# From the provided Sample Characteristics Dictionary:
+# 0: ['tissue: Platelets']
+# 1: ['diagnosis: sCAD', 'diagnosis: healthy', 'diagnosis: STEMI']
+trait_row = 1  # diagnosis holds CAD-related labels (healthy, sCAD, STEMI)
+age_row = None  # not available in the dictionary
+gender_row = None  # not available in the dictionary
+
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, float) and pd.isna(x):
+        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):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower().strip()
+    # Binary: CAD presence (1) vs Healthy/Control (0)
+    if vl in {"healthy", "control", "hd", "normal"}:
+        return 0
+    # Map CAD-related diagnoses to 1
+    if any(k in vl for k in ["scad", "stemi", "ami", "myocardial infarction", "coronary artery disease", "cad"]):
+        return 1
+    return None
+
+def convert_age(x):
+    # Not used (age_row=None), but provided for completeness
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        if 0 <= age <= 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    # Not used (gender_row=None), but provided for completeness
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower().strip()
+    if vl in {"male", "m"}:
+        return 1
+    if vl in {"female", "f"}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (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
+    )
+    # Observe a preview
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+    # Save clinical data
+    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
+# The observed identifiers (e.g., '2824546_st') are Affymetrix probe set IDs, not human gene symbols.
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+import pandas as pd
+
+# Keep a copy of the probe-level data
+probe_data = gene_data.copy()
+
+# 1) Robustly select the identifier column by direct overlap against expression IDs
+expr_ids = set(probe_data.index.astype(str))
+
+id_candidates = [c for c in ['ID', 'probeset_id'] if c in gene_annotation.columns]
+if not id_candidates:
+    raise ValueError("No suitable identifier columns ('ID', 'probeset_id') found in gene_annotation.")
+
+overlaps = {}
+for col in id_candidates:
+    vals = gene_annotation[col].astype(str).str.strip()
+    overlaps[col] = len(set(vals) & expr_ids)
+
+# Choose the column with the largest overlap; break ties preferring 'ID'
+best_id_col = sorted(overlaps.items(), key=lambda x: (x[1], x[0] == 'ID'), reverse=True)[0][0]
+print(f"Selected identifier column for mapping: {best_id_col} (overlap={overlaps[best_id_col]})")
+if overlaps[best_id_col] == 0:
+    raise ValueError("No overlap between gene_annotation IDs and expression probe IDs; cannot map.")
+
+# 2) Select a biologically plausible gene symbol column
+def count_extracted_symbols(series: pd.Series) -> int:
+    return series.astype(str).map(lambda s: len(extract_human_gene_symbols(s))).sum()
+
+gene_symbol_candidates = [c for c in [
+    'gene_symbol', 'symbol', 'Gene Symbol', 'GENE_SYMBOL', 'gene_assignment', 'mrna_assignment'
+] if c in gene_annotation.columns]
+
+if not gene_symbol_candidates:
+    raise ValueError("No plausible gene symbol columns found (expected one of gene_symbol/symbol/gene_assignment/mrna_assignment).")
+
+# Score candidates by number of extractable symbols
+scores = {c: count_extracted_symbols(gene_annotation[c]) for c in gene_symbol_candidates}
+# Sort by score descending
+sorted_gene_cols = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+
+# Pick the best that is not the same as the identifier column
+best_gene_col = None
+for col, sc in sorted_gene_cols:
+    if col != best_id_col:
+        best_gene_col = col
+        best_count = sc
+        break
+
+if best_gene_col is None:
+    raise ValueError("Failed to select a gene symbol column different from the identifier column.")
+
+print(f"Selected gene symbol column: {best_gene_col} (extracted_symbol_tokens={best_count})")
+
+# 3) Build mapping and apply to convert probe-level to gene-level data
+mapping_df = get_gene_mapping(gene_annotation, prob_col=best_id_col, gene_col=best_gene_col)
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Normalize gene symbols and aggregate duplicates
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Report shapes
+print(f"Probe-level shape: {probe_data.shape}")
+print(f"Gene-level shape after mapping and normalization: {gene_data.shape}")
+
+# Save gene-level matrix
+out_dir = os.path.dirname(out_gene_data_file)
+os.makedirs(out_dir, exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols (reuse in-memory if available; otherwise load from disk)
+if 'gene_data' not in globals() or not isinstance(gene_data, pd.DataFrame):
+    if os.path.exists(out_gene_data_file):
+        gene_data = pd.read_csv(out_gene_data_file, index_col=0)
+    else:
+        raise RuntimeError("Gene expression data not found in memory or on disk.")
+
+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
+if 'selected_clinical_df' not in globals() or not isinstance(selected_clinical_df, pd.DataFrame):
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise RuntimeError("Clinical data not found in memory or on disk.")
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and drop biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Ensure pure Python bools for JSON serialization
+is_trait_biased = bool(is_trait_biased)
+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))
+
+# 5. Final validation and save cohort info with robust fallback for json serialization edge cases
+note = "INFO: Platelet expression; only trait available (no Age/Gender in annotations)."
+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 as e:
+    # Rare case: environments where non-native bools sneak in; retry after removing the file
+    if "not JSON serializable" in str(e):
+        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=str(note)
+        )
+    else:
+        raise
+
+# 6. Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Coronary_artery_disease/code/GSE120774.py

