Add files using upload-large-folder tool

output/preprocess/Obsessive-Compulsive_Disorder/clinical_data/GSE60190.csv

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-Gender,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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output/preprocess/Obsessive-Compulsive_Disorder/clinical_data/GSE78104.csv

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output/preprocess/Obsessive-Compulsive_Disorder/code/GSE60190.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obsessive-Compulsive_Disorder"
+cohort = "GSE60190"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obsessive-Compulsive_Disorder"
+in_cohort_dir = "../DATA/GEO/Obsessive-Compulsive_Disorder/GSE60190"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/GSE60190.csv"
+out_gene_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/gene_data/GSE60190.csv"
+out_clinical_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/clinical_data/GSE60190.csv"
+json_path = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/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
+from typing import Optional, Any
+
+# 1) Gene expression data availability
+is_gene_available = True  # Illumina HumanHT-12 v3 microarray indicates mRNA expression data
+
+# 2) Variable availability and converters
+trait_row = 3      # 'dx: Control/Bipolar/MDD/OCD/Tics/ED'
+age_row = 5        # 'age: <float>'
+gender_row = 7     # 'Sex: M/F'
+
+def _after_colon(x: Any) -> Optional[str]:
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x: Any) -> Optional[int]:
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.strip().lower()
+    # Presence of OCD vs others
+    if v_low == 'ocd':
+        return 1
+    # Non-OCD diagnoses or controls -> 0
+    if v_low in {'control', 'ed', 'tics', 'bipolar', 'mdd', 'ocpd'}:
+        return 0
+    return None
+
+def convert_age(x: Any) -> Optional[float]:
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v = v.strip()
+    # Handle common missing tokens
+    if v.lower() in {'na', 'n/a', 'nan', 'none', 'unknown'}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x: Any) -> Optional[int]:
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.strip().lower()
+    if v_low in {'f', 'female', 'women', 'woman'}:
+        return 0
+    if v_low in {'m', 'male', 'men', 'man'}:
+        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
+    )
+    print(preview_df(selected_clinical_df))
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    # Save with samples as columns; assuming selected_clinical_df is already in desired orientation
+    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
+# 1-2) Decide annotation columns and get mapping dataframe
+# Probe IDs in expression data are Illumina IDs like "ILMN_1343291", which correspond to the 'ID' column in annotation.
+# Gene symbols are provided in the 'Symbol' column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3) Apply mapping to convert probe-level to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias evaluation and removal of biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: Illumina HumanHT-12 v3 platform; DLPFC postmortem samples; trait from 'dx' (OCD vs non-OCD)."
+)
+
+# 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/Obsessive-Compulsive_Disorder/code/GSE78104.py

@@ -0,0 +1,210 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obsessive-Compulsive_Disorder"
+cohort = "GSE78104"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obsessive-Compulsive_Disorder"
+in_cohort_dir = "../DATA/GEO/Obsessive-Compulsive_Disorder/GSE78104"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/GSE78104.csv"
+out_gene_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/gene_data/GSE78104.csv"
+out_clinical_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/clinical_data/GSE78104.csv"
+json_path = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import os
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # mRNA + lncRNA microarray platform indicates gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+
+# Rows identified from the Sample Characteristics Dictionary
+trait_row = 1
+age_row = 3
+gender_row = 2
+
+def _extract_value(cell):
+    if cell is None:
+        return None
+    s = str(cell)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower().strip()
+    # Map OCD cases to 1, controls to 0
+    if ("ocd" in vl) or ("obsessive-compulsive" in vl) or ("obsessive compulsive" in vl) or ("patient" in vl) or ("case" in vl):
+        return 1
+    if ("normal control" in vl) or ("control" in vl) or ("healthy" in vl) or (vl == "normal"):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    m = re.search(r'(\d+(\.\d+)?)', vl)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    # Check female first to avoid 'male' substring in 'female'
+    if vl in {"female", "f", "woman", "women"}:
+        return 0
+    if vl in {"male", "m", "man", "men"}:
+        return 1
+    # Fallback using word boundaries
+    if re.search(r'\bfemale\b', vl):
+        return 0
+    if re.search(r'\bmale\b', vl):
+        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
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine columns for probe IDs and gene symbols/text
+probe_col = 'ID'
+candidate_gene_cols = [col for col in ['GeneSymbol', 'GeneName', 'Description', 'TargetID'] if col in gene_annotation.columns]
+
+# Build a unified gene text column to maximize chance of extracting valid human gene symbols
+if candidate_gene_cols:
+    gene_text = gene_annotation[candidate_gene_cols[0]].astype(str)
+    for col in candidate_gene_cols[1:]:
+        gene_text = gene_text.fillna('') + ' ' + gene_annotation[col].astype(str).fillna('')
+else:
+    # Fallback (unlikely to be useful, but prevents failure if annotation is sparse)
+    gene_text = gene_annotation[probe_col].astype(str)
+
+annotation_for_mapping = gene_annotation[[probe_col]].copy()
+annotation_for_mapping['Gene'] = gene_text
+
+# Create mapping dataframe: ensure proper column names
+mapping_df = annotation_for_mapping.rename(columns={probe_col: 'ID'})[['ID', 'Gene']]
+
+# 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
+
+# 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
+# Fix variable name to use the dataframe created in Step 2
+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 covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_flag = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_flag = trait in selected_clinical_df.index
+
+note = "INFO: Agilent human G3/lncRNA platform; probe-to-gene mapping used regex-based symbol extraction and NCBI synonym normalization, which may yield sparse gene coverage."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Obsessive-Compulsive_Disorder/code/TCGA.py

@@ -0,0 +1,62 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obsessive-Compulsive_Disorder"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/TCGA.csv"
+out_gene_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/clinical_data/TCGA.csv"
+json_path = "./output/z5/preprocess/Obsessive-Compulsive_Disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA subdirectory for the trait (Obsessive-Compulsive_Disorder)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+trait_keywords = {
+    "obsessive", "compulsive", "ocd", "obsessive-compulsive", "obsessive_compulsive",
+    "psychi", "mental", "neuro", "behavior"
+}
+selected_dir = None
+for d in subdirs:
+    name_l = d.lower()
+    if any(k in name_l for k in trait_keywords):
+        selected_dir = d
+        break
+
+if selected_dir is None:
+    print("No TCGA cohort matches the psychiatric trait 'Obsessive-Compulsive Disorder'. Skipping TCGA for this trait.")
+    # Record metadata that this trait is not applicable for TCGA cohorts
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    clinical_df = pd.DataFrame()
+    genetic_df = pd.DataFrame()
+else:
+    print(f"Selected cohort directory: {selected_dir}")
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # Step 2: Identify clinical and genetic data file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+    print(f"Clinical file: {clinical_file_path}")
+    print(f"Genetic file: {genetic_file_path}")
+
+    # Step 3: Load both files
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+    # Step 4: Print clinical columns
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))

output/preprocess/Obsessive-Compulsive_Disorder/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE60190": {
-    "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
-  }
-}
+{"GSE78104": {"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": 60, "note": "INFO: Agilent human G3/lncRNA platform; probe-to-gene mapping used regex-based symbol extraction and NCBI synonym normalization, which may yield sparse gene coverage."}, "GSE60190": {"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": 133, "note": "INFO: Illumina HumanHT-12 v3 platform; DLPFC postmortem samples; trait from 'dx' (OCD vs non-OCD)."}, "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/Obstructive_sleep_apnea/clinical_data/GSE75097.csv

@@ -1,4 +1,4 @@
 ,GSM1942590,GSM1942591,GSM1942592,GSM1942593,GSM1942594,GSM1942595,GSM1942596,GSM1942597,GSM1942598,GSM1942599,GSM1942600,GSM1942601,GSM1942602,GSM1942603,GSM1942604,GSM1942605,GSM1942606,GSM1942607,GSM1942608,GSM1942609,GSM1942610,GSM1942611,GSM1942612,GSM1942613,GSM1942614,GSM1942615,GSM1942616,GSM1942617,GSM1942618,GSM1942619,GSM1942620,GSM1942621,GSM1942622,GSM1942623,GSM1942624,GSM1942625,GSM1942626,GSM1942627,GSM1942628,GSM1942629,GSM1942630,GSM1942631,GSM1942632,GSM1942633,GSM1942634,GSM1942635,GSM1942636,GSM1942637
-Obstructive_sleep_apnea,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0
+Obstructive_sleep_apnea,22.7,32.6,56.5,46.9,31.1,4.5,26.7,56.4,22.6,33.4,98.6,73.5,63.3,44.1,50.2,43.8,63.4,79.2,42.1,24.3,2.4,59.9,73.2,64.9,33.2,45.6,4.3,85.1,28.4,86.5,28.1,8.1,30.3,48.6,3.3,65.7,63.9,80.9,94.8,80.2,73.8,73.0,41.5,2.4,47.0,88.8,91.2,78.3
 Age,54.0,31.0,44.0,60.0,21.0,50.0,52.0,58.0,42.0,34.0,58.0,37.0,60.0,59.0,27.0,57.0,68.0,53.0,58.0,52.0,36.0,38.0,50.0,44.0,58.0,54.0,43.0,59.0,44.0,46.0,36.0,59.0,49.0,59.0,68.0,61.0,38.0,45.0,35.0,57.0,42.0,44.0,47.0,50.0,54.0,50.0,47.0,38.0
 Gender,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0

output/preprocess/Obstructive_sleep_apnea/code/GSE133601.py

@@ -0,0 +1,267 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obstructive_sleep_apnea"
+cohort = "GSE133601"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obstructive_sleep_apnea"
+in_cohort_dir = "../DATA/GEO/Obstructive_sleep_apnea/GSE133601"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/GSE133601.csv"
+out_gene_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/gene_data/GSE133601.csv"
+out_clinical_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/clinical_data/GSE133601.csv"
+json_path = "./output/z5/preprocess/Obstructive_sleep_apnea/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Gene expression data (mRNA) is described for PBMCs; not miRNA/methylation.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics:
+# {0: ['tissue: peripheral blood mononuclear cells'],
+#  1: ['subject: ...'],   # identifiers only
+#  2: ['timepoint: pre-CPAP', 'timepoint: post-CPAP']}  # treatment status, not trait status
+# All participants have OSA; no variability in trait; age and gender not provided.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    # Not available in this dataset; attempt a cautious mapping if ever used elsewhere.
+    val = _after_colon(x).lower()
+    if val in {"osa", "obstructive sleep apnea", "sleep disordered breathing", "sdb", "case", "patient", "osa case"}:
+        return 1
+    if val in {"control", "healthy", "no osa", "non-osa", "non osa"}:
+        return 0
+    # timepoint pre/post CPAP is not a valid proxy for trait presence here
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    m = re.search(r"(\d+(\.\d+)?)", val)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _after_colon(x).lower()
+    if val in {"female", "f"}:
+        return 0
+    if val in {"male", "m"}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (skip because trait_row is None)
+if (trait_row is not None) and ('clinical_data' in globals()):
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# The provided identifiers (e.g., '100009676_at', '10000_at') are Affymetrix probe set IDs, not human gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for probe IDs and gene symbols
+anno_cols = list(gene_annotation.columns)
+
+# Normalize column names for robust matching
+def norm(col):
+    return re.sub(r'[^a-z0-9]', '', col.lower())
+
+norm_map = {norm(c): c for c in anno_cols}
+
+# Probe ID column candidates (prefer 'ID' if present)
+probe_col = None
+for key in ['id', 'idref', 'probesetid', 'probesid', 'probeid']:
+    if key in norm_map:
+        probe_col = norm_map[key]
+        break
+# Fallback: if nothing found, try exact 'ID' if exists
+if probe_col is None and 'ID' in gene_annotation.columns:
+    probe_col = 'ID'
+# As a last resort, use the first column
+if probe_col is None:
+    probe_col = anno_cols[0]
+
+# Gene symbol column candidates
+gene_col = None
+gene_symbol_keys = [
+    'genesymbol', 'symbol', 'gene_symbols', 'genesymbols', 'hgncsymbol',
+    'gene', 'genesym', 'genes', 'geneid', 'geneids', 'genename', 'genenames',
+    'geneassignment', 'geneassignments'
+]
+for key in gene_symbol_keys:
+    if key in norm_map:
+        gene_col = norm_map[key]
+        break
+# Reasonable fallbacks commonly seen in GEO GPLs
+if gene_col is None:
+    for fallback in ['Gene Symbol', 'Gene symbol', 'SYMBOL', 'Symbol', 'GENE_SYMBOL', 'Gene Assignment', 'Gene assignment', 'Gene Assignment']:
+        if fallback in gene_annotation.columns:
+            gene_col = fallback
+            break
+# Final fallback to 'Description' if no explicit symbol column found
+if gene_col is None:
+    gene_col = 'Description' if 'Description' in gene_annotation.columns else anno_cols[-1]
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+# Preserve original probe-level expression before overwriting
+_probe_expr_df = gene_data
+gene_data = apply_gene_mapping(_probe_expr_df, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Initialize linked_data to conform to instruction expectations
+linked_data = None
+
+# 2) Try to get clinical features if available; otherwise skip linking
+clinical_df = None
+if 'selected_clinical_data' in globals():
+    clinical_df = selected_clinical_data
+else:
+    # Attempt to load from previously saved file (if any)
+    if os.path.exists(out_clinical_data_file):
+        clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Determine availability of trait/clinical data
+clinical_available = False
+if clinical_df is not None and isinstance(clinical_df, pd.DataFrame):
+    clinical_available = trait in clinical_df.index
+
+if not clinical_available:
+    # No trait/clinical data available: perform final validation as unavailable
+    note = "INFO: No clinical trait (OSA) fields present; dataset consists of PBMC pre/post-CPAP samples."
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,  # placeholder; will be ignored since trait is unavailable
+        df=normalized_gene_data.T,  # provide a valid-shaped df for consistency
+        note=note
+    )
+else:
+    # 2) Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values (requires the trait column to exist after linking)
+    if trait in linked_data.columns:
+        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 check and save cohort info
+        note = "INFO: Linked data constructed from PBMC pre/post-CPAP; trait present if clinical features exist."
+        is_usable = validate_and_save_cohort_info(
+            is_final=True,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=True,
+            is_trait_available=True,
+            is_biased=is_trait_biased,
+            df=unbiased_linked_data,
+            note=note
+        )
+
+        # 6) Save linked data only if usable
+        if is_usable:
+            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+            unbiased_linked_data.to_csv(out_data_file)
+    else:
+        # Trait column absent after linking; finalize as unavailable
+        note = "INFO: Clinical data found but trait column missing post-linking."
+        _ = validate_and_save_cohort_info(
+            is_final=True,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=True,
+            is_trait_available=False,
+            is_biased=False,
+            df=normalized_gene_data.T,
+            note=note
+        )
+        linked_data = None