@@ -0,0 +1,252 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE120774"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE120774"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE120774.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE120774.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE120774.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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
+
+# Step 1: Determine gene expression availability
+is_gene_available = True  # Based on series title/summary indicating mRNA expression data
+
+# Step 2: Identify variable rows and define converters
+trait_row = 2   # 'disease: None' vs 'disease: coronary disease'
+age_row = None  # No age information in the sample characteristics
+gender_row = 1  # 'gender: M' / 'gender: F'
+
+def _after_colon(val):
+    if val is None:
+        return None
+    s = str(val)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(val):
+    v = _after_colon(val)
+    if v is None or v == "":
+        return None
+    v_low = v.lower()
+    # Map controls to 0, cases to 1
+    if any(k in v_low for k in ["none", "control", "no cad", "no coronary", "no disease", "normal"]):
+        return 0
+    if any(k in v_low for k in ["cad", "coronary", "case", "ischemic", "coronary disease"]):
+        return 1
+    return None
+
+def convert_age(val):
+    # Not used (age_row is None), but provided for completeness/robustness
+    v = _after_colon(val)
+    if v is None or v == "":
+        return None
+    import re
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(val):
+    v = _after_colon(val)
+    if v is None or v == "":
+        return None
+    v_low = v.lower()
+    if v_low in ["m", "male"]:
+        return 1
+    if v_low in ["f", "female"]:
+        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 (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 = preview_df(selected_clinical_df, n=5)
+    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. Extract gene expression data starting after "!series_matrix_table_begin"
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene/probe identifiers) as a list for readability
+print(list(gene_data.index[:20]))
+
+# Step 4: Gene Identifier Review
+import os
+import re
+import pandas as pd
+
+def is_human_gene_symbol(x: str) -> bool:
+    if not isinstance(x, str):
+        return False
+    s = x.strip()
+    if len(s) == 0:
+        return False
+    if s.isdigit():
+        return False
+    if s.upper().startswith(("ENSG", "ENST", "ENSP", "ILMN_", "A_", "NM_", "NR_")):
+        return False
+    if re.search(r"[._]", s):
+        return False
+    if re.match(r"^[A-Za-z][A-Za-z0-9\-]*$", s) is None:
+        return False
+    return True
+
+requires_mapping = True  # default conservative choice
+
+if os.path.exists(out_gene_data_file):
+    try:
+        df = pd.read_csv(out_gene_data_file, index_col=0)
+        # Heuristic: genes are usually the larger dimension
+        if df.shape[0] >= df.shape[1]:
+            gene_ids = df.index.astype(str).tolist()
+        else:
+            gene_ids = df.columns.astype(str).tolist()
+        sample_ids = gene_ids[: min(200, len(gene_ids))]
+        symbol_flags = [is_human_gene_symbol(g) for g in sample_ids]
+        # If majority look like valid gene symbols, mapping is not required
+        if len(symbol_flags) > 0 and (sum(symbol_flags) / len(symbol_flags)) > 0.8:
+            requires_mapping = False
+        else:
+            requires_mapping = True
+    except Exception:
+        requires_mapping = True
+else:
+    # If the file doesn't exist, fall back to domain knowledge: purely numeric IDs require mapping.
+    requires_mapping = True
+
+print(f"requires_gene_mapping = {str(requires_mapping)}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Decide columns for mapping and extract mapping dataframe
+# Expression data uses numeric probe IDs like '7892501', which match 'ID' in gene_annotation.
+# Gene symbols are embedded in 'gene_assignment'.
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 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)
+
+# Optionally save the processed gene expression data for downstream steps
+import os
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save normalized gene data
+# Load gene data from previous step if not in memory
+if 'gene_data' not in locals():
+    if os.path.exists(out_gene_data_file):
+        gene_data = pd.read_csv(out_gene_data_file, index_col=0)
+    else:
+        raise FileNotFoundError("Gene data not found in memory or on disk.")
+
+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
+# Load clinical data if not in memory
+if 'selected_clinical_df' not in locals():
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise FileNotFoundError("Clinical data not found in memory or on disk.")
+
+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 demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+# Ensure Python-native bool
+is_trait_biased = bool(is_trait_biased)
+
+# 5. Final validation and save cohort info
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_after = [c for c in unbiased_linked_data.columns if c not in covariate_cols]
+is_gene_available_final = bool(len(gene_cols_after) > 0)
+is_trait_available_final = bool((trait in unbiased_linked_data.columns) and (int(unbiased_linked_data[trait].notna().sum()) > 0))
+
+note = "INFO: Probe-to-gene mapping applied; gene symbols normalized using NCBI synonyms; Gender available; Age not available."
+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/Coronary_artery_disease/code/GSE156357.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE156357"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE156357"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE156357.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE156357.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE156357.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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) Gene expression data availability
+is_gene_available = True  # mRNA gene expression profiling was performed on responder cells
+
+# 2) Variable availability
+# All participants are men with stable CAD; no per-sample variation in CAD status, age, or gender in the sample dict.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _get_value_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) == 2 else x.strip()
+    return x
+
+def convert_trait(x):
+    # CAD status is constant (all CAD) and not explicitly recorded per sample; treat as unavailable.
+    return None
+
+def convert_age(x):
+    # No age field present
+    return None
+
+def convert_gender(x):
+    # Background says all are men; constant feature -> not used
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction
+# Skipped because trait_row is None (no usable clinical variability)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# The observed identifiers like '1007_s_at', '1053_at', '1255_g_at' are Affymetrix probe set IDs, not HGNC gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1) Identify the correct columns for probe IDs and gene symbols from the annotation preview:
+#    Probe identifier column: 'ID'
+#    Gene symbol column: 'Gene Symbol'
+
+# 2) Build the probe-to-gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene 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
+
+# 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 clinical and genetic data only if clinical features exist; otherwise, skip linking
+try:
+    selected_clinical_data
+except NameError:
+    selected_clinical_data = None
+
+linked_data = None
+if selected_clinical_data is not None and not selected_clinical_data.empty:
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine bias and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data
+    )
+
+    # 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)
+else:
+    # No usable clinical trait data available; record metadata as initial filtering only
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Coronary_artery_disease/code/GSE234398.py

@@ -0,0 +1,141 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE234398"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE234398"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE234398.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE234398.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE234398.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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: Dataset Analysis and Clinical Feature Extraction for cohort GSE234398
+
+# 1) Gene expression availability
+is_gene_available = True  # mRNA gene expression (not miRNA/methylation) per series description
+
+# 2) Variable availability and converters
+# From the provided Sample Characteristics Dictionary:
+# 0: ['cell type: monocytes'] -> not the trait
+# 1: ['Sex: male', 'Sex: female'] -> gender
+# 2: ['age: ...'] -> age
+# All samples are CAD patients per background -> trait is constant and thus unavailable for association
+trait_row = None
+age_row = 2
+gender_row = 1
+
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        return s.split(':', 1)[1].strip()
+    return s.strip()
+
+def convert_trait(x):
+    # Binary: 1 = CAD/Case/Patient, 0 = Control/Healthy
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    positives = ['cad', 'coronary artery disease', 'case', 'patient', 'disease', 'yes', 'cad patient']
+    negatives = ['control', 'healthy', 'no', 'normal']
+    # Heuristics
+    if any(tok in v_low for tok in positives):
+        return 1
+    if any(tok in v_low for tok in negatives):
+        return 0
+    # Direct numeric-like
+    if v_low in {'1', '0'}:
+        return int(v_low)
+    return None
+
+def convert_age(x):
+    # Continuous numeric age in years
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    if v_low in {'na', 'n/a', 'nan', 'none', 'unknown'}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        # Extract leading numeric if present
+        import re
+        m = re.search(r'[-+]?\d+(\.\d+)?', v)
+        if m:
+            try:
+                return float(m.group(0))
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    if v_low in {'male', 'm'}:
+        return 1
+    if v_low in {'female', 'f'}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    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)
+else:
+    print("Trait data not available or constant for this cohort; skipping clinical feature extraction.")

output/preprocess/Coronary_artery_disease/code/GSE250283.py

@@ -0,0 +1,135 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE250283"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE250283"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE250283.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE250283.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE250283.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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 based on provided background and sample characteristics
+is_gene_available = True  # Illumina HumanHT-12 v4.0 expression beadchip indicates mRNA expression data
+
+# Sample Characteristics Dictionary indicates:
+# 0: tissue (blood) - not useful for trait
+# 1: gender - available
+# 2: DM vs Healthy - diabetes status, not CAD
+# 3: retinopathy status - not CAD
+trait_row = None   # No explicit CAD status in the provided characteristics
+age_row = None     # No age field present
+gender_row = 1     # 'gender: Female' / 'gender: Male'
+
+# Conversion functions
+import re
+from typing import Optional
+
+def _extract_value(cell: str) -> str:
+    if cell is None:
+        return ""
+    parts = str(cell).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(cell).strip()
+
+def convert_trait(x: str) -> Optional[int]:
+    """
+    Convert CAD status to binary: CAD present -> 1, no CAD -> 0.
+    Returns None if CAD cannot be inferred from the value.
+    """
+    val = _extract_value(x).lower()
+    # Explicit CAD mentions
+    keywords_cad_pos = [
+        "cad", "coronary artery disease", "with cad", "cad+", "cad positive", "with coronary artery disease"
+    ]
+    keywords_cad_neg = [
+        "no cad", "without cad", "non-cad", "cad-", "cad negative", "control without cad"
+    ]
+    # Strong negative if explicit "healthy" and CAD context is implied
+    if any(k in val for k in keywords_cad_neg):
+        return 0
+    if any(k in val for k in keywords_cad_pos):
+        # Avoid false positives like "family history of CAD: none" by checking negation
+        if "no " in val or "without" in val or "none" in val:
+            return 0
+        return 1
+
+    # Generic case/control if explicitly labeled for CAD (avoid mapping unrelated groups)
+    if "case" in val and ("cad" in val or "coronary" in val):
+        return 1
+    if "control" in val and ("cad" in val or "coronary" in val):
+        return 0
+
+    # Not inferable as CAD
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    val = _extract_value(x)
+    # Extract first number (integer or float)
+    m = re.search(r"(\d+(?:\.\d+)?)", val)
+    if not m:
+        return None
+    age = float(m.group(1))
+    if 0 <= age <= 120:
+        return age
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    val = _extract_value(x).lower()
+    if val in {"female", "f", "woman", "women"}:
+        return 0
+    if val in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction (skip because trait_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 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, index=True)

output/preprocess/Coronary_artery_disease/code/GSE54975.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE54975"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE54975"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE54975.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE54975.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE54975.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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
+# Decisions based on provided background and sample characteristics:
+# - This is a SuperSeries about epigenomic changes; however, GEO Series Matrix parsing suggests an expression matrix exists.
+# - No disease status (CAD) recorded; only background, cell type, and treatment. Hence trait, age, and gender are unavailable.
+
+# 1) Gene expression availability
+is_gene_available = True  # Likely expression data (matrix) rather than pure miRNA/methylation
+
+# 2) Variable availability
+trait_row = None   # No CAD status in characteristics
+age_row = None     # No age information
+gender_row = None  # No gender information
+
+# 2.2) Converters (defined for interface consistency; not used since rows are None)
+
+def convert_trait(x):
+    # Placeholder: no CAD status available
+    if x is None:
+        return None
+    try:
+        v = str(x).split(":", 1)[-1].strip().lower()
+    except Exception:
+        return None
+    # If ever present, map CAD-related labels to binary
+    if v in {"cad", "coronary artery disease", "case", "patient"}:
+        return 1
+    if v in {"control", "healthy", "no cad", "non-cad"}:
+        return 0
+    return None
+
+def convert_age(x):
+    # No age field available
+    if x is None:
+        return None
+    try:
+        v = str(x).split(":", 1)[-1].strip()
+        v = v.replace("years", "").replace("year", "").strip()
+        return float(v) if v not in {"", "na", "n/a", "none", "unknown"} else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # No gender field available
+    if x is None:
+        return None
+    try:
+        v = str(x).split(":", 1)[-1].strip().lower()
+    except Exception:
+        return None
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v 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 is skipped because trait_row is None
+
+# 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
+# Choose the identifier and gene symbol columns based on previews:
+# - Probe identifier column: 'ID' (matches ILMN_* probe IDs in expression data)
+# - Gene symbol column: 'Symbol'
+
+# 1) Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 2) Apply mapping to convert probe-level to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save gene-level expression
+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 trait data was extracted earlier; otherwise, record as unusable for association analysis
+if 'selected_clinical_data' in locals():
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait and demographics are biased
+    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; processed and quality-checked."
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait/clinical features not available; skip linking and record metadata accordingly
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Coronary_artery_disease/code/GSE59867.py