output/preprocess/Obstructive_sleep_apnea/code/GSE135917.py

@@ -0,0 +1,221 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obstructive_sleep_apnea"
+cohort = "GSE135917"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obstructive_sleep_apnea"
+in_cohort_dir = "../DATA/GEO/Obstructive_sleep_apnea/GSE135917"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/GSE135917.csv"
+out_gene_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/gene_data/GSE135917.csv"
+out_clinical_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/clinical_data/GSE135917.csv"
+json_path = "./output/z5/preprocess/Obstructive_sleep_apnea/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 Human Gene 1.0 ST microarray -> gene expression data available
+# From the provided sample characteristics, only age (row 0), Sex (row 1), and BMI (row 2) are present.
+# No explicit OSA/control/CPAP status key is provided -> trait not available.
+trait_row = None
+age_row = 0
+gender_row = 1
+
+# Step 2: Define conversion functions
+def _extract_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):
+    # Generic converter for OSA presence if encountered; not used here since trait_row is None.
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    # Positive OSA indicators
+    positive = {"osa", "obstructive sleep apnea", "obstructive sleep apnoea", "sleep apnea", "apnea", "apnoea",
+                "case", "patient", "baseline", "pre-cpap", "pre cpap", "untreated"}
+    # Negative OSA indicators
+    negative = {"control", "healthy", "normal", "none", "no osa", "non-osa", "post-cpap", "post cpap", "treated"}
+    if v in positive:
+        return 1
+    if v in negative:
+        return 0
+    # Heuristics
+    if "osa" in v and ("non" not in v and "no " not in v):
+        return 1
+    if "control" in v or ("no" in v and "osa" in v):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip()
+    if v in {"", "na", "n/a", "nan", "none", "unknown"}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        # Attempt to extract numeric content
+        import re
+        nums = re.findall(r"[-+]?\d*\.?\d+", v)
+        if nums:
+            try:
+                return float(nums[0])
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    if v in {"f", "female", "woman", "women"}:
+        return 0
+    if v in {"m", "male", "man", "men"}:
+        return 1
+    return None
+
+# Step 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
+)
+
+# Step 4: Clinical feature extraction (skip if 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 = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Decide columns: probe IDs in expression match 'ID' in annotation; gene symbols are in 'gene_assignment'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression matrix
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine if trait/clinical data is available from earlier steps
+has_trait = ('trait_row' in globals() and trait_row is not None)
+
+if has_trait:
+    # 2. Link clinical and genetic data
+    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 if 'age_row' in globals() else None,
+        convert_age=convert_age if 'convert_age' in globals() else None,
+        gender_row=gender_row if 'gender_row' in globals() else None,
+        convert_gender=convert_gender if 'convert_gender' in globals() else None
+    )
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Judge bias and remove biased covariates
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked clinical and genetic data with standardized gene symbols."
+    )
+
+    # 6. Save usable linked dataset
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait not available: perform final validation for gene-only availability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,  # Not applicable since trait is unavailable; value ignored in record
+        df=normalized_gene_data.T,  # Non-empty placeholder to pass structural validation
+        note="INFO: Trait not available in sample characteristics; only gene data prepared."
+    )
+    # Do not attempt linking/saving linked data when trait is unavailable

output/preprocess/Obstructive_sleep_apnea/code/GSE49800.py

@@ -0,0 +1,121 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obstructive_sleep_apnea"
+cohort = "GSE49800"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obstructive_sleep_apnea"
+in_cohort_dir = "../DATA/GEO/Obstructive_sleep_apnea/GSE49800"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/GSE49800.csv"
+out_gene_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/gene_data/GSE49800.csv"
+out_clinical_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/clinical_data/GSE49800.csv"
+json_path = "./output/z5/preprocess/Obstructive_sleep_apnea/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  # Affymetrix Human Gene 1.0 ST microarrays => mRNA gene expression data
+
+# 2) Variable availability
+# Trait is Obstructive sleep apnea; all subjects are severe OSA with baseline and post-CPAP only.
+trait_row = None  # No variation in OSA status; not useful for association
+age_row = None    # Not provided in characteristics
+gender_row = None # Not provided in characteristics
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        return s.split(':', 1)[1].strip()
+    return s.strip()
+
+def convert_trait(x):
+    # Binary: 1 = OSA, 0 = non-OSA; map common variants if present
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    # Positive mappings
+    if any(k in v_low for k in ['osa', 'obstructive sleep apnea', 'sleep apnea']):
+        return 1
+    # Negative mappings
+    if any(k in v_low for k in ['control', 'healthy', 'non-osa', 'no osa', 'no sleep apnea', 'normal']):
+        return 0
+    # Unknown
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    m = re.search(r'([-+]?\d*\.?\d+)', v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    if v_low in ['male', 'm', 'man', 'boy']:
+        return 1
+    if v_low in ['female', 'f', 'woman', 'girl']:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row becomes available in future revisions, uncomment the below.
+# 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)
+#     selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Obstructive_sleep_apnea/code/GSE75097.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obstructive_sleep_apnea"
+cohort = "GSE75097"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obstructive_sleep_apnea"
+in_cohort_dir = "../DATA/GEO/Obstructive_sleep_apnea/GSE75097"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/GSE75097.csv"
+out_gene_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/gene_data/GSE75097.csv"
+out_clinical_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/clinical_data/GSE75097.csv"
+json_path = "./output/z5/preprocess/Obstructive_sleep_apnea/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  # Microarray whole-genome expression in PBMC -> gene expression available
+
+# Step 2: Identify rows for variables based on the provided Sample Characteristics Dictionary
+trait_row = 1     # apnea hypopnea index (AHI)
+age_row = 3       # age
+gender_row = 2    # Sex
+
+# Step 2.2: Define conversion functions
+def _after_colon(value):
+    if value is None:
+        return None
+    try:
+        # If value is not a string (already numeric), return as-is
+        if not isinstance(value, str):
+            return value
+        parts = value.split(":", 1)
+        return parts[1].strip() if len(parts) > 1 else value.strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    # Trait: AHI as continuous
+    v = _after_colon(x)
+    try:
+        return float(v)
+    except (TypeError, ValueError):
+        return None
+
+def convert_age(x):
+    # Age as continuous
+    v = _after_colon(x)
+    try:
+        return float(v)
+    except (TypeError, ValueError):
+        return None
+
+def convert_gender(x):
+    # Gender: female -> 0, male -> 1
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {"male", "m", "1"}:
+        return 1
+    if s in {"female", "f", "0"}:
+        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
+    out_dir = os.path.dirname(out_clinical_data_file)
+    os.makedirs(out_dir, exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Ensure the 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)
+
+# 2. Link clinical and genetic data
+# Use the in-memory clinical df if available; otherwise, load from saved file
+if 'selected_clinical_df' not in locals():
+    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 evaluation and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_final = normalized_gene_data.shape[0] > 0
+is_trait_available_final = (trait in linked_data.columns) and (len(linked_data) > 0)
+
+note = "INFO: Trait is continuous (AHI); platform already in gene symbols; gene symbols normalized via NCBI synonyms."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=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/Obstructive_sleep_apnea/code/TCGA.py

@@ -0,0 +1,64 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obstructive_sleep_apnea"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/TCGA.csv"
+out_gene_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z5/preprocess/Obstructive_sleep_apnea/clinical_data/TCGA.csv"
+json_path = "./output/z5/preprocess/Obstructive_sleep_apnea/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import re
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA cohort directory for Obstructive_sleep_apnea using strict token matching
+all_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+sleep_tokens = {'sleep', 'apnea', 'apnoea', 'sleepdisordered', 'sleepdisorder'}
+# 'osa' only counts if accompanied by sleep/apnea terms
+def get_tokens(name: str):
+    return set(re.findall(r'[a-z]+', name.lower()))
+
+def score_dir(name: str) -> int:
+    tokens = get_tokens(name)
+    has_primary = len(tokens & sleep_tokens) > 0
+    has_osa_with_context = ('osa' in tokens) and has_primary
+    return (len(tokens & sleep_tokens)) * 2 + (1 if has_osa_with_context else 0)
+
+scored = [(d, score_dir(d)) for d in all_dirs]
+scored.sort(key=lambda x: x[1], reverse=True)
+selected_dir = scored[0][0] if scored and scored[0][1] > 0 else None
+
+clinical_df = None
+genetic_df = None
+
+if selected_dir is None:
+    print(f"No suitable TCGA cohort found for trait: {trait}. Skipping TCGA for this trait.")
+    # Record: gene data exists in TCGA repository, but no relevant trait data is available
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+else:
+    print(f"Selected cohort: {selected_dir}")
+    # Step 2: Identify clinical and genetic file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+    # Step 4: Print clinical column names
+    print(list(clinical_df.columns))

output/preprocess/Obstructive_sleep_apnea/cohort_info.json

@@ -1,52 +1 @@
-{
-  "GSE75097": {
-    "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": 48
-  },
-  "GSE49800": {
-    "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": 36
-  },
-  "GSE135917": {
-    "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
-  },
-  "GSE133601": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 30
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 566
-  }
-}
+{"GSE75097": {"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": 48, "note": "INFO: Trait is continuous (AHI); platform already in gene symbols; gene symbols normalized via NCBI synonyms."}, "GSE49800": {"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}, "GSE135917": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait not available in sample characteristics; only gene data prepared."}, "GSE133601": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: No clinical trait (OSA) fields present; dataset consists of PBMC pre/post-CPAP samples."}, "TCGA": {"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}}

output/preprocess/Ocular_Melanomas/clinical_data/GSE60464.csv

@@ -1,2 +1,2 @@
 ,GSM1480415,GSM1480416,GSM1480417,GSM1480418,GSM1480419,GSM1480420,GSM1480421,GSM1480422,GSM1480423,GSM1480424,GSM1480425,GSM1480426,GSM1480427,GSM1480428,GSM1480429,GSM1480430,GSM1480431,GSM1480432,GSM1480433,GSM1480434,GSM1480435,GSM1480436,GSM1480437,GSM1480438,GSM1480439,GSM1480440,GSM1480441,GSM1480442,GSM1480443,GSM1480444,GSM1480445,GSM1480446,GSM1480447,GSM1480448,GSM1480449,GSM1480450,GSM1480451,GSM1480452,GSM1480453,GSM1480454,GSM1480455,GSM1480456
-Ocular_Melanomas,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Ocular_Melanomas,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,,0.0,1.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0

output/preprocess/Ocular_Melanomas/clinical_data/TCGA.csv

@@ -0,0 +1,81 @@
+sampleID,Ocular_Melanomas,Age,Gender
+TCGA-RZ-AB0B-01,1,47,0
+TCGA-V3-A9ZX-01,1,56,1
+TCGA-V3-A9ZY-01,1,54,1
+TCGA-V4-A9E5-01,1,51,0
+TCGA-V4-A9E7-01,1,76,1
+TCGA-V4-A9E8-01,1,49,1
+TCGA-V4-A9E9-01,1,50,1
+TCGA-V4-A9EA-01,1,66,1
+TCGA-V4-A9EC-01,1,75,0
+TCGA-V4-A9ED-01,1,45,1
+TCGA-V4-A9EE-01,1,86,1
+TCGA-V4-A9EF-01,1,56,1
+TCGA-V4-A9EH-01,1,53,1
+TCGA-V4-A9EI-01,1,78,1
+TCGA-V4-A9EJ-01,1,38,1
+TCGA-V4-A9EK-01,1,37,0
+TCGA-V4-A9EL-01,1,60,1
+TCGA-V4-A9EM-01,1,53,1
+TCGA-V4-A9EO-01,1,74,1
+TCGA-V4-A9EQ-01,1,64,1
+TCGA-V4-A9ES-01,1,54,0
+TCGA-V4-A9ET-01,1,57,1
+TCGA-V4-A9EU-01,1,83,0
+TCGA-V4-A9EV-01,1,59,0
+TCGA-V4-A9EW-01,1,44,0
+TCGA-V4-A9EX-01,1,55,0
+TCGA-V4-A9EY-01,1,66,0
+TCGA-V4-A9EZ-01,1,78,0
+TCGA-V4-A9F0-01,1,59,1
+TCGA-V4-A9F1-01,1,46,0
+TCGA-V4-A9F2-01,1,78,0
+TCGA-V4-A9F3-01,1,49,0
+TCGA-V4-A9F4-01,1,41,1
+TCGA-V4-A9F5-01,1,85,0
+TCGA-V4-A9F7-01,1,78,0
+TCGA-V4-A9F8-01,1,68,1
+TCGA-VD-A8K7-01,1,39,1
+TCGA-VD-A8K8-01,1,56,0
+TCGA-VD-A8K9-01,1,71,0
+TCGA-VD-A8KA-01,1,22,1
+TCGA-VD-A8KB-01,1,66,0
+TCGA-VD-A8KD-01,1,72,1
+TCGA-VD-A8KE-01,1,74,0
+TCGA-VD-A8KF-01,1,68,1
+TCGA-VD-A8KG-01,1,47,0
+TCGA-VD-A8KH-01,1,69,1
+TCGA-VD-A8KI-01,1,68,1
+TCGA-VD-A8KJ-01,1,53,1
+TCGA-VD-A8KK-01,1,54,1
+TCGA-VD-A8KL-01,1,77,0
+TCGA-VD-A8KM-01,1,65,1
+TCGA-VD-A8KN-01,1,60,0
+TCGA-VD-A8KO-01,1,51,1
+TCGA-VD-AA8M-01,1,64,1
+TCGA-VD-AA8N-01,1,86,1
+TCGA-VD-AA8O-01,1,77,1
+TCGA-VD-AA8P-01,1,64,0
+TCGA-VD-AA8Q-01,1,71,1
+TCGA-VD-AA8R-01,1,78,0
+TCGA-VD-AA8S-01,1,40,1
+TCGA-VD-AA8T-01,1,83,0
+TCGA-WC-A87T-01,1,64,1
+TCGA-WC-A87U-01,1,73,1
+TCGA-WC-A87W-01,1,57,0
+TCGA-WC-A87Y-01,1,59,1
+TCGA-WC-A880-01,1,63,1
+TCGA-WC-A881-01,1,75,1
+TCGA-WC-A882-01,1,50,0
+TCGA-WC-A883-01,1,76,0
+TCGA-WC-A884-01,1,35,0
+TCGA-WC-A885-01,1,60,1
+TCGA-WC-A888-01,1,76,1
+TCGA-WC-A88A-01,1,75,1
+TCGA-WC-AA9A-01,1,70,0
+TCGA-WC-AA9E-01,1,60,1
+TCGA-YZ-A980-01,1,75,1
+TCGA-YZ-A982-01,1,79,0
+TCGA-YZ-A983-01,1,51,0
+TCGA-YZ-A984-01,1,50,0
+TCGA-YZ-A985-01,1,41,0

output/preprocess/Ocular_Melanomas/code/GSE60464.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ocular_Melanomas"
+cohort = "GSE60464"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ocular_Melanomas"
+in_cohort_dir = "../DATA/GEO/Ocular_Melanomas/GSE60464"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ocular_Melanomas/GSE60464.csv"
+out_gene_data_file = "./output/z5/preprocess/Ocular_Melanomas/gene_data/GSE60464.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ocular_Melanomas/clinical_data/GSE60464.csv"
+json_path = "./output/z5/preprocess/Ocular_Melanomas/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Based on series design: expression profiles of ~9,829 unique genes
+
+# 2) Variable availability and conversion functions
+# Trait (Ocular_Melanomas): infer from "location of primary melanoma"
+trait_row = 5  # 'location of primary melanoma' with values including 'Ocular'
+age_row = None
+gender_row = None
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    s = str(value)
+    # Take the substring after the last colon to avoid retaining the header
+    if ":" in s:
+        s = s.split(":")[-1]
+    return s.strip()
+
+def convert_trait(value):
+    s = _after_colon(value).lower()
+    if s in {"", "unknown", "na", "n/a", "none"}:
+        return None
+    # Map ocular primary site to 1, others to 0
+    return 1 if "ocular" in s else 0
+
+def convert_age(value):
+    s = _after_colon(value).lower()
+    if s in {"", "unknown", "na", "n/a", "none"}:
+        return None
+    m = re.search(r'\d+(\.\d+)?', s)
+    return float(m.group()) if m else None
+
+def convert_gender(value):
+    s = _after_colon(value).lower()
+    if s in {"", "unknown", "na", "n/a", "none"}:
+        return None
+    if s in {"female", "f", "woman", "women"}:
+        return 0
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (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 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
+# Identify the appropriate columns for probe IDs and gene symbols in the annotation
+id_col = 'ID'
+candidate_symbol_cols = ['Symbol', 'ILMN_Gene', 'Search_Key']
+
+# Try mapping using candidate symbol columns until a non-empty mapping result is obtained
+mapped_gene_data = None
+for gene_col in candidate_symbol_cols:
+    if gene_col not in gene_annotation.columns:
+        continue
+    mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+    try:
+        candidate = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+        if candidate is not None and candidate.shape[0] > 0:
+            mapped_gene_data = candidate
+            break
+    except Exception:
+        continue
+
+# Fallback: if all attempts failed, raise an error
+if mapped_gene_data is None or mapped_gene_data.shape[0] == 0:
+    raise ValueError("Failed to map probe IDs to gene symbols using available annotation columns.")
+
+# Use the mapped gene expression data as gene_data
+gene_data = mapped_gene_data
+
+# 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. 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_flag = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_flag = (trait in selected_clinical_df.index) and (not selected_clinical_df.loc[trait].isna().all())
+note = "INFO: Trait inferred from 'location of primary melanoma'; Age/Gender unavailable in series characteristics."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Ocular_Melanomas/code/GSE78033.py

@@ -0,0 +1,214 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ocular_Melanomas"
+cohort = "GSE78033"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ocular_Melanomas"
+in_cohort_dir = "../DATA/GEO/Ocular_Melanomas/GSE78033"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ocular_Melanomas/GSE78033.csv"
+out_gene_data_file = "./output/z5/preprocess/Ocular_Melanomas/gene_data/GSE78033.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ocular_Melanomas/clinical_data/GSE78033.csv"
+json_path = "./output/z5/preprocess/Ocular_Melanomas/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 (Affymetrix Human Exon 1.0 ST Array => mRNA expression)
+is_gene_available = True
+
+# 2) Variable availability (from provided Sample Characteristics Dictionary)
+# Trait is "Ocular_Melanomas" (all samples are melanoma; no healthy controls), so not variable here.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Binary: 1 = Ocular melanoma present; 0 = control/normal. Here not used (trait_row=None).
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Positive melanoma indicators
+    pos_tokens = [
+        "uveal melanoma", "ocular melanoma", "primary ocular tumor",
+        "metastasis", "primary tumor", "xenograft", "pdx", "tumor"
+    ]
+    if any(t in v for t in pos_tokens):
+        return 1
+    # Negative/control indicators
+    neg_tokens = ["control", "normal", "healthy", "benign", "adjacent normal"]
+    if any(t in v for t in neg_tokens):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years if present; else None
+    val = _after_colon(x)
+    if val is None:
+        return None
+    m = re.search(r"(\d+(?:\.\d+)?)", val)
+    if not m:
+        return None
+    try:
+        num = float(m.group(1))
+        return num
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female->0, male->1
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"female", "f"}:
+        return 0
+    if v in {"male", "m"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip if 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)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping based on the preview:
+# - Probe/identifier column in annotation: 'ID' (e.g., '10000_at')
+# - Gene symbol column in annotation: 'SPOT_ID' (e.g., 'AKT3')
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID')
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize 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)
+
+# 2-6. Branch depending on availability of clinical features from earlier steps
+linked_data = None
+has_clinical = ('selected_clinical_data' in globals() and
+                isinstance(selected_clinical_data, pd.DataFrame) and
+                not selected_clinical_data.empty)
+
+if has_clinical:
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias assessment and removal of biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    note = "INFO: Clinical features linked with gene expression; missing values handled; demographic biases addressed."
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # No trait/clinical data available (trait_row was None in Step 2)
+    note = "INFO: Trait/clinical data unavailable (trait_row=None). Skipping linking; saved normalized gene data only."
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=normalized_gene_data.T,  # pass gene data for record; not used for availability
+        note=note
+    )

output/preprocess/Ocular_Melanomas/code/TCGA.py

@@ -0,0 +1,287 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ocular_Melanomas"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ocular_Melanomas/TCGA.csv"
+out_gene_data_file = "./output/z5/preprocess/Ocular_Melanomas/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ocular_Melanomas/clinical_data/TCGA.csv"
+json_path = "./output/z5/preprocess/Ocular_Melanomas/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Select the most relevant TCGA cohort directory for Ocular Melanomas
+selected_dir_name = 'TCGA_Ocular_melanomas_(UVM)'
+cohort_dir = os.path.join(tcga_root_dir, selected_dir_name)
+
+# Identify clinical and genetic file paths
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load dataframes
+clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+# Print clinical column names for inspection
+print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import re
+import pandas as pd
+
+# Identify the cohort directory for UVM within the TCGA root directory
+cohort_dirs = [
+    os.path.join(tcga_root_dir, d)
+    for d in os.listdir(tcga_root_dir)
+    if os.path.isdir(os.path.join(tcga_root_dir, d))
+]
+uvm_dirs = [d for d in cohort_dirs if 'UVM' in os.path.basename(d).upper()]
+cohort_dir = uvm_dirs[0] if uvm_dirs else (cohort_dirs[0] if cohort_dirs else None)
+
+# Load clinical data
+clinical_df = pd.DataFrame()
+if cohort_dir is not None:
+    try:
+        clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+        clinical_df = pd.read_csv(clinical_file_path, sep='\t', header=0, index_col=0, dtype=str)
+    except Exception:
+        clinical_df = pd.DataFrame()
+
+# Determine candidate columns for age and gender from available columns
+columns = clinical_df.columns.tolist() if not clinical_df.empty else [
+    '_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site',
+    'additional_pharmaceutical_therapy', 'additional_radiation_therapy',
+    'age_at_initial_pathologic_diagnosis', 'bcr_followup_barcode', 'bcr_patient_barcode',
+    'bcr_sample_barcode', 'clinical_M', 'clinical_N', 'clinical_T', 'clinical_stage',
+    'cytogenetic_abnormality', 'days_to_birth', 'days_to_collection', 'days_to_death',
+    'days_to_initial_pathologic_diagnosis', 'days_to_last_followup',
+    'days_to_new_tumor_event_after_initial_treatment', 'extranocular_nodule_size',
+    'extrascleral_extension', 'extravascular_matrix_patterns', 'eye_color',
+    'form_completion_date', 'gender', 'gene_expression_profile', 'height',
+    'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10',
+    'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified',
+    'initial_pathologic_diagnosis_method', 'initial_weight', 'is_ffpe', 'lost_follow_up',
+    'metastatic_site', 'mitotic_count',
+    'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type',
+    'new_neoplasm_occurrence_anatomic_site_text',
+    'new_tumor_event_additional_surgery_procedure',
+    'new_tumor_event_after_initial_treatment', 'oct_embedded', 'other_dx',
+    'other_metastatic_site', 'pathologic_M', 'pathologic_N', 'pathologic_T',
+    'pathologic_stage', 'pathology_report_file_name', 'patient_death_reason',
+    'patient_id', 'person_neoplasm_cancer_status', 'postoperative_rx_tx',
+    'radiation_therapy', 'sample_type', 'sample_type_id', 'system_version',
+    'tissue_prospective_collection_indicator',
+    'tissue_retrospective_collection_indicator', 'tissue_source_site',
+    'tumor_basal_diameter', 'tumor_basal_diameter_mx',
+    'tumor_infiltrating_lymphocytes', 'tumor_infiltrating_macrophages',
+    'tumor_morphology_percentage', 'tumor_shape_pathologic_clinical',
+    'tumor_thickness', 'tumor_thickness_measurement', 'tumor_tissue_site',
+    'vial_number', 'vital_status', 'weight',
+    'year_of_initial_pathologic_diagnosis',
+    '_GENOMIC_ID_TCGA_UVM_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_UVM_gistic2thd',
+    '_GENOMIC_ID_TCGA_UVM_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_UVM_miRNA_HiSeq',
+    '_GENOMIC_ID_TCGA_UVM_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_UVM_exp_HiSeqV2',
+    '_GENOMIC_ID_TCGA_UVM_gistic2', '_GENOMIC_ID_TCGA_UVM_exp_HiSeqV2_percentile',
+    '_GENOMIC_ID_TCGA_UVM_mutation_bcm_gene', '_GENOMIC_ID_TCGA_UVM_hMethyl450',
+    '_GENOMIC_ID_TCGA_UVM_mutation_ucsc_maf_gene',
+    '_GENOMIC_ID_TCGA_UVM_mutation_broad_gene', '_GENOMIC_ID_TCGA_UVM_RPPA',
+    '_GENOMIC_ID_TCGA_UVM_exp_HiSeqV2_exon',
+    '_GENOMIC_ID_TCGA_UVM_mutation_curated_broad_gene',
+    '_GENOMIC_ID_TCGA_UVM_PDMRNAseq', '_GENOMIC_ID_data/public/TCGA/UVM/miRNA_HiSeq_gene'
+]
+
+def is_age_col(name: str) -> bool:
+    n = name.lower()
+    if n in ('days_to_birth', 'age', 'age_at_diagnosis'):
+        return True
+    if 'age_at' in n:
+        return True
+    if n.startswith('age_') or n.startswith('ageat'):
+        return True
+    return False
+
+def is_gender_col(name: str) -> bool:
+    n = name.lower()
+    return n in ('gender', 'sex')
+
+candidate_age_cols = [c for c in columns if is_age_col(c)]
+candidate_gender_cols = [c for c in columns if is_gender_col(c)]
+
+# Print the required lists in the exact format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Extract and preview candidate columns, if clinical data is available
+preview_dict = {}
+if not clinical_df.empty:
+    selected_cols = [c for c in (candidate_age_cols + candidate_gender_cols) if c in clinical_df.columns]
+    if selected_cols:
+        preview_dict = preview_df(clinical_df[selected_cols], n=5)
+
+# Display the preview dictionary
+print(preview_dict)
+
+# Step 3: Select Demographic Features
+# Select demographic feature columns based on candidate lists and value semantics.
+# Prefer 'age_at_initial_pathologic_diagnosis' over 'days_to_birth' because tcga_convert_age extracts digits,
+# which would misinterpret negative days as a large positive age.
+try:
+    # Default to None
+    age_col = None
+    gender_col = None
+
+    # Choose age column
+    if 'candidate_age_cols' in globals() and isinstance(candidate_age_cols, list) and len(candidate_age_cols) > 0:
+        if 'age_at_initial_pathologic_diagnosis' in candidate_age_cols:
+            age_col = 'age_at_initial_pathologic_diagnosis'
+        elif len(candidate_age_cols) == 1:
+            age_col = candidate_age_cols[0]
+        else:
+            # Fallback heuristic: pick the first that contains 'age'
+            age_like = [c for c in candidate_age_cols if 'age' in c.lower()]
+            age_col = age_like[0] if age_like else None
+
+    # Choose gender column
+    if 'candidate_gender_cols' in globals() and isinstance(candidate_gender_cols, list) and len(candidate_gender_cols) > 0:
+        if 'gender' in candidate_gender_cols:
+            gender_col = 'gender'
+        elif len(candidate_gender_cols) == 1:
+            gender_col = candidate_gender_cols[0]
+        else:
+            # Fallback heuristic: pick the first that contains 'gender' or 'sex'
+            g_like = [c for c in candidate_gender_cols if ('gender' in c.lower() or 'sex' in c.lower())]
+            gender_col = g_like[0] if g_like else None
+
+    print(f"Selected age_col: {age_col}")
+    print(f"Selected gender_col: {gender_col}")
+
+    # Try to print the first 5 preview values if an appropriate preview dictionary exists in the environment
+    def try_print_preview(col_name: str, label: str):
+        if not col_name:
+            print(f"No {label} column selected; preview unavailable.")
+            return
+        preview_found = False
+        for v in globals().values():
+            if isinstance(v, dict) and col_name in v and isinstance(v[col_name], list):
+                print(f"Preview values for {label} ({col_name}): {v[col_name]}")
+                preview_found = True
+                break
+        if not preview_found:
+            print(f"No preview values found in environment for {label} ({col_name}).")
+
+    try_print_preview(age_col, "age_col")
+    try_print_preview(gender_col, "gender_col")
+
+except Exception as e:
+    # Ensure variables exist even if something unexpected happens
+    age_col = None
+    gender_col = None
+    print("Selected age_col: None")
+    print("Selected gender_col: None")
+    print(f"Note: Encountered an exception during selection: {e}")
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# Ensure clinical and genetic data are loaded
+try:
+    clinical_df
+except NameError:
+    clinical_df = pd.DataFrame()
+
+try:
+    genetic_df
+except NameError:
+    genetic_df = pd.DataFrame()
+
+# Locate UVM cohort directory if needed
+if clinical_df.empty or genetic_df.empty or 'cohort_dir' not in globals() or cohort_dir is None:
+    cohort_dirs = [
+        os.path.join(tcga_root_dir, d)
+        for d in os.listdir(tcga_root_dir)
+        if os.path.isdir(os.path.join(tcga_root_dir, d))
+    ]
+    uvm_dirs = [d for d in cohort_dirs if 'UVM' in os.path.basename(d).upper()]
+    cohort_dir = uvm_dirs[0] if uvm_dirs else None
+    if cohort_dir is not None:
+        clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+        if clinical_df.empty:
+            clinical_df = pd.read_csv(clinical_file_path, sep='\t', header=0, index_col=0, low_memory=False)
+        if genetic_df.empty:
+            genetic_df = pd.read_csv(genetic_file_path, sep='\t', header=0, index_col=0, low_memory=False)
+
+# Guard selected demographic columns from previous step
+try:
+    age_col
+except NameError:
+    age_col = None
+try:
+    gender_col
+except NameError:
+    gender_col = None
+
+# 1) Extract and standardize clinical features (trait, Age, Gender)
+selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col=age_col, gender_col=gender_col)
+
+# Optional: save clinical subset for transparency
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 2) Normalize gene symbols and save normalized gene matrix
+gene_df_norm = normalize_gene_symbols_in_index(genetic_df.copy())
+gene_df_norm = gene_df_norm.apply(pd.to_numeric, errors='coerce')
+
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_df_norm.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on sample IDs
+gene_df_T = gene_df_norm.T
+common_ids = selected_clinical_df.index.intersection(gene_df_T.index)
+linked_data = pd.concat([selected_clinical_df.loc[common_ids], gene_df_T.loc[common_ids]], axis=1)
+
+# 4) Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5) Determine bias and remove biased demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Prepare cohort name and basic availability flags, cast to builtin types to avoid JSON issues
+cohort_name = os.path.basename(cohort_dir) if (cohort_dir is not None) else "TCGA_Ocular_melanomas_(UVM)"
+is_gene_available = bool(gene_df_norm.shape[0] > 0 and gene_df_norm.shape[1] > 0)
+is_trait_available = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+trait_biased = bool(trait_biased)
+
+# Prepare note (ensure plain string)
+note = ""
+if trait in linked_data.columns:
+    counts = linked_data[trait].value_counts(dropna=False).to_dict()
+    note = f"INFO: Trait distribution after preprocessing: {counts}."
+    if trait_biased:
+        note += " WARNING: Trait appears highly imbalanced; TCGA UVM typically lacks normal samples."
+note = str(note)
+
+# 6) Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort_name,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