@@ -0,0 +1,124 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE59867"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE59867"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE59867.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE59867.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE59867.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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 data availability
+is_gene_available = True  # Affymetrix HuGene 1.0 ST arrays (mRNA) -> gene expression data available
+
+# From the provided sample characteristics, CAD status cannot be distinguished (all groups have CAD).
+# Hence, treat the CAD trait as not available for association analysis.
+trait_row = None
+
+# Age and gender are not present in the provided sample characteristics dictionary.
+age_row = None
+gender_row = None
+
+# Step 2: Define conversion functions
+
+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) == 2 else x.strip()
+        return val if val not in {"", "N/A", "NA", "na", "n/a", "null", "Null", "NONE", "None"} else None
+    return x
+
+def convert_trait(x):
+    # Not used since trait_row is None. For robustness, map 'hf progression' values to CAD presence (1), N/A -> None.
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if v in {"stable cad", "hf", "non-hf"}:
+        return 1  # All indicate CAD presence, thus constant across dataset
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    try:
+        # Extract numeric component if age is embedded in text
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", str(val))
+        if not m:
+            return None
+        age = float(m.group())
+        # Basic sanity check for human age
+        if 0 < age < 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Coronary_artery_disease/code/GSE64554.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE64554"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE64554"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE64554.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE64554.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE64554.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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  # "PART 1 - Genes" indicates gene expression data is available.
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics dictionary:
+# 0: 'age: ...' -> age_row
+# 1: 'tissue: ...' -> not used for this task
+# 2: 'disease state: coronary artery disease' / 'control' -> trait_row
+trait_row = 2
+age_row = 0
+gender_row = None  # Gender not provided in the sample characteristics
+
+def _extract_value(cell):
+    if cell is None:
+        return None
+    s = str(cell).strip().strip('"').strip("'")
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(cell):
+    v = _extract_value(cell)
+    if not v:
+        return None
+    v_low = v.lower()
+    # Negative labels first to avoid 'cad' substring in 'non-cad'
+    neg_patterns = ['control', 'ctrl', 'healthy', 'normal', 'non-cad', 'no cad', 'without cad', 'non cad']
+    if any(p in v_low for p in neg_patterns):
+        return 0
+    pos_patterns = ['cad', 'coronary artery disease', 'coronary-artery disease', 'coronary heart disease', 'ischemic heart disease', 'ihd']
+    if any(p in v_low for p in pos_patterns) or 'coronary artery' in v_low:
+        return 1
+    # Fallback heuristics
+    if v_low in ['case', 'patient']:
+        return 1
+    if v_low in ['control', 'ctrl']:
+        return 0
+    return None
+
+def convert_age(cell):
+    v = _extract_value(cell)
+    if not v:
+        return None
+    # Extract the first integer or float number
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(cell):
+    v = _extract_value(cell)
+    if not v:
+        return None
+    v_low = v.lower()
+    if v_low in ['female', 'f', 'woman', 'women', 'girl']:
+        return 0
+    if v_low in ['male', 'm', 'man', 'men', 'boy']:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if clinical data available)
+if 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
+    )
+    prev = preview_df(selected_clinical_df)
+    print(prev)
+
+    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 annotation preview
+id_col = 'ID'  # Matches probe IDs like ILMN_1343291 in gene_data
+symbol_candidates = ['Symbol', 'ILMN_Gene']
+symbol_col = next((c for c in symbol_candidates if c in gene_annotation.columns), None)
+
+# Fallback safety: if chosen symbol column exists but is entirely NaN, try next candidate
+if symbol_col is not None and gene_annotation[symbol_col].dropna().empty:
+    remaining = [c for c in symbol_candidates if c != symbol_col and c in gene_annotation.columns]
+    symbol_col = remaining[0] if remaining else symbol_col
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=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
+
+# Ensure clinical features are available in memory; if not, load from disk
+if 'selected_clinical_df' not in globals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Evaluate bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available = (trait in unbiased_linked_data.columns) and (len(unbiased_linked_data) > 0)
+note = "INFO: Paired EAT/SAT samples; gender not provided; tissue heterogeneity may be present."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Coronary_artery_disease/code/GSE64566.py