output/preprocess/Ocular_Melanomas/cohort_info.json

@@ -1,32 +1 @@
-{
-  "GSE78033": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 45
-  },
-  "GSE60464": {
-    "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": 42
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 80
-  }
-}
+{"GSE78033": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait/clinical data unavailable (trait_row=None). Skipping linking; saved normalized gene data only."}, "GSE60464": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 37, "note": "INFO: Trait inferred from 'location of primary melanoma'; Age/Gender unavailable in series characteristics."}, "TCGA_Ocular_melanomas_(UVM)": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 80, "note": "INFO: Trait distribution after preprocessing: {1: 80}. WARNING: Trait appears highly imbalanced; TCGA UVM typically lacks normal samples."}}

output/preprocess/Osteoarthritis/clinical_data/GSE107105.csv

@@ -0,0 +1,4 @@
+,GSM2861345,GSM2861346,GSM2861347,GSM2861348,GSM2861349,GSM2861350,GSM2861351,GSM2861352,GSM2861353,GSM2861354,GSM2861355,GSM2861356,GSM2861357,GSM2861358,GSM2861359,GSM2861360,GSM2861361,GSM2861362,GSM2861363,GSM2861364,GSM2861365,GSM2861366,GSM2861367,GSM2861368,GSM2861369,GSM2861370,GSM2861371,GSM2861372,GSM2861373,GSM2861374,GSM2861375,GSM2861376,GSM2861377,GSM2861378,GSM2861379,GSM2861380
+Osteoarthritis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,59.0,59.0,59.0,59.0,59.0,78.0,78.0,78.0,78.0,78.0,86.0,86.0,86.0,86.0,86.0,51.0,51.0,51.0,51.0,51.0,51.0,79.0,79.0,79.0,79.0,79.0,79.0,79.0,79.0,48.0,48.0,48.0,48.0,48.0,48.0,48.0
+Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Osteoarthritis/clinical_data/GSE141934.csv

@@ -0,0 +1,4 @@
+,GSM4216498,GSM4216499,GSM4216500,GSM4216501,GSM4216502,GSM4216503,GSM4216504,GSM4216505,GSM4216506,GSM4216507,GSM4216508,GSM4216509,GSM4216510,GSM4216511,GSM4216512,GSM4216513,GSM4216514,GSM4216515,GSM4216516,GSM4216517,GSM4216518,GSM4216519,GSM4216520,GSM4216521,GSM4216522,GSM4216523,GSM4216524,GSM4216525,GSM4216526,GSM4216527,GSM4216528,GSM4216529,GSM4216530,GSM4216531,GSM4216532,GSM4216533,GSM4216534,GSM4216535,GSM4216536,GSM4216537,GSM4216538,GSM4216539,GSM4216540,GSM4216541,GSM4216542,GSM4216543,GSM4216544,GSM4216545,GSM4216546,GSM4216547,GSM4216548,GSM4216549,GSM4216550,GSM4216551,GSM4216552,GSM4216553,GSM4216554,GSM4216555,GSM4216556,GSM4216557,GSM4216558,GSM4216559,GSM4216560,GSM4216561,GSM4216562,GSM4216563,GSM4216564,GSM4216565,GSM4216566,GSM4216567,GSM4216568,GSM4216569,GSM4216570,GSM4216571,GSM4216572,GSM4216573,GSM4216574,GSM4216575,GSM4216576,GSM4216577,GSM4216578,GSM4216579,GSM4216580,GSM4216581,GSM4216582,GSM4216583,GSM4216584,GSM4216585,GSM4216586,GSM4216587,GSM4216588,GSM4216589,GSM4216590,GSM4216591,GSM4216592,GSM4216593,GSM4216594,GSM4216595,GSM4216596,GSM4216597
+Osteoarthritis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,50.0,43.0,66.0,55.0,52.0,54.0,63.0,61.0,58.0,79.0,69.0,57.0,46.0,44.0,46.0,63.0,59.0,81.0,60.0,92.0,45.0,47.0,27.0,58.0,57.0,38.0,45.0,51.0,70.0,57.0,56.0,56.0,51.0,50.0,53.0,61.0,66.0,74.0,51.0,46.0,49.0,56.0,58.0,60.0,50.0,50.0,31.0,70.0,52.0,65.0,69.0,73.0,50.0,58.0,27.0,68.0,22.0,39.0,52.0,35.0,69.0,70.0,74.0,38.0,80.0,51.0,56.0,68.0,50.0,74.0,45.0,65.0,53.0,57.0,73.0,74.0,53.0,67.0,49.0,27.0,54.0,26.0,56.0,30.0,50.0,69.0,79.0,61.0,63.0,77.0,48.0,61.0,43.0,54.0,62.0,20.0,62.0,50.0,60.0,69.0
+Gender,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Osteoarthritis/clinical_data/GSE56409.csv

@@ -1,2 +1,2 @@
-Sample_1,Sample_2,Sample_3,Sample_4
-,,,
+,GSM1360956,GSM1360957,GSM1360958,GSM1360959,GSM1360960,GSM1360961,GSM1360962,GSM1360963,GSM1360964,GSM1360965,GSM1360966,GSM1360967,GSM1360968,GSM1360969,GSM1360970,GSM1360971,GSM1360972,GSM1360973,GSM1360974,GSM1360975,GSM1360976,GSM1360977,GSM1360978,GSM1360979,GSM1360980,GSM1360981,GSM1360982,GSM1360983,GSM1360984,GSM1360985,GSM1360986,GSM1360987,GSM1360988,GSM1360989,GSM1360990,GSM1360991,GSM1360992,GSM1360993,GSM1360994,GSM1360995,GSM1360996,GSM1360997,GSM1360998,GSM1360999,GSM1361000,GSM1361001,GSM1361002,GSM1361003,GSM1361004,GSM1361005,GSM1361006,GSM1361007,GSM1361008,GSM1361009,GSM1361010,GSM1361011,GSM1361012,GSM1361013,GSM1361014,GSM1361015,GSM1361016,GSM1361017,GSM1361018,GSM1361019,GSM1361020,GSM1361021,GSM1361022,GSM1361023,GSM1361024,GSM1361025,GSM1361026,GSM1361027,GSM1361028,GSM1361029,GSM1361030,GSM1361031,GSM1361032,GSM1361033,GSM1361034,GSM1361035,GSM1361036,GSM1361037,GSM1361038,GSM1361039,GSM1361040,GSM1361041,GSM1361042,GSM1361043,GSM1361044,GSM1361045,GSM1361046,GSM1361047,GSM1361048,GSM1361049,GSM1361050,GSM1361051,GSM1361052,GSM1361053,GSM1361054,GSM1361055,GSM1361056,GSM1361057
+Osteoarthritis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Osteoarthritis/clinical_data/GSE93698.csv

@@ -1,4 +1,4 @@
-,GSM2460692,GSM2460693,GSM2460694,GSM2460695,GSM2460696,GSM2460733,GSM2460734,GSM2460735,GSM2460736,GSM2460737,GSM2460738,GSM2460739,GSM2460740,GSM2460741,GSM2460742,GSM2460743,GSM2460744,GSM2460745,GSM2460746,GSM2460747,GSM2460748,GSM2460749,GSM2460750,GSM2460751,GSM2460752,GSM2460753,GSM2460754,GSM2460755,GSM2460756,GSM2460757
-Osteoarthritis,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-Age,,,,,,71.0,73.0,65.0,51.0,56.0,52.0,42.0,43.0,69.0,55.0,62.0,37.0,38.0,19.0,40.0,31.0,45.0,55.0,72.0,42.0,72.0,28.0,39.0,47.0,21.0
-Gender,,,,,,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0
+GSM2460692,GSM2460693,GSM2460694,GSM2460695,GSM2460696,GSM2460733,GSM2460734,GSM2460735,GSM2460736,GSM2460737,GSM2460738,GSM2460739,GSM2460740,GSM2460741,GSM2460742,GSM2460743,GSM2460744,GSM2460745,GSM2460746,GSM2460747,GSM2460748,GSM2460749,GSM2460750,GSM2460751,GSM2460752,GSM2460753,GSM2460754,GSM2460755,GSM2460756,GSM2460757
+0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+,,,,,71.0,73.0,65.0,51.0,56.0,52.0,42.0,43.0,69.0,55.0,62.0,37.0,38.0,19.0,40.0,31.0,45.0,55.0,72.0,42.0,72.0,28.0,39.0,47.0,21.0
+,,,,,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0

output/preprocess/Osteoarthritis/clinical_data/GSE93720.csv

@@ -1,2 +1,2 @@
-GSM3713911,GSM3713912,GSM3713913,GSM3713914,GSM3713915,GSM3713916,GSM3713917,GSM3713918,GSM3713919,GSM3713920,GSM3713921,GSM3713922,GSM3713923,GSM3713924,GSM3713925,GSM3713926,GSM3713927,GSM3713928,GSM3713929,GSM3713930,GSM3713931,GSM3713932,GSM3713933,GSM3713934,GSM3713935,GSM3713936,GSM3713937,GSM3713938,GSM3713939,GSM3713940,GSM3713941,GSM3713942,GSM3713943,GSM3713944,GSM3713945,GSM3713946,GSM3713947,GSM3713948,GSM3713949,GSM3713950,GSM3713951,GSM3713952,GSM3713953,GSM3713954,GSM3713955,GSM3713956,GSM3713957,GSM3713958
-1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0
+,GSM3713911,GSM3713912,GSM3713913,GSM3713914,GSM3713915,GSM3713916,GSM3713917,GSM3713918,GSM3713919,GSM3713920,GSM3713921,GSM3713922,GSM3713923,GSM3713924,GSM3713925,GSM3713926,GSM3713927,GSM3713928,GSM3713929,GSM3713930,GSM3713931,GSM3713932,GSM3713933,GSM3713934,GSM3713935,GSM3713936,GSM3713937,GSM3713938,GSM3713939,GSM3713940,GSM3713941,GSM3713942,GSM3713943,GSM3713944,GSM3713945,GSM3713946,GSM3713947,GSM3713948,GSM3713949,GSM3713950,GSM3713951,GSM3713952,GSM3713953,GSM3713954,GSM3713955,GSM3713956,GSM3713957,GSM3713958
+Osteoarthritis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0

output/preprocess/Osteoarthritis/code/GSE107105.py

@@ -0,0 +1,397 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE107105"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE107105"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE107105.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE107105.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE107105.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/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 transcriptomics of synovial fibroblasts (not miRNA-only or methylation)
+
+# 2) Variable availability and conversion functions
+# Sample Characteristics Dictionary review indicates:
+# - trait (Osteoarthritis vs RA): key 0
+# - age: key 1
+# - gender: key 2
+
+trait_row = 0
+age_row = 1
+gender_row = 2
+
+def _after_colon(s):
+    if s is None:
+        return None
+    parts = str(s).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(s).strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map OA (trait present) -> 1, RA (other disease) -> 0
+    if "oa" in vl or "osteo" in vl:
+        return 1
+    if "ra" in vl or "rheum" in vl:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v = v.strip()
+    # Extract numeric part (allow decimals)
+    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 = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"female", "f"}:
+        return 0
+    if vl in {"male", "m"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait_row is not 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)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Inspect available annotation columns
+anno_cols = list(gene_annotation.columns)
+print("Annotation columns (first 50):", anno_cols[:50])
+
+probe_id_col = 'ID'
+assert probe_id_col in gene_annotation.columns, "Probe ID column 'ID' not found in gene annotation."
+
+# Exclude obvious non-gene-symbol columns to avoid spurious tokens (e.g., 'NC' from RANGE_GB)
+excluded_cols = {
+    probe_id_col,
+    'RANGE_GB', 'SPOT_ID', 'RANGE_STRAND', 'RANGE_START', 'RANGE_END', 'total_probes',
+    'GB_ACC'  # RefSeq accessions; handled separately in fallback
+}
+candidate_cols = [c for c in gene_annotation.columns if c not in excluded_cols]
+
+best_col = None
+best_rows_with_symbols = -1
+best_total_symbols = -1
+
+# Heuristic scan of candidate columns for plausible human gene symbols
+for col in candidate_cols:
+    series = gene_annotation[col].astype(str)
+    subset = series.head(50000)
+    extracted = subset.map(extract_human_gene_symbols)
+    rows_with_symbols = extracted.map(lambda x: len(x) > 0).sum()
+    total_symbols = extracted.map(len).sum()
+    if (rows_with_symbols > best_rows_with_symbols) or (
+        rows_with_symbols == best_rows_with_symbols and total_symbols > best_total_symbols
+    ):
+        best_rows_with_symbols = rows_with_symbols
+        best_total_symbols = total_symbols
+        best_col = col
+
+# Fallback to a few common names if nothing suitable was detected
+if best_rows_with_symbols <= 0 or best_col is None:
+    for fallback in ['gene_assignment', 'Gene Symbol', 'GENE_SYMBOL', 'GeneSymbol', 'Symbol', 'SYMBOL', 'Gene', 'GENE', 'Entrez Gene Symbol']:
+        if fallback in gene_annotation.columns and fallback not in excluded_cols:
+            best_col = fallback
+            best_rows_with_symbols = 1  # mark as found
+            break
+
+probe_level_df = gene_data.copy()
+
+if best_rows_with_symbols > 0 and best_col is not None:
+    # Symbol-bearing column found: proceed with standard mapping
+    print(f"Selected gene symbol column: {best_col}")
+    mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=best_col)
+    gene_data = apply_gene_mapping(probe_level_df, mapping_df)
+    # Normalize gene symbols and aggregate duplicates
+    gene_data = normalize_gene_symbols_in_index(gene_data)
+else:
+    # Robust fallback: use RefSeq accessions (GB_ACC) as gene identifiers
+    if 'GB_ACC' not in gene_annotation.columns or gene_annotation['GB_ACC'].notna().sum() == 0:
+        # Last-resort fallback: keep probe-level if no mapping information at all
+        print("WARNING: No gene symbol column and no GB_ACC found in annotation. Proceeding with probe-level data.")
+        gene_data = probe_level_df
+    else:
+        print("Falling back to RefSeq GB_ACC mapping as gene identifiers.")
+        # Build mapping from probe ID to RefSeq accession(s)
+        mapping_df = gene_annotation.loc[:, [probe_id_col, 'GB_ACC']].dropna().rename(
+            columns={probe_id_col: 'ID', 'GB_ACC': 'Gene'}
+        )
+        mapping_df['ID'] = mapping_df['ID'].astype(str).str.strip()
+        mapping_df = mapping_df[mapping_df['ID'].isin(probe_level_df.index)].copy()
+
+        # Split potential multi-mapped accessions and distribute expression equally
+        split_regex = re.compile(r'[;,/| ]+')
+
+        def split_accessions(val):
+            if pd.isna(val):
+                return []
+            tokens = [t for t in split_regex.split(str(val)) if t]
+            # keep RefSeq-like accessions: NM_, NR_, XM_, XR_
+            tokens = [t for t in tokens if re.match(r'^[NX][MR]_\d+(\.\d+)?$', t)]
+            return tokens
+
+        mapping_df['GeneList'] = mapping_df['Gene'].apply(split_accessions)
+        mapping_df['num_genes'] = mapping_df['GeneList'].apply(len)
+        # If no valid accession extracted, drop
+        mapping_df = mapping_df[mapping_df['num_genes'] > 0].copy()
+        if len(mapping_df) == 0:
+            print("WARNING: GB_ACC mapping did not yield valid RefSeq accessions. Proceeding with probe-level data.")
+            gene_data = probe_level_df
+        else:
+            mapping_df = mapping_df.explode('GeneList').rename(columns={'GeneList': 'Gene'})
+            mapping_df = mapping_df.set_index('ID')
+            merged = mapping_df.join(probe_level_df)
+            expr_cols = [c for c in merged.columns if c not in ['Gene', 'num_genes']]
+            merged[expr_cols] = merged[expr_cols].div(merged['num_genes'], axis=0)
+            gene_data = merged.groupby('Gene')[expr_cols].sum()
+
+# Light validation with warnings (no hard failure)
+n_genes = gene_data.shape[0]
+if n_genes is None or n_genes <= 100:
+    print(f"WARNING: Mapping produced a small number of gene identifiers (n={n_genes}). "
+          f"Downstream power may be limited.")
+else:
+    print(f"Gene-level dataset ready with {n_genes} features.")
+
+# Step 7: Gene Identifier Mapping
+# Robust gene identifier mapping for GSE107105
+
+# Keep a copy of probe-level expression
+probe_level_df = gene_data.copy()
+
+# Decide identifier and symbol columns from annotation
+probe_id_col = 'ID'
+assert probe_id_col in gene_annotation.columns, "Probe ID column 'ID' not found in gene annotation."
+
+# Try to detect a symbol-bearing column
+excluded_cols = {
+    probe_id_col, 'RANGE_GB', 'SPOT_ID', 'RANGE_STRAND', 'RANGE_START', 'RANGE_END', 'total_probes', 'GB_ACC'
+}
+candidate_cols = [c for c in gene_annotation.columns if c not in excluded_cols]
+
+best_col = None
+best_rows_with_symbols = -1
+best_total_symbols = -1
+for col in candidate_cols:
+    col_series = gene_annotation[col].astype(str)
+    # Use a reasonable subset
+    subset = col_series.head(50000)
+    extracted = subset.map(extract_human_gene_symbols)
+    rows_with_symbols = extracted.map(lambda x: len(x) > 0).sum()
+    total_symbols = extracted.map(len).sum()
+    if (rows_with_symbols > best_rows_with_symbols) or (
+        rows_with_symbols == best_rows_with_symbols and total_symbols > best_total_symbols
+    ):
+        best_rows_with_symbols = rows_with_symbols
+        best_total_symbols = total_symbols
+        best_col = col
+
+# Fallback names if heuristic finds nothing
+if best_rows_with_symbols <= 0 or best_col is None:
+    for fallback in ['gene_assignment', 'Gene Symbol', 'GENE_SYMBOL', 'GeneSymbol', 'Symbol', 'SYMBOL', 'Gene', 'GENE', 'Entrez Gene Symbol']:
+        if fallback in gene_annotation.columns and fallback not in excluded_cols:
+            best_col = fallback
+            best_rows_with_symbols = 1
+            break
+
+# If a plausible gene symbol column exists, use standard mapping to symbols
+if best_rows_with_symbols > 0 and best_col is not None:
+    mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=best_col)
+    gene_data = apply_gene_mapping(probe_level_df, mapping_df)
+    gene_data = normalize_gene_symbols_in_index(gene_data)
+else:
+    # Fallback: map probes to RefSeq accessions using GB_ACC
+    if 'GB_ACC' not in gene_annotation.columns:
+        # As last resort, keep probe-level data
+        gene_data = probe_level_df
+    else:
+        ann_sub = gene_annotation.loc[:, [probe_id_col, 'GB_ACC']].dropna().copy()
+        # Align to probes present in expression
+        ann_sub[probe_id_col] = ann_sub[probe_id_col].astype(str).str.strip()
+        ann_sub = ann_sub[ann_sub[probe_id_col].isin(probe_level_df.index)].copy()
+
+        # Split GB_ACC cells into individual accessions and clean up
+        split_regex = re.compile(r'[;,/| ]+')
+
+        def split_accessions(val):
+            if pd.isna(val):
+                return []
+            toks = [t for t in split_regex.split(str(val)) if t]
+            # Keep RefSeq-like accessions
+            toks = [t for t in toks if re.match(r'^[NX][MR]_\d+(\.\d+)?$', t)]
+            return toks
+
+        ann_sub['AccessionList'] = ann_sub['GB_ACC'].apply(split_accessions)
+        # Remove rows with no valid accessions
+        ann_sub = ann_sub[ann_sub['AccessionList'].map(len) > 0]
+        if len(ann_sub) == 0:
+            # No usable mapping; keep probe-level
+            gene_data = probe_level_df
+        else:
+            ann_sub = ann_sub.drop(columns=['GB_ACC']).explode('AccessionList').rename(columns={'AccessionList': 'Accession'})
+            # Compute number of targets per probe for equal splitting
+            ann_sub['num_targets'] = ann_sub.groupby(probe_id_col)['Accession'].transform('count')
+
+            # Join with expression and distribute weights
+            ann_sub = ann_sub.set_index(probe_id_col)
+            merged = ann_sub.join(probe_level_df, how='inner')
+            expr_cols = [c for c in merged.columns if c not in ['Accession', 'num_targets']]
+
+            # Avoid division by zero
+            merged['num_targets'] = merged['num_targets'].replace(0, 1)
+            merged[expr_cols] = merged[expr_cols].div(merged['num_targets'], axis=0)
+
+            # Aggregate to accession-level expression
+            gene_data = merged.groupby('Accession', as_index=True)[expr_cols].sum()
+
+# Optional sanity message
+print(f"Gene-level dataframe shape: {gene_data.shape}")
+
+# Step 8: Data Normalization and Linking
+import os
+import re
+
+# 1. Normalize gene symbols when appropriate; otherwise keep accession-level features
+refseq_pattern = re.compile(r'^[NX][MR]_\d+(\.\d+)?$')
+idx = gene_data.index.astype(str)
+if len(idx) > 0:
+    refseq_ratio = sum(bool(refseq_pattern.match(x)) for x in idx) / len(idx)
+else:
+    refseq_ratio = 0.0
+
+note_msg = "INFO: Gene symbol normalization applied."
+if refseq_ratio >= 0.8:
+    # Index appears to be mostly RefSeq accessions; skip normalization
+    normalized_gene_data = gene_data.copy()
+    note_msg = "WARNING: No reliable gene symbol annotation. Using RefSeq accession-level features; normalization skipped."
+else:
+    # Attempt normalization; if too many rows dropped, fall back
+    normalized_attempt = normalize_gene_symbols_in_index(gene_data.copy())
+    keep_ratio = (normalized_attempt.shape[0] / gene_data.shape[0]) if gene_data.shape[0] > 0 else 0.0
+    if (normalized_attempt.shape[0] == 0) or (keep_ratio < 0.1):
+        normalized_gene_data = gene_data.copy()
+        note_msg = f"WARNING: Gene symbol normalization dropped too many rows (kept {keep_ratio:.1%}). Falling back to original identifiers."
+    else:
+        normalized_gene_data = normalized_attempt
+        note_msg = f"INFO: Gene symbol normalization applied (kept {keep_ratio:.1%} of rows)."
+
+# Ensure output directory exists and save normalized gene (or accession-level) 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. Detect 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 = True  # trait row was extracted earlier
+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_msg
+)
+
+# 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/Osteoarthritis/code/GSE141934.py

@@ -0,0 +1,191 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE141934"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE141934"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE141934.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE141934.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE141934.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Transcriptional data from CD4+ T cells is described in the series
+
+# 2) Identify variable rows from the Sample Characteristics Dictionary
+trait_row = 6    # 'working_diagnosis' contains 'Osteoarthritis'
+age_row = 2      # 'age: <number>'
+gender_row = 1   # 'gender: F/M'
+
+# 2.2) Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    try:
+        parts = str(x).split(":")
+        return parts[-1].strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None or v == "" or v.lower() == "unknown":
+        return None
+    return 1 if v.lower() == "osteoarthritis" else 0
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None or v == "" or v.lower() in {"na", "n/a", "unknown"}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        # Try to extract digits if mixed content
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", v)
+        if m:
+            try:
+                return float(m.group())
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None or v == "":
+        return None
+    vl = v.lower()
+    if vl in {"female", "f"}:
+        return 0
+    if vl in {"male", "m"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction, preview, and save
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Illumina probe IDs (e.g., "ILMN_1343291") are not human gene symbols and require mapping.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the columns for probe IDs and gene symbols in the annotation dataframe
+probe_col = 'ID'       # Matches probe identifiers like 'ILMN_1343291'
+gene_symbol_col = 'Symbol'  # Contains gene symbols
+
+# Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+probe_data = gene_data  # preserve the probe-level data obtained previously
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1) Normalize gene symbols and save gene matrix
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) 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_flag = normalized_gene_data.shape[0] > 0
+is_trait_available_flag = (trait in linked_data.columns)
+note = ("INFO: Illumina probe IDs mapped to gene symbols via platform annotation; "
+        "CD4+ T cells from peripheral blood; trait derived from 'working_diagnosis' "
+        "as binary Osteoarthritis vs. non-Osteoarthritis.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Osteoarthritis/code/GSE142049.py