@@ -0,0 +1,251 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE64566"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE64566"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE64566.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE64566.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE64566.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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  # Title indicates "GE/miRNA", so gene expression is likely available.
+
+# 2) Variable availability and converters
+# Identify rows from the provided Sample Characteristics Dictionary
+trait_row = 0  # 'disease state: coronary artery disease' vs 'disease state: control'
+age_row = 1    # 'age: <number>'
+gender_row = None  # No gender information present
+
+def _after_colon(value):
+    if value is None:
+        return None
+    parts = str(value).split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Positive/CAD
+    pos = {'coronary artery disease', 'cad', 'case', 'patient', 'disease', 'cad+', 'coronary-artery-disease'}
+    # Control/Healthy
+    neg = {'control', 'ctrl', 'healthy', 'normal', 'cad-', 'no cad', 'non-cad', 'non cad', 'no-cad'}
+    if vl in pos:
+        return 1
+    if vl in neg:
+        return 0
+    # Heuristics
+    if 'coronary' in vl or 'cad' in vl or 'disease' in vl:
+        # e.g., 'coronary artery disease'
+        return 1
+    if 'control' in vl or 'ctrl' in vl or 'healthy' in vl or 'normal' in vl:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v = v.strip()
+    # Extract first number
+    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
+    vl = v.strip().lower()
+    if vl in {'male', 'm', 'man'}:
+        return 1
+    if vl in {'female', 'f', 'woman'}:
+        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 (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("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
+# ILMN_ prefixed identifiers are Illumina probe IDs, not human gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for probe IDs and gene symbols from the annotation preview:
+# - Probe IDs: 'ID' (e.g., 'ILMN_1725881'), matching the probe IDs in the expression data (e.g., 'ILMN_3166935')
+# - Gene symbols: 'Symbol' (e.g., 'TRIM44', 'FCGR2B')
+
+# 1-2) Build mapping dataframe from annotation
+probe_col = 'ID'
+gene_col = 'Symbol'
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+probe_data = gene_data  # preserve 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)
+
+# 2. Link clinical and genetic data (use the correct clinical dataframe variable)
+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
+is_gene_available_final = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (trait in selected_clinical_df.index) and (selected_clinical_df.shape[1] > 0)
+note = "INFO: No gender data in clinical annotations; samples come from two adipose tissues (EAT and SAT)."
+
+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)
+
+# Step 8: Gene Identifier Mapping
+import pandas as pd
+
+# Rebuild probe-level data to ensure a clean start
+probe_data = get_genetic_data(matrix_file)
+
+# Decide columns for mapping
+probe_col = 'ID'
+symbol_col = 'Symbol'
+fallback_cols = [c for c in ['ILMN_Gene', 'Definition', 'Synonyms'] if c in gene_annotation.columns]
+
+mapping_sources = [symbol_col] + fallback_cols if symbol_col in gene_annotation.columns else fallback_cols
+
+# Construct mapping dataframe
+cols_to_take = [probe_col] + mapping_sources
+mapping_df = gene_annotation.loc[:, cols_to_take].copy()
+
+def coalesce_gene_text(row):
+    vals = []
+    for c in mapping_sources:
+        val = row.get(c)
+        if pd.notna(val):
+            vals.append(str(val))
+    if not vals:
+        return None
+    if pd.notna(row.get(symbol_col, None)):
+        return str(row.get(symbol_col))
+    return " ; ".join(vals)
+
+mapping_df['Gene'] = mapping_df.apply(coalesce_gene_text, axis=1)
+mapping_df = mapping_df[[probe_col, 'Gene']].dropna()
+mapping_df[probe_col] = mapping_df[probe_col].astype(str).str.strip()
+mapping_df['Gene'] = mapping_df['Gene'].astype(str).str.strip()
+mapping_df = mapping_df[mapping_df[probe_col] != ""]
+# Restrict to probes present in the expression matrix
+mapping_df = mapping_df[mapping_df[probe_col].isin(probe_data.index)].copy()
+mapping_df = mapping_df.rename(columns={probe_col: 'ID'})
+
+# Diagnostics before mapping
+print(f"Total probes in expression data: {probe_data.shape[0]}")
+print(f"Probes with mapping entries: {mapping_df['ID'].nunique()}")
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(probe_data, mapping_df)
+
+# Diagnostics after mapping
+print(f"Total genes after mapping: {gene_data.shape[0]}")
+print("Mapped gene expression preview (shape):", gene_data.shape)

output/preprocess/Coronary_artery_disease/code/GSE86216.py

@@ -0,0 +1,130 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE86216"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE86216"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/GSE86216.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/GSE86216.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/GSE86216.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_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
+
+# 1) Gene expression availability
+is_gene_available = True  # PBMC transcriptome profiling indicates mRNA expression data (not miRNA-only or methylation)
+
+# 2) Variable availability based on Sample Characteristics Dictionary
+# Keys present: 0: sampleid, 1: treatment, 2: time, 3: cell type
+# No explicit or inferable Age/Gender; Trait (CAD) is constant (all CAD patients)
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Data type conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) == 2 else x.strip()
+    return x
+
+def convert_trait(x):
+    # Binary: 1 = CAD, 0 = Control
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    # Common case/control mappings
+    case_markers = [
+        "cad", "coronary artery disease", "patient", "case",
+        "stable angina", "ncl", "multivessel coronary artery disease"
+    ]
+    control_markers = ["control", "healthy", "normal", "no cad", "non-cad"]
+    if any(m in v for m in case_markers):
+        return 1
+    if any(m in v for m in control_markers):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = str(val).lower()
+    # Extract first integer or float
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        num = float(m.group(1))
+        return num
+    except:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    if v in ["male", "m", "man"]:
+        return 1
+    if v in ["female", "f", "woman"]:
+        return 0
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+# If in future a trait_row is identified, the following template can be used:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Coronary_artery_disease/code/TCGA.py

@@ -0,0 +1,59 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Coronary_artery_disease/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Coronary_artery_disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Coronary_artery_disease/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Coronary_artery_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Discover available TCGA cohort subdirectories
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Define synonyms/related terms for Coronary Artery Disease
+cad_terms = {
+    "coronary", "artery", "arterial", "ischemic", "ischemia", "myocardial",
+    "heart", "cardiac", "cardio", "cad", "atherosclerosis", "vascular"
+}
+
+# Score subdirectories for relevance
+def score_dir(name: str) -> int:
+    name_l = name.lower()
+    return sum(1 for t in cad_terms if t in name_l)
+
+scores = {d: score_dir(d) for d in subdirs}
+# Select the best matching directory if any positive score
+selected_dir = None
+if scores:
+    best_dir, best_score = max(scores.items(), key=lambda x: x[1])
+    if best_score > 0:
+        selected_dir = best_dir
+
+if selected_dir is None:
+    # No suitable TCGA cohort for Coronary Artery Disease; 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)
+    clinical_fp, genetic_fp = tcga_get_relevant_filepaths(cohort_dir)
+
+    clinical_df = pd.read_csv(clinical_fp, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_fp, sep='\t', index_col=0, low_memory=False)
+
+    print(list(clinical_df.columns))