@@ -0,0 +1,220 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE142049"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE142049"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE142049.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE142049.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE142049.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/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  # Title and summary indicate transcriptional (gene expression) data in B cells.
+
+# 2) Variable availability and converters
+# Keys from Sample Characteristics Dictionary:
+# 1: gender
+# 2: age
+# 6: working_diagnosis (contains 'Osteoarthritis' among categories)
+trait_row = 6
+age_row = 2
+gender_row = 1
+
+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]
+    val = val.strip()
+    if val == "" or val.lower() in {"na", "n/a", "nan", "none", "null", "unknown"}:
+        return None
+    return val
+
+def convert_trait(x):
+    # Map working diagnosis to Osteoarthritis=1, others=0
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Heuristic: any label containing 'osteo' is Osteoarthritis
+    if "osteo" in v:
+        return 1
+    # For other defined diagnoses, treat as not Osteoarthritis
+    return 0
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    m = re.search(r"-?\d+\.?\d*", val)
+    if not m:
+        return None
+    try:
+        num = float(m.group(0))
+        return num
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"f", "female", "woman", "women"}:
+        return 0
+    if v 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 (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)
+
+    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
+# ILMN_* identifiers are Illumina probe IDs, not human gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine mapping columns based on the annotation preview: 'ID' (probe IDs) and 'Symbol' (gene symbols)
+probe_col = 'ID'
+gene_symbol_col = 'Symbol'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping: convert probe-level data to gene-level expression
+probe_data = gene_data  # keep a reference to the existing probe-level data from previous steps
+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. Bias assessment and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Ensure native Python types to avoid JSON serialization issues
+is_gene_available_final = bool(normalized_gene_data.shape[0] > 0)
+is_trait_available_final = bool((trait in unbiased_linked_data.columns) and bool(unbiased_linked_data[trait].notna().any()))
+is_trait_biased_py = bool(is_trait_biased)
+note = str("INFO: Probe IDs (ILMN) mapped via SOFT 'ID'->'Symbol'; symbols normalized using NCBI synonyms.")
+
+try:
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available_final,
+        is_trait_available=is_trait_available_final,
+        is_biased=is_trait_biased_py,
+        df=unbiased_linked_data,
+        note=note
+    )
+except TypeError:
+    # Fallback: if existing JSON has non-serializable content from prior runs, back it up and retry fresh
+    try:
+        if os.path.exists(json_path):
+            os.replace(json_path, json_path + ".bak")
+    except Exception:
+        pass
+    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_py,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+# 6. Save linked dataset if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Osteoarthritis/code/GSE236924.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE236924"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE236924"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE236924.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE236924.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE236924.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Array-based study comparing tissues; likely gene expression microarray (not miRNA-only or methylation-only)
+
+# 2) Variable availability and converters
+# From the provided Sample Characteristics Dictionary, only disease status is available at key 0
+trait_row = 0
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Map osteoarthritis as 1; other diseases or controls as 0
+    if v in {'oa', 'osteoarthritis'}:
+        return 1
+    if v in {'ra', 'rheumatoid arthritis', 'control', 'healthy', 'normal', 'non-disease', 'non disease', 'nd'}:
+        return 0
+    # Heuristic: if 'osteo' in string, treat as OA; if 'rheumat' present or 'control' present, treat as 0
+    if 'osteo' in v:
+        return 1
+    if 'rheumat' in v or 'control' in v:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not available; provided for completeness if needed in future
+    val = _extract_value(x)
+    if val is None:
+        return None
+    # Try to extract number from strings like "age: 56", "56 years"
+    import re
+    m = re.search(r'(\d+\.?\d*)', val)
+    if m:
+        try:
+            return float(m.group(1))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    # Not available; provided for completeness if needed in future
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {'male', 'm'}:
+        return 1
+    if v in {'female', 'f'}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is 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 and save
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Clinical data preview:", 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
+# Map probe IDs to gene symbols using annotation columns identified from preview:
+# - Probe ID column: 'ID' (matches gene_data index like '1007_s_at')
+# - Gene symbol column: 'Gene Symbol'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+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 (fix variable name)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Trait available; Age/Gender not provided in clinical annotations; "
+        "Affymetrix probes mapped to gene symbols via platform annotation; gene symbols normalized via synonyms.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Osteoarthritis/code/GSE55457.py

@@ -0,0 +1,206 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE55457"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE55457"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE55457.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE55457.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE55457.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix HG-U133 A/B transcriptomic (gene expression) data
+
+# 2. Variable Availability and Data Type Conversion
+# Identified keys from the Sample Characteristics Dictionary
+trait_row = 2      # 'clinical status: rheumatoid arthritis' | 'normal control' | 'osteoarthritis'
+age_row = 1        # 'age: <number>'
+gender_row = 0     # 'gender: female' | 'gender: male'
+
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(value):
+    """
+    Map OA status to binary: OA -> 1; RA/Controls/others -> 0; unknown -> None
+    """
+    s = _after_colon(value)
+    if not s:
+        return None
+    s = s.lower().strip()
+    # Positive (case)
+    if s in {'osteoarthritis', 'oa'}:
+        return 1
+    # Negative (non-case): controls and other diseases
+    if s in {'normal control', 'control', 'healthy control', 'healthy', 'cg', 'control group',
+             'rheumatoid arthritis', 'ra'}:
+        return 0
+    # Heuristic mappings
+    if 'osteo' in s:
+        return 1
+    if 'rheumatoid' in s or 'ra' == s:
+        return 0
+    if 'control' in s or 'healthy' in s or 'normal' in s:
+        return 0
+    return None
+
+def convert_age(value):
+    """
+    Extract numeric age; return as float; unknown -> None
+    """
+    s = _after_colon(value)
+    if not s:
+        return None
+    m = re.search(r'(\d+\.?\d*)', s)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except:
+        return None
+
+def convert_gender(value):
+    """
+    Map gender to binary: female -> 0, male -> 1; unknown -> None
+    """
+    s = _after_colon(value)
+    if not s:
+        return None
+    s = s.lower()
+    if s in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if s 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
+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
+# Affymetrix probe set identifiers (e.g., '1007_s_at', '1053_at') indicate non-gene-symbol IDs.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for mapping based on the preview:
+# Probe identifiers: 'ID'
+# Gene symbols: 'Gene Symbol'
+
+# 2. Extract the mapping dataframe (probe ID -> gene symbols)
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Apply the mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 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)
+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 with the 'geo_link_clinical_genetic_data' function from the library.
+# Ensure clinical data is available in memory; otherwise, reload it from disk.
+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)
+
+# 5. Conduct quality check and save the cohort information.
+note = ("INFO: Cohort includes OA, RA, and controls; trait is defined as OA (1) vs non-OA (0). "
+        "Affymetrix HG-U133A/B platform; probe-to-gene mapping with equal split for multi-gene probes; "
+        "gene symbols normalized via NCBI synonyms.")
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, 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/Osteoarthritis/code/GSE56409.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE56409"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE56409"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE56409.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE56409.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE56409.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/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 (not miRNA/methylation) per series summary
+
+# 2. Variable Availability and Data Type Conversion
+
+# From Sample Characteristics Dictionary:
+# 0: tissue (Skin/Bone Marrow/Synovium)
+# 1: disease (RA/OA) -> maps to trait Osteoarthritis
+# 2: serum (Low/High)
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _extract_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):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    # Map osteoarthritis to 1, rheumatoid arthritis to 0
+    if s in {"oa", "osteoarthritis"} or "osteo" in s:
+        return 1
+    if s in {"ra", "rheumatoid arthritis", "rheumatoid"} or "rheumatoid" in s:
+        return 0
+    # If other/unknown disease labels appear, set to None
+    return None
+
+def convert_age(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in {"na", "n/a", "none", "unknown", ""}:
+        return None
+    # Extract a numeric value (years assumed)
+    m = re.search(r"[-+]?\d*\.?\d+", s)
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in {"male", "m"}:
+        return 1
+    if s 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 data is 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(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Affymetrix probe set IDs (e.g., '1007_s_at') are not 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
+# Identify the correct columns for probe IDs and gene symbols from the annotation preview:
+# Probe IDs: 'ID' (e.g., '1007_s_at')
+# Gene symbols: 'Gene Symbol'
+
+# 1-2. Build mapping dataframe from annotation
+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 gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias evaluation and removal of biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: Affymetrix probe-to-gene mapping applied; trait is OA(1) vs RA(0); age/gender not available in matrix."
+)
+
+# 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/Osteoarthritis/code/GSE75181.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE75181"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE75181"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE75181.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE75181.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE75181.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Microarray gene expression profiling described in background
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics:
+# 1: ['disease state: osteoarthritis'] -> constant and not useful for trait association (all OA)
+# 2: ['gender: female', 'gender: male'] -> available
+# 3: ['age: ... years old'] -> available
+trait_row = None
+age_row = 3
+gender_row = 2
+
+def _after_colon(value):
+    if value is None:
+        return None
+    if isinstance(value, str) and ':' in value:
+        return value.split(':', 1)[1].strip()
+    return value
+
+# Even though trait is not available (constant OA), define a converter for completeness (unused)
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).lower()
+    # Map osteoarthritis/OA to 1, normal/control to 0 if ever present; otherwise None
+    if 'osteoarthritis' in v_low or v_low == 'oa':
+        return 1
+    if 'normal' in v_low or 'control' in v_low or v_low == 'no':
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract first number as age in years
+    import re
+    match = re.search(r'(\d+(\.\d+)?)', str(v))
+    if match:
+        try:
+            age_val = float(match.group(1))
+            return age_val
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).lower().strip()
+    if v_low in ['female', 'f', 'woman', 'women']:
+        return 0
+    if v_low 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
+#     )
+#     clinical_preview = preview_df(selected_clinical_df)
+#     selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Identify the probe ID and gene symbol columns in the annotation and create mapping
+probe_col = 'ID'       # matches probe identifiers like ILMN_1343291
+gene_col = 'Symbol'    # contains gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+probe_level_expr = gene_data  # from previous step
+gene_data = apply_gene_mapping(expression_df=probe_level_expr, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save to file
+import os
+
+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. Trait is not available (constant OA per Step 2), so skip linking and downstream processing.
+# Perform final validation reflecting lack of trait data and do not save linked data.
+is_trait_available = False
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=is_trait_available,
+    is_biased=False,
+    df=normalized_gene_data.T,
+    note="INFO: Trait not available (constant OA); linking and downstream steps skipped."
+)
+
+# Do not save out_data_file since dataset is not usable for association without trait variation.

output/preprocess/Osteoarthritis/code/GSE93698.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE93698"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE93698"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE93698.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE93698.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE93698.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/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 series description: high-density transcriptomic)
+is_gene_available = True
+
+# 2) Variable availability inferred from Sample Characteristics Dictionary in the prompt
+trait_row = 1     # 'disease state'
+age_row = 2       # 'age'
+gender_row = 3    # 'gender'
+
+# 2.2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    x = str(x)
+    parts = x.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
+    v_low = v.lower()
+    # Case = Osteoarthritis -> 1; Non-OA diseases -> 0
+    if "osteoarthritis" in v_low:
+        return 1
+    # Known non-OA disease states in this series
+    non_oa_terms = [
+        "rheumatoid arthritis", "diffuse systemic sclerosis", "microcrystalline arthritis",
+        "systemic lupus erythematosus", "seronegative arthritis"
+    ]
+    if any(term in v_low for term in non_oa_terms):
+        return 0
+    # If disease state given but not matching above, default to 0 (non-OA) if it looks like a disease label
+    if len(v_low) > 0:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract first number
+    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 = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {"f", "female", "woman", "women"}:
+        return 0
+    if v_low in {"m", "male", "man", "men"}:
+        return 1
+    return None
+
+# 3) Initial filtering metadata save
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (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)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the correct columns for mapping: probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2) Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Apply the mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features if necessary
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available = normalized_gene_data.shape[0] > 0
+is_trait_available = trait in linked_data.columns
+note = "INFO: Affymetrix probe IDs mapped to gene symbols; partial age/gender; missingness handled per protocol."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, is_gene_available, is_trait_available, is_trait_biased, unbiased_linked_data, note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Osteoarthritis/code/GSE93720.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE93720"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE93720"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE93720.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE93720.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE93720.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+# Microarray platform "GeneChip Human Genome U133 Plus 2.0" => mRNA expression data
+is_gene_available = True
+
+# 2) Variable availability and converters
+# From the sample characteristics:
+# 0: disease (OA vs RA) -> use as trait (binary: OA=1, RA=0)
+trait_row = 0
+age_row = None
+gender_row = None
+
+# Data type notes (for reference)
+trait_type = 'binary'
+age_type = 'continuous'
+gender_type = 'binary'
+
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        x = str(x)
+    parts = x.split(':', 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip().strip('"').strip()
+
+def convert_trait(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if v in {'oa', 'osteoarthritis'}:
+        return 1
+    if v in {'ra', 'rheumatoid arthritis'}:
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if v in {'na', 'n/a', 'none', ''}:
+        return None
+    nums = re.findall(r'[\d.]+', v)
+    if not nums:
+        return None
+    try:
+        return float(nums[0])
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if v in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if v in {'male', 'm', 'man', 'men'}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 in the annotation:
+# - Probe identifiers in expression data match the 'ID' column in gene_annotation
+# - Gene symbols are stored in the 'Gene Symbol' column
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2. Build mapping dataframe (probe -> gene symbol)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+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_flag = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_flag = isinstance(selected_clinical_df, pd.DataFrame) and (trait in selected_clinical_df.index)
+
+note = "INFO: In vitro synovial fibroblast stimulations (TNF, IL-17A, TNF+IL-17A, baseline) present; trait is OA vs RA donor origin."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Osteoarthritis/code/GSE98460.py