output/preprocess/Craniosynostosis/clinical_data/GSE27976.csv

@@ -1,4 +1,4 @@
 ,GSM692146,GSM692147,GSM692148,GSM692149,GSM692150,GSM692151,GSM692152,GSM692153,GSM692154,GSM692155,GSM692156,GSM692157,GSM692158,GSM692159,GSM692160,GSM692161,GSM692162,GSM692163,GSM692164,GSM692165,GSM692166,GSM692167,GSM692168,GSM692169,GSM692170,GSM692171,GSM692172,GSM692173,GSM692174,GSM692175,GSM692176,GSM692177,GSM692178,GSM692179,GSM692180,GSM692181,GSM692182,GSM692183,GSM692184,GSM692185,GSM692186,GSM692187,GSM692188,GSM692189,GSM692190,GSM692191,GSM692192,GSM692193,GSM692194,GSM692195,GSM692196,GSM692197,GSM692198,GSM692199,GSM692200,GSM692201,GSM692202,GSM692203,GSM692204,GSM692205,GSM692206,GSM692207,GSM692208,GSM692209,GSM692210,GSM692211,GSM692212,GSM692213,GSM692214,GSM692215,GSM692216,GSM692217,GSM692218,GSM692219,GSM692220,GSM692221,GSM692222,GSM692223,GSM692224,GSM692225,GSM692226,GSM692227,GSM692228,GSM692229,GSM692230,GSM692231,GSM692232,GSM692233,GSM692234,GSM692235,GSM692236,GSM692237,GSM692238,GSM692239,GSM692240,GSM692241,GSM692242,GSM692243,GSM692244,GSM692245,GSM692246,GSM692247,GSM692248,GSM692249,GSM692250,GSM692251,GSM692252,GSM692253,GSM692254,GSM692255,GSM692256,GSM692257,GSM692258,GSM692259,GSM692260,GSM692261,GSM692262,GSM692263,GSM692264,GSM692265,GSM692266,GSM692267,GSM692268,GSM692269,GSM692270,GSM692271,GSM692272,GSM692273,GSM692274,GSM692275,GSM692276,GSM692277,GSM692278,GSM692279,GSM692280,GSM692281,GSM692282,GSM692283,GSM692284,GSM692285,GSM692286,GSM692287,GSM692288,GSM692289,GSM692290,GSM692291,GSM692292,GSM692293,GSM692294,GSM692295,GSM692296,GSM692297,GSM692298,GSM692299,GSM692300,GSM692301,GSM692302,GSM692303,GSM692304,GSM692305,GSM692306,GSM692307,GSM692308,GSM692309,GSM692310,GSM692311,GSM692312,GSM692313,GSM692314,GSM692315,GSM692316,GSM692317,GSM692318,GSM692319,GSM692320,GSM692321,GSM692322,GSM692323,GSM692324,GSM692325,GSM692326,GSM692327,GSM692328,GSM692329,GSM692330,GSM692331,GSM692332,GSM692333,GSM692334,GSM692335,GSM692336,GSM692337,GSM692338,GSM692339,GSM692340,GSM692341,GSM692342,GSM692343,GSM692344,GSM692345,GSM692346,GSM692347,GSM692348,GSM692349,GSM692350,GSM692351,GSM692352,GSM692353,GSM692354,GSM692355,GSM692356,GSM692357,GSM692358,GSM692359,GSM692360,GSM692361,GSM692362,GSM692363,GSM692364,GSM692365,GSM692366,GSM692367,GSM692368,GSM692369,GSM692370,GSM692371,GSM692372,GSM692373,GSM692374,GSM692375,GSM692376,GSM692377,GSM692378,GSM692379,GSM692380,GSM692381,GSM692382,GSM692383,GSM692384,GSM692385,GSM692386,GSM692387,GSM692388,GSM692389,GSM692390,GSM692391,GSM692392,GSM692393,GSM692394
-Melanoma,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Craniosynostosis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
 Age,12.87,10.4,12.3,11.4,10.1,11.0,4.27,7.97,4.33,9.33,7.93,10.27,10.87,3.87,3.2,13.27,5.6,14.9,3.03,12.4,8.9,14.17,6.33,14.87,8.4,9.07,13.33,10.0,13.23,10.33,14.33,6.67,2.93,10.2,6.87,9.7,8.77,11.7,10.17,6.33,11.47,2.93,11.17,8.13,3.2,7.9,5.83,9.4,2.47,9.33,13.1,9.43,9.3,10.5,9.77,5.27,22.17,13.1,12.37,11.1,9.67,6.67,10.77,7.7,10.9,11.83,16.33,13.17,6.47,8.37,17.6,24.23,6.77,8.47,18.33,14.2,3.27,8.43,9.93,18.43,19.6,7.97,11.8,9.0,10.37,10.97,3.13,9.87,11.53,22.2,27.53,8.57,13.57,11.93,4.37,10.83,18.97,7.53,9.73,2.93,3.37,7.27,8.07,8.2,8.37,7.4,5.7,9.77,6.5,9.7,23.77,7.67,9.33,4.77,4.43,5.03,19.77,4.5,5.2,4.83,5.3,8.6,5.73,9.37,4.67,4.4,3.8,8.1,6.13,5.2,9.0,5.87,5.33,13.17,20.17,3.97,11.17,8.3,8.43,5.23,5.37,4.93,8.5,3.13,4.9,10.57,3.63,4.47,3.53,4.97,5.87,5.83,11.13,8.63,2.93,5.07,4.03,4.57,6.27,3.87,3.6,12.13,3.9,5.93,6.3,5.03,5.3,3.33,4.0,12.0,36.0,6.0,5.0,2.0,0.5,3.0,9.03,3.43,7.37,4.77,5.63,5.77,11.5,6.4,8.87,10.83,25.43,7.27,11.97,10.23,12.77,22.97,5.53,10.77,4.2,8.33,7.63,4.93,10.8,4.33,8.73,8.73,10.2,11.83,10.6,10.0,1.0,5.0,16.0,84.0,5.0,120.0,12.0,48.0,36.0,20.0,48.0,2.0,18.0,60.0,48.0,12.0,24.0,9.0,96.0,6.0,15.0,120.0,96.0,13.0,84.0,1.0,72.0,48.0,48.0,6.0,72.0,72.0,34.0,84.0,13.0,2.0,0.5,0.75,0.57,25.03,4.47,4.23,10.4
 Gender,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0

output/preprocess/Craniosynostosis/code/GSE27976.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Craniosynostosis"
+cohort = "GSE27976"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Craniosynostosis"
+in_cohort_dir = "../DATA/GEO/Craniosynostosis/GSE27976"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Craniosynostosis/GSE27976.csv"
+out_gene_data_file = "./output/z2/preprocess/Craniosynostosis/gene_data/GSE27976.csv"
+out_clinical_data_file = "./output/z2/preprocess/Craniosynostosis/clinical_data/GSE27976.csv"
+json_path = "./output/z2/preprocess/Craniosynostosis/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 (gene expression) study per series description
+
+# 2) Variable availability and converters
+trait_row = 2   # 'type: Control' vs synostosis subtypes
+age_row = 0     # 'age months: <value>'
+gender_row = 1  # 'gender: M/F'
+
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vlow = str(v).strip().lower()
+    if "control" in vlow:
+        return 0
+    # map any synostosis type to case
+    if "synost" in vlow:
+        return 1
+    # Heuristic: any non-control labeled type in this dataset represents craniosynostosis
+    if vlow in {"sagittal", "metopic", "coronal", "coronal synostosis r", "coronal synostosis l"}:
+        return 1
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    if isinstance(v, (int, float)):
+        return float(v)
+    # extract first numeric value (assumed months)
+    m = re.search(r'[-+]?\d*\.?\d+', str(v))
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vlow = str(v).strip().lower()
+    if vlow in {"m", "male"}:
+        return 1
+    if vlow in {"f", "female"}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for mapping: probe IDs ('ID') and gene symbols info ('gene_assignment')
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene 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. 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
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: Gene symbols normalized and data linked; missing values handled per protocol."
+)
+
+# 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/Craniosynostosis/code/TCGA.py

@@ -0,0 +1,68 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Craniosynostosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Craniosynostosis/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Craniosynostosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Craniosynostosis/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Craniosynostosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+# Step 1: Identify the most relevant TCGA subdirectory for Craniosynostosis (none expected)
+subdirs = os.listdir(tcga_root_dir)
+
+# Define keywords related to Craniosynostosis
+keywords = [
+    ("craniosynostosis", 5),
+    ("synostosis", 4),
+    ("cranio", 3),
+    ("skull", 2),
+    ("suture", 1),
+]
+
+def score_dir(name: str) -> int:
+    lname = name.lower()
+    return max((w for k, w in keywords if k in lname), default=0)
+
+scored = [(d, score_dir(d)) for d in subdirs]
+# Select dir with highest score if any > 0
+selected_dir = None
+if scored:
+    best_dir, best_score = max(scored, key=lambda x: x[1])
+    if best_score > 0:
+        selected_dir = best_dir
+
+clinical_df, genetic_df = pd.DataFrame(), pd.DataFrame()
+
+if selected_dir is None:
+    # No suitable TCGA cohort for Craniosynostosis; record and skip
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort found for the trait. Skipping. Clinical columns: []")
+else:
+    # Step 2: Identify file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    def read_tcga_file(path: str) -> pd.DataFrame:
+        compression = 'gzip' if path.endswith('.gz') else None
+        return pd.read_csv(path, sep='\t', index_col=0, low_memory=False, compression=compression)
+
+    clinical_df = read_tcga_file(clinical_file_path)
+    genetic_df = read_tcga_file(genetic_file_path)
+
+    # Step 4: Print column names of the clinical data
+    print(list(clinical_df.columns))

output/preprocess/Craniosynostosis/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE27976": {
-    "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": 249
-  },
-  "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
-  }
-}
+{"GSE27976": {"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": 249, "note": "INFO: Gene symbols normalized and data linked; missing values handled per protocol."}, "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/Creutzfeldt-Jakob_Disease/clinical_data/GSE62699.csv

@@ -0,0 +1,2 @@
+,GSM1531616,GSM1531617,GSM1531618,GSM1531619,GSM1531620,GSM1531621,GSM1531622,GSM1531623,GSM1531624,GSM1531625,GSM1531626,GSM1531627,GSM1531628,GSM1531629,GSM1531630,GSM1531631,GSM1531632,GSM1531633,GSM1531634,GSM1531635,GSM1531636,GSM1531637,GSM1531638,GSM1531639,GSM1531640,GSM1531641,GSM1531642,GSM1531643,GSM1531644,GSM1531645,GSM1531646,GSM1531647,GSM1531648,GSM1531649,GSM1531650,GSM1531651
+Creutzfeldt-Jakob_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