@@ -0,0 +1,239 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE98460"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE98460"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/GSE98460.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/GSE98460.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/GSE98460.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # RNA microarray-based transcriptional analysis suggests gene expression data is available.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Keys identified from Sample Characteristics Dictionary
+trait_row = None  # All samples are OA; no variability for case/control on the trait.
+age_row = 2
+gender_row = 3
+
+# Conversion helpers
+def _extract_value(cell: str) -> str:
+    if cell is None:
+        return ""
+    parts = str(cell).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(cell).strip()
+
+def convert_trait(x):
+    # Not used since trait_row is None. If ever used, map OA to 1 and others to 0.
+    val = _extract_value(x).lower()
+    if val in {"", "na", "n/a", "none", "unknown"}:
+        return None
+    return 1 if "osteoarthritis" in val or val == "oa" else 0
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val == "" or val.lower() in {"na", "n/a", "none", "unknown"}:
+        return None
+    # Extract the first number (integer or float)
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if not m:
+        return None
+    try:
+        num = float(m.group())
+        return num
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_value(x).lower()
+    if val in {"", "na", "n/a", "none", "unknown"}:
+        return None
+    if val.startswith("f"):
+        return 0
+    if val.startswith("m"):
+        return 1
+    # Try to infer from words
+    if "female" in val:
+        return 0
+    if "male" in val:
+        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
+# Skipped because trait_row is None (no variable trait data available in this cohort).
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Based on the provided identifiers from the previous step
+ids = ['16650001', '16650003', '16650005', '16650007', '16650009', '16650011',
+       '16650013', '16650015', '16650017', '16650019', '16650021', '16650023',
+       '16650025', '16650027', '16650029', '16650031', '16650033', '16650035',
+       '16650037', '16650041']
+
+# Human gene symbols typically contain alphabetic characters; purely numeric IDs indicate probe IDs.
+requires_gene_mapping = all(x.isdigit() for x in ids)
+
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+
+# Try to find a platform SOFT file that contains a usable gene symbol column
+probe_col = 'ID'
+candidate_gene_cols = [
+    'Gene Symbol', 'GENE_SYMBOL', 'Symbol', 'GENE', 'GENE_SYMBOLS', 'SYMBOL',
+    'gene_assignment', 'Gene Title', 'GENE_NAME'
+]
+
+soft_files = [f for f in os.listdir(in_cohort_dir) if 'soft' in f.lower()]
+best_soft = None
+best_col = None
+best_count = 0
+best_annotation = None
+
+for sf in soft_files:
+    try:
+        ann = get_gene_annotation(os.path.join(in_cohort_dir, sf))
+    except Exception:
+        continue
+    present_candidates = [col for col in candidate_gene_cols if col in ann.columns]
+    if not present_candidates:
+        continue
+    # Score columns by how many recognizable human gene symbols they yield
+    for col in present_candidates:
+        series = ann[col].dropna().astype(str)
+        sample = series.head(200)
+        count = sum(len(extract_human_gene_symbols(x)) for x in sample)
+        if count > best_count:
+            best_count = count
+            best_col = col
+            best_soft = sf
+            best_annotation = ann
+
+# If a suitable annotation with gene symbols is found, build mapping and apply it
+mapped_successfully = False
+if best_annotation is not None and best_col is not None and best_count > 0:
+    try:
+        mapping_df = get_gene_mapping(best_annotation, prob_col=probe_col, gene_col=best_col)
+        # Ensure there is overlap between mapping IDs and expression IDs
+        overlap = mapping_df['ID'].isin(gene_data.index).sum()
+        if overlap > 0:
+            mapped_gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+            # Sanity check: non-empty result
+            if mapped_gene_data.shape[0] > 0:
+                gene_data = mapped_gene_data
+                mapped_successfully = True
+    except Exception:
+        mapped_successfully = False
+
+# Fallback: if mapping not successful, keep probe-level data as-is (identity, probe IDs as features)
+# This allows the pipeline to continue, but downstream steps should consider this dataset unsuitable for cross-cohort gene-level analyses.
+# No further action needed since 'gene_data' already holds probe-level expression.
+
+# Step 7: Data Normalization and Linking
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene-level data (may be empty if mapping failed)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine availability based on normalized gene-level data
+is_gene_available = normalized_gene_data.shape[0] > 0
+
+# 2-6. Proceed only if clinical data exists; otherwise, finalize with correct flags
+if 'selected_clinical_data' in globals() and selected_clinical_data is not None:
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias checks and removal of biased covariates
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked clinical and gene data; performed missing-value handling and bias checks."
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait not available; skip linking and downstream steps
+    note_msg = "INFO: Trait not available for this cohort; skipped linking and downstream steps."
+    if normalized_gene_data.shape[0] == 0:
+        note_msg += " Gene mapping/normalization yielded no recognized symbols."
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=False,
+        is_biased=False,  # placeholder; function will override due to empty df
+        df=pd.DataFrame(),
+        note=note_msg
+    )

output/preprocess/Osteoarthritis/code/TCGA.py

@@ -0,0 +1,60 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoarthritis/TCGA.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoarthritis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoarthritis/clinical_data/TCGA.csv"
+json_path = "./output/z5/preprocess/Osteoarthritis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA subdirectory for Osteoarthritis (likely none in TCGA cancer cohorts)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_terms_priority = [
+    "osteoarthritis",
+    "degenerative_joint_disease",
+    "djd",
+    "arthritis",
+    "joint",
+    "cartilage"
+]
+
+selected_dir = None
+for term in trait_terms_priority:
+    matches = [d for d in subdirs if term in d.lower()]
+    if matches:
+        # Choose the most specific (first) match
+        selected_dir = matches[0]
+        break
+
+clinical_df = None
+genetic_df = None
+
+if selected_dir is not None:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)
+
+    print(list(clinical_df.columns))
+else:
+    # No suitable TCGA cohort for Osteoarthritis; record and skip
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=f"No_TCGA_Cohort_for_{trait.replace(' ', '_')}",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print([])

output/preprocess/Osteoarthritis/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE98460": {
-    "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
-  },
-  "GSE93720": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 1
-  },
-  "GSE93698": {
-    "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": 30
-  },
-  "GSE75181": {
-    "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
-  },
-  "GSE56409": {
-    "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": 102
-  },
-  "GSE55457": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 33
-  },
-  "GSE236924": {
-    "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": 132
-  },
-  "GSE142049": {
-    "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": 114
-  },
-  "GSE141934": {
-    "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": 100
-  },
-  "GSE107105": {
-    "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": 36
-  },
-  "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
-  }
-}
+{"GSE98460": {"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": "INFO: Trait not available for this cohort; skipped linking and downstream steps. Gene mapping/normalization yielded no recognized symbols."}, "GSE93720": {"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": 48, "note": "INFO: In vitro synovial fibroblast stimulations (TNF, IL-17A, TNF+IL-17A, baseline) present; trait is OA vs RA donor origin."}, "GSE93698": {"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": 30, "note": "INFO: Affymetrix probe IDs mapped to gene symbols; partial age/gender; missingness handled per protocol."}, "GSE75181": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait not available (constant OA); linking and downstream steps skipped."}, "GSE56409": {"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": 102, "note": "INFO: Affymetrix probe-to-gene mapping applied; trait is OA(1) vs RA(0); age/gender not available in matrix."}, "GSE55457": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 33, "note": "INFO: Cohort includes OA, RA, and controls; trait is defined as OA (1) vs non-OA (0). Affymetrix HG-U133A/B platform; probe-to-gene mapping with equal split for multi-gene probes; gene symbols normalized via NCBI synonyms."}, "GSE236924": {"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": 132, "note": "INFO: Trait available; Age/Gender not provided in clinical annotations; Affymetrix probes mapped to gene symbols via platform annotation; gene symbols normalized via synonyms."}, "GSE142049": {"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": 114, "note": "INFO: Probe IDs (ILMN) mapped via SOFT 'ID'->'Symbol'; symbols normalized using NCBI synonyms."}, "GSE141934": {"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": 99, "note": "INFO: Illumina probe IDs mapped to gene symbols via platform annotation; CD4+ T cells from peripheral blood; trait derived from 'working_diagnosis' as binary Osteoarthritis vs. non-Osteoarthritis."}, "GSE107105": {"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": 36, "note": "WARNING: Gene symbol normalization dropped too many rows (kept 0.0%). Falling back to original identifiers."}, "No_TCGA_Cohort_for_Osteoarthritis": {"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/Osteoporosis/clinical_data/GSE56814.csv

@@ -1,74 +1,2 @@
-,Osteoporosis,Gender
-GSM1369683,,0
-GSM1369684,,0
-GSM1369685,,0
-GSM1369686,,0
-GSM1369687,,0
-GSM1369688,,0
-GSM1369689,,0
-GSM1369690,,0
-GSM1369691,,0
-GSM1369692,,0
-GSM1369693,,0
-GSM1369694,,0
-GSM1369695,,0
-GSM1369696,,0
-GSM1369697,,0
-GSM1369698,,0
-GSM1369699,,0
-GSM1369700,,0
-GSM1369701,,0
-GSM1369702,,0
-GSM1369703,,0
-GSM1369704,,0
-GSM1369705,,0
-GSM1369706,,0
-GSM1369707,,0
-GSM1369708,,0
-GSM1369709,,0
-GSM1369710,,0
-GSM1369711,,0
-GSM1369712,,0
-GSM1369713,,0
-GSM1369714,,0
-GSM1369715,,0
-GSM1369716,,0
-GSM1369717,,0
-GSM1369718,,0
-GSM1369719,,0
-GSM1369720,,0
-GSM1369721,,0
-GSM1369722,,0
-GSM1369723,,0
-GSM1369724,,0
-GSM1369725,,0
-GSM1369726,,0
-GSM1369727,,0
-GSM1369728,,0
-GSM1369729,,0
-GSM1369730,,0
-GSM1369731,,0
-GSM1369732,,0
-GSM1369733,,0
-GSM1369734,,0
-GSM1369735,,0
-GSM1369736,,0
-GSM1369737,,0
-GSM1369738,,0
-GSM1369739,,0
-GSM1369740,,0
-GSM1369741,,0
-GSM1369742,,0
-GSM1369743,,0
-GSM1369744,,0
-GSM1369745,,0
-GSM1369746,,0
-GSM1369747,,0
-GSM1369748,,0
-GSM1369749,,0
-GSM1369750,,0
-GSM1369751,,0
-GSM1369752,,0
-GSM1369753,,0
-GSM1369754,,0
-GSM1369755,,0
+,GSM1369683,GSM1369684,GSM1369685,GSM1369686,GSM1369687,GSM1369688,GSM1369689,GSM1369690,GSM1369691,GSM1369692,GSM1369693,GSM1369694,GSM1369695,GSM1369696,GSM1369697,GSM1369698,GSM1369699,GSM1369700,GSM1369701,GSM1369702,GSM1369703,GSM1369704,GSM1369705,GSM1369706,GSM1369707,GSM1369708,GSM1369709,GSM1369710,GSM1369711,GSM1369712,GSM1369713,GSM1369714,GSM1369715,GSM1369716,GSM1369717,GSM1369718,GSM1369719,GSM1369720,GSM1369721,GSM1369722,GSM1369723,GSM1369724,GSM1369725,GSM1369726,GSM1369727,GSM1369728,GSM1369729,GSM1369730,GSM1369731,GSM1369732,GSM1369733,GSM1369734,GSM1369735,GSM1369736,GSM1369737,GSM1369738,GSM1369739,GSM1369740,GSM1369741,GSM1369742,GSM1369743,GSM1369744,GSM1369745,GSM1369746,GSM1369747,GSM1369748,GSM1369749,GSM1369750,GSM1369751,GSM1369752,GSM1369753,GSM1369754,GSM1369755
+Osteoporosis,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0

output/preprocess/Osteoporosis/clinical_data/GSE56815.csv

@@ -1,3 +1,2 @@
 ,GSM1369756,GSM1369757,GSM1369758,GSM1369759,GSM1369760,GSM1369761,GSM1369762,GSM1369763,GSM1369764,GSM1369765,GSM1369766,GSM1369767,GSM1369768,GSM1369769,GSM1369770,GSM1369771,GSM1369772,GSM1369773,GSM1369774,GSM1369775,GSM1369776,GSM1369777,GSM1369778,GSM1369779,GSM1369780,GSM1369781,GSM1369782,GSM1369783,GSM1369784,GSM1369785,GSM1369786,GSM1369787,GSM1369788,GSM1369789,GSM1369790,GSM1369791,GSM1369792,GSM1369793,GSM1369794,GSM1369795,GSM1369796,GSM1369797,GSM1369798,GSM1369799,GSM1369800,GSM1369801,GSM1369802,GSM1369803,GSM1369804,GSM1369805,GSM1369806,GSM1369807,GSM1369808,GSM1369809,GSM1369810,GSM1369811,GSM1369812,GSM1369813,GSM1369814,GSM1369815,GSM1369816,GSM1369817,GSM1369818,GSM1369819,GSM1369820,GSM1369821,GSM1369822,GSM1369823,GSM1369824,GSM1369825,GSM1369826,GSM1369827,GSM1369828,GSM1369829,GSM1369830,GSM1369831,GSM1369832,GSM1369833,GSM1369834,GSM1369835
 Osteoporosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
-Age,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Osteoporosis/code/GSE152073.py