output/preprocess/Creutzfeldt-Jakob_Disease/code/GSE62699.py

@@ -0,0 +1,122 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Creutzfeldt-Jakob_Disease"
+cohort = "GSE62699"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease"
+in_cohort_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease/GSE62699"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/GSE62699.csv"
+out_gene_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/gene_data/GSE62699.csv"
+out_clinical_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/clinical_data/GSE62699.csv"
+json_path = "./output/z2/preprocess/Creutzfeldt-Jakob_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
+
+# 1) Gene expression availability
+# Background indicates mRNA (Affymetrix HG-U133A 2.0) was profiled, so gene expression data is available.
+is_gene_available = True
+
+# 2) Variable availability and converters
+# From the provided Sample Characteristics Dictionary:
+# {0: ['diagnosis: alcohol dependence (AD)', 'diagnosis: Control'],
+#  1: ['tissue type: post mortem brain']}
+# The target trait is Creutzfeldt-Jakob Disease, which was excluded in this study.
+trait_row = None  # Not available for Creutzfeldt-Jakob Disease
+age_row = None    # Not present in provided characteristics dictionary
+gender_row = None # Not present in provided characteristics dictionary
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    parts = str(value).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(value).strip()
+
+def convert_trait(value):
+    """
+    Binary: 1 = Creutzfeldt-Jakob Disease present, 0 = absent.
+    Heuristics avoid false positives for 'AD' (adult) by using word boundaries.
+    """
+    v = _after_colon(value).strip().lower()
+    if not v:
+        return None
+    # Positive detection for CJD
+    if "creutzfeldt" in v or "jakob" in v or re.search(r"\bcjd\b", v):
+        return 1
+    # Confident negatives (controls or other diagnoses like alcohol dependence)
+    if re.search(r"\bcontrols?\b", v):
+        return 0
+    if re.search(r"\balcohol dependence\b", v, flags=re.I):
+        return 0
+    if re.search(r"\b(ad)\b", v, flags=re.I) or re.search(r"\(ad\)", v, flags=re.I):
+        return 0
+    if re.search(r"\bhealthy\b|\bnormal\b", v):
+        return 0
+    return None
+
+def convert_age(value):
+    """
+    Continuous: extract numeric age in years if present.
+    """
+    v = _after_colon(value).lower()
+    if not v or v in {"na", "n/a", "nan", "none", "missing", "unknown"}:
+        return None
+    m = re.search(r"(-?\d+\.?\d*)", v)
+    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(value):
+    """
+    Binary: 0 = female, 1 = male.
+    Use word boundaries to prevent 'female' being misclassified as 'male'.
+    """
+    v = _after_colon(value).strip().lower()
+    if not v:
+        return None
+    if re.search(r"\bfemale\b|^f$", v):
+        return 0
+    if re.search(r"\bmale\b|^m$", v):
+        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 is skipped because trait_row is None.

output/preprocess/Creutzfeldt-Jakob_Disease/code/GSE87629.py

@@ -0,0 +1,121 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Creutzfeldt-Jakob_Disease"
+cohort = "GSE87629"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease"
+in_cohort_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease/GSE87629"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/GSE87629.csv"
+out_gene_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/gene_data/GSE87629.csv"
+out_clinical_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/clinical_data/GSE87629.csv"
+json_path = "./output/z2/preprocess/Creutzfeldt-Jakob_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
+
+# 1) Gene expression availability
+is_gene_available = True  # Illumina microarray gene expression per series description
+
+# 2) Variable availability assessment from provided sample characteristics
+# Sample Characteristics Dictionary indicates:
+# - disease state: all "celiac disease..." (not our target trait: Creutzfeldt-Jakob Disease) and constant
+# - no explicit age or gender fields
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    parts = str(value).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(value).strip()
+
+def convert_trait(v):
+    # Map to binary: 1 = Creutzfeldt-Jakob Disease (CJD), 0 = not CJD
+    s = _after_colon(v).lower()
+    if not s:
+        return None
+    # Positive CJD indicators
+    if any(k in s for k in ["creutzfeldt", "jakob", "cjd", "prion disease", "sporadic cjd", "variant cjd"]):
+        return 1
+    # Strong non-CJD indicators commonly seen
+    if any(k in s for k in ["celiac", "coeliac", "gluten", "control", "healthy", "normal"]):
+        return 0
+    return None
+
+def convert_age(v):
+    s = _after_colon(v).lower()
+    if not s:
+        return None
+    # Extract first integer/float as age in years
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(v):
+    s = _after_colon(v).lower()
+    if not s:
+        return None
+    # Normalize common gender representations
+    if any(x in s for x in ["female", "f", "woman", "women"]):
+        return 0
+    if any(x in s for x 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 because 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_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Creutzfeldt-Jakob_Disease/code/TCGA.py

@@ -0,0 +1,61 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Creutzfeldt-Jakob_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Creutzfeldt-Jakob_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Identify the most relevant TCGA subdirectory for the trait
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_keywords = [
+    "creutzfeldt-jakob", "creutzfeldt", "jakob", "cjd", "prion", "spongiform", "encephalopathy"
+]
+
+def score_dir(name: str, keywords):
+    ln = name.lower().replace("_", " ").replace("-", " ")
+    return sum(1 for kw in keywords if kw in ln)
+
+scored = [(d, score_dir(d, trait_keywords)) for d in subdirs]
+# Select the directory with the highest score > 0
+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 for Creutzfeldt-Jakob Disease in TCGA; 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
+    def read_tcga_file(path):
+        compression = 'gzip' if path.endswith('.gz') else None
+        return pd.read_csv(path, sep='\t', index_col=0, low_memory=False, compression=compression)
+
+    clinical_df = read_tcga_file(clinical_file_path)
+    genetic_df = read_tcga_file(genetic_file_path)
+
+    # Step 4: Print clinical column names
+    print(f"Selected Cohort: {selected_dir}")
+    print(clinical_df.columns.tolist())

output/preprocess/Creutzfeldt-Jakob_Disease/cohort_info.json

@@ -1,32 +1 @@
-{
-  "GSE87629": {
-    "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": 39
-  },
-  "GSE62699": {
-    "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
-  },
-  "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
-  }
-}
+{"GSE87629": {"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}, "GSE62699": {"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/Crohns_Disease/clinical_data/GSE123086.csv

@@ -1,4 +1,4 @@
-GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049
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output/preprocess/Crohns_Disease/clinical_data/GSE123088.csv

@@ -1,4 +1,4 @@
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 Gender,1.0,,0.0,0.0,1.0,1.0,,1.0,0.0,0.0,0.0,,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,0.0,0.0,1.0,1.0,,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,,0.0,1.0,0.0,,1.0,,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Crohns_Disease/clinical_data/GSE186963.csv

@@ -0,0 +1,2 @@
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output/preprocess/Crohns_Disease/clinical_data/GSE193677.csv

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