@@ -0,0 +1,131 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE152073"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE152073"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoporosis/GSE152073.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoporosis/gene_data/GSE152073.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoporosis/clinical_data/GSE152073.csv"
+json_path = "./output/z5/preprocess/Osteoporosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene expression availability (Affymetrix microarrays -> mRNA expression)
+is_gene_available = True
+
+# 2) Variable availability based on Sample Characteristics Dictionary
+# Provided keys: 0: gender (all female -> constant), 1: age (varied), 2: height (constant)
+trait_row = None   # No osteoporosis status available in characteristics
+age_row = 1        # age (years)
+gender_row = None  # All female -> constant -> not useful
+
+# 2.2) Conversion functions
+def _after_colon(s: str) -> str:
+    if s is None:
+        return ""
+    parts = str(s).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(s).strip()
+
+def convert_trait(x):
+    """
+    Convert osteoporosis-related entries to binary:
+    1 = osteoporosis present, 0 = absent.
+    Heuristics handle common labels (case/control, yes/no, patient/healthy).
+    Unknown or ambiguous -> None.
+    """
+    val = _after_colon(x).lower()
+    if val == "":
+        return None
+    # Direct mappings
+    if val in {"case", "patient", "yes", "y", "positive", "pos"}:
+        return 1
+    if val in {"control", "healthy", "no", "n", "negative", "neg"}:
+        return 0
+    # Keyword-based
+    if "osteopor" in val:
+        if any(neg in val for neg in ["no ", " no", "not", "non-", "without", "free"]):
+            return 0
+        return 1
+    return None
+
+def convert_age(x):
+    """
+    Extract numeric age in years from entries like 'age (years): 76'.
+    """
+    val = _after_colon(x)
+    if val == "":
+        return None
+    # keep only first number (int or float)
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if not m:
+        return None
+    try:
+        return float(m.group(0))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Convert gender to binary: female->0, male->1.
+    Handles common variations. Unknown -> None.
+    """
+    val = _after_colon(x).lower()
+    if val in {"female", "f", "woman", "women"}:
+        return 0
+    if val 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 (skip if trait not available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Osteoporosis/code/GSE20881.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE20881"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE20881"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoporosis/GSE20881.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoporosis/gene_data/GSE20881.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoporosis/clinical_data/GSE20881.csv"
+json_path = "./output/z5/preprocess/Osteoporosis/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
+from datetime import datetime
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on series summary indicating gene expression profiling (not miRNA-only or methylation)
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# Trait for this project is Osteoporosis, which is not present in this cohort (Crohn's disease vs controls)
+trait_row = None
+
+# Birth date is available at key 2; can be used to approximate age
+age_row = 2
+
+# No explicit gender field observed; inference from medications/notes would be unreliable and not in a single key
+gender_row = None
+
+# 2.2 Data Type Conversion
+
+def _extract_value(cell: str) -> str:
+    if cell is None:
+        return ""
+    parts = str(cell).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(cell).strip()
+
+def convert_trait(cell: str):
+    # Trait of interest is Osteoporosis, not recorded in this dataset; return None
+    _ = _extract_value(cell)
+    return None
+
+def _parse_birth_year(value: str) -> int:
+    # Handles formats like mm/dd/yy, m/d/yy, mm/dd/yyyy, and known typo '1060' -> 1960
+    value = value.strip()
+    if not value:
+        return None
+    try:
+        # Try common formats
+        for fmt in ["%m/%d/%Y", "%m/%d/%y", "%m/%d/%Y ", "%m/%d/%y "]:
+            try:
+                dt = datetime.strptime(value, fmt)
+                year = dt.year
+                break
+            except ValueError:
+                continue
+        else:
+            # Fallback: manual split
+            parts = value.replace("-", "/").split("/")
+            if len(parts) >= 3:
+                y = parts[2].strip()
+                if len(y) == 4 and y.isdigit():
+                    year = int(y)
+                elif len(y) <= 2 and y.isdigit():
+                    yy = int(y)
+                    year = 1900 + yy if yy >= 30 else 2000 + yy
+                else:
+                    return None
+            else:
+                return None
+
+        # Heuristic correction for obvious typo like 1060 -> 1960
+        if 1000 <= year < 1930:
+            # If it's clearly a birth year far in the past but suspicious (e.g., 1060),
+            # bump by 900 to map 1060->1960. Leave genuine older years (e.g., 1928) unchanged.
+            if year < 1900:
+                year += 900
+
+        return year
+    except Exception:
+        return None
+
+# Approximate age at time of sampling; most procedures are in 2004-2005 per provided data
+# Use 2005 as reference if exact procedure date per sample is not available here
+REFERENCE_PROCEDURE_YEAR = 2005
+
+def convert_age(cell: str):
+    val = _extract_value(cell)
+    byear = _parse_birth_year(val)
+    if byear is None:
+        return None
+    age = REFERENCE_PROCEDURE_YEAR - byear
+    # Basic sanity check
+    if age < 0 or age > 120:
+        return None
+    return float(age)
+
+def convert_gender(cell: str):
+    # Gender not available in a dedicated key; this converter handles typical forms if encountered
+    val = _extract_value(cell).lower()
+    if val in ["female", "f", "0"]:
+        return 0
+    if val in ["male", "m", "1"]:
+        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
+# Skipped because trait_row is None (no Osteoporosis status available in this cohort)

output/preprocess/Osteoporosis/code/GSE224330.py

@@ -0,0 +1,197 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE224330"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE224330"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoporosis/GSE224330.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoporosis/gene_data/GSE224330.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoporosis/clinical_data/GSE224330.csv"
+json_path = "./output/z5/preprocess/Osteoporosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import math
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Transcriptomic profiling described in background info
+
+# 2) Variable availability and converters based on the provided Sample Characteristics Dictionary
+# trait: Osteoporosis inferred from "comorbidity" field -> row 3
+trait_row = 3
+
+# age: available at row 1
+age_row = 1
+
+# gender: available at row 2
+gender_row = 2
+
+def _get_value_after_colon(x):
+    if x is None or (isinstance(x, float) and math.isnan(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):
+    # Binary: Osteoporosis present -> 1, else -> 0
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if "osteopor" in v:
+        return 1
+    # Treat explicit non-osteoporosis comorbidities and 'none' as 0
+    if v in {"none", "hypothyroidism", "arthrosis", "schizoaffective disorder"} or v != "":
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous: extract number of years
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", val)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if v in {"f", "female", "woman", "women"}:
+        return 0
+    if v 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 (only if clinical data is 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(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for probe IDs and gene symbols based on annotation preview
+probe_col_candidates = ['ID', 'SPOT_ID', 'ProbeID', 'PROBE_ID']
+gene_col_candidates = ['GENE_SYMBOL', 'GENE_SYMBOLS', 'Symbol', 'Gene Symbol']
+
+probe_col = next(col for col in probe_col_candidates if col in gene_annotation.columns)
+gene_col = next(col for col in gene_col_candidates if col in gene_annotation.columns)
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3) Apply mapping to convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias evaluation and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Osteoporosis trait inferred from the 'comorbidity' field; many samples lacked explicit comorbidity "
+        "annotation prior to filtering, which may reduce usable sample size after removing missing trait values.")
+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/Osteoporosis/code/GSE35925.py

@@ -0,0 +1,133 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE35925"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE35925"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoporosis/GSE35925.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoporosis/gene_data/GSE35925.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoporosis/clinical_data/GSE35925.csv"
+json_path = "./output/z5/preprocess/Osteoporosis/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 U133 Plus 2.0 microarray indicates gene expression data
+
+# Step 2: Variable availability based on provided Sample Characteristics Dictionary
+trait_row = None  # No osteoporosis-related variable available
+age_row = 1       # 'age' is available with multiple values
+gender_row = None # Gender is constant 'female' across samples -> not useful
+
+# Step 2.2: Conversion functions
+def _extract_value_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        return val.strip()
+    return x
+
+def convert_trait(x):
+    # Binary mapping for osteoporosis presence: 1 for osteoporosis/low BMD/fracture; 0 for healthy/control/no
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+
+    positives = {"osteoporosis", "osteoporotic", "op", "case", "patient", "fracture", "fragility fracture", "low bmd",
+                 "osteopenia"}
+    negatives = {"control", "healthy", "normal", "no", "non-osteoporosis", "nonosteoporosis", "non osteoporotic"}
+
+    # Heuristics: explicit yes/no
+    if s in {"yes", "y", "true", "1"}:
+        return 1
+    if s in {"no", "n", "false", "0"}:
+        return 0
+
+    # Keyword-based mapping
+    for p in positives:
+        if p in s:
+            return 1
+    for n in negatives:
+        if n in s:
+            return 0
+
+    return None
+
+def convert_age(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip()
+    # Remove non-numeric trailing characters if any
+    try:
+        # Handle possible formats like "66", "66 years", "66y"
+        num = ''.join(ch for ch in s if (ch.isdigit() or ch == '.' or ch == '-'))
+        if num in {"", "-", ".", "-.", ".-"}:
+            return None
+        return float(num)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {"female", "f", "woman", "women"}:
+        return 0
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# Step 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
+)
+
+# Step 4: Clinical feature extraction (skip because trait_row is None)
+# If trait_row were 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)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Osteoporosis/code/GSE51495.py

@@ -0,0 +1,113 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE51495"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE51495"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoporosis/GSE51495.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoporosis/gene_data/GSE51495.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoporosis/clinical_data/GSE51495.csv"
+json_path = "./output/z5/preprocess/Osteoporosis/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 info and characteristics
+# This series is a nonhuman primate study; therefore, human trait/age/gender are considered unavailable.
+is_gene_available = True  # Transcriptional profiles indicate gene expression data
+trait_row = None          # Osteoporosis status not provided and nonhuman study
+age_row = None            # Nonhuman data -> not usable for human trait analysis
+gender_row = None         # Nonhuman data -> not usable for human trait analysis
+
+# Conversion helpers
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    if s == '' or s.lower() in {'na', 'n/a', 'none', 'null', 'unknown', 'not available', '.'}:
+        return None
+    return s
+
+def convert_trait(v):
+    # Trait (Osteoporosis status) not available for human data in this nonhuman study
+    return None
+
+def convert_age(v):
+    s = _after_colon(v)
+    if s is None:
+        return None
+    try:
+        return float(s)
+    except Exception:
+        # Attempt to strip non-numeric text if present
+        import re
+        nums = re.findall(r"[-+]?\d*\.\d+|\d+", s)
+        if not nums:
+            return None
+        try:
+            return float(nums[0])
+        except Exception:
+            return None
+
+def convert_gender(v):
+    s = _after_colon(v)
+    if s is None:
+        return None
+    s_low = s.lower()
+    if s_low.startswith('f'):
+        return 0
+    if s_low.startswith('m'):
+        return 1
+    return None
+
+# Initial filtering metadata: trait availability determined by trait_row
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction is skipped because trait_row is None (no human clinical data available)
+# If trait_row were available, we would use:
+# 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)
+# selected_clinical.to_csv(out_clinical_data_file)

output/preprocess/Osteoporosis/code/GSE56814.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE56814"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE56814"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoporosis/GSE56814.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoporosis/gene_data/GSE56814.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoporosis/clinical_data/GSE56814.csv"
+json_path = "./output/z5/preprocess/Osteoporosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix HuEx-1_0-st-v2 mRNA expression platform
+
+# 2. Variable Availability and Data Type Conversion
+
+# Availability
+trait_row = 1          # 'bone mineral density: high BMD' / 'low BMD' -> proxy for Osteoporosis status
+age_row = None         # No per-sample age values provided in characteristics
+gender_row = None      # Only 'Female' present -> constant, not useful
+
+# 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) == 2 else s.strip()
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    v = v.lower()
+    # Map low BMD (case) -> 1, high BMD (control) -> 0
+    if 'low' in v:
+        return 1
+    if 'high' in v:
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    # Extract a numeric age if present
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    v = v.lower()
+    if v.startswith('fem'):
+        return 0
+    if v.startswith('male') or v.startswith('man') or v == 'm':
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if 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)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+    print(preview)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Based on numeric probe-like identifiers (e.g., '2315554'), mapping to human gene symbols is required.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Choose probe ID and gene symbol columns and build mapping dataframe
+# Probe IDs match 'ID' column; gene symbols are embedded in 'gene_assignment'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and demographic features are severely biased, and remove biased features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information
+note = ("INFO: Affymetrix HuEx-1_0-st-v2 monocyte expression. Trait derived from high vs. low hip BMD; "
+        "all samples female; no per-sample age available; gender constant and omitted. "
+        "Gene symbols normalized via NCBI synonyms; multi-gene probe mappings split and summed.")
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note
+)
+
+# 6. If the linked data is usable, save it
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Osteoporosis/code/GSE56815.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE56815"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE56815"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoporosis/GSE56815.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoporosis/gene_data/GSE56815.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoporosis/clinical_data/GSE56815.csv"
+json_path = "./output/z5/preprocess/Osteoporosis/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 (Affymetrix HG-U133A => gene expression)
+is_gene_available = True
+
+# 2) Variable availability based on Sample Characteristics Dictionary
+# trait: Osteoporosis approximated by BMD group (low vs high)
+trait_row = 1  # 'bone mineral density: high BMD' / 'bone mineral density: low BMD'
+# age not explicitly available
+age_row = None
+# gender is constant (all Female), hence not useful
+gender_row = None
+
+# 2.2) Data type conversion functions
+def _get_value_after_colon(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        x = str(x)
+    parts = x.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Map low BMD -> 1 (case), high BMD -> 0 (control)
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if "low" in v:
+        return 1
+    if "high" in v:
+        return 0
+    if "osteopor" in v:  # fallback if phrased differently
+        return 1
+    if "control" in v:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years if present (not used here since age_row is None)
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if v in {"na", "n/a", "unknown", ""}:
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    num = float(m.group(1))
+    if num.is_integer():
+        return int(num)
+    return num
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1 (not used here since gender_row is None)
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"f", "female", "woman", "women"}:
+        return 0
+    if v 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 (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)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify appropriate columns for mapping: 'ID' for probe IDs and 'Gene Symbol' for gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+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
+note = ("INFO: Trait derived from BMD group (low=1 case, high=0 control). "
+        "All samples are female; Gender excluded as constant. "
+        "Age unavailable in clinical annotations. "
+        "Platform: Affymetrix HG-U133A; probe-to-gene mapping applied with split-sum aggregation.")
+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/Osteoporosis/code/GSE62589.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE62589"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE62589"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Osteoporosis/GSE62589.csv"
+out_gene_data_file = "./output/z5/preprocess/Osteoporosis/gene_data/GSE62589.csv"
+out_clinical_data_file = "./output/z5/preprocess/Osteoporosis/clinical_data/GSE62589.csv"
+json_path = "./output/z5/preprocess/Osteoporosis/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) Assess gene expression availability based on series description
+is_gene_available = True  # "Transcriptomic" strongly indicates gene expression data (not solely miRNA/methylation)
+
+# 2) Variable availability from the provided Sample Characteristics Dictionary
+# Observed keys and unique values:
+# 0: ['tissue: blood']
+# 1: ['cell type: Peripheral blood monocytes']
+# 2: ['Sex: female']
+# No trait or age fields; gender is constant (all female), hence not useful.
+
+trait_row = None
+age_row = None
+gender_row = None  # Constant feature -> treat as not available
+
+# 2.2 Converters
+import math
+import re
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    val = val.strip().strip('"').strip("'")
+    return val if val != "" else None
+
+def convert_trait(x):
+    """
+    Binary: Osteoporosis status -> 1, control/normal -> 0.
+    Heuristics also consider common labels and BMD/Fracture terms.
+    Unknown/unmappable -> None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+
+    # Direct mappings
+    pos_terms = [
+        "osteoporosis", "op", "case", "patient", "affected", "fragility fracture", "fracture",
+        "low bmd", "low-bmd", "osteoporotic"
+    ]
+    neg_terms = [
+        "control", "normal", "healthy", "non-osteoporosis", "non osteoporosis",
+        "no fracture", "nonfracture", "high bmd", "high-bmd", "unaffected"
+    ]
+    for t in pos_terms:
+        if t in vl:
+            return 1
+    for t in neg_terms:
+        if t in vl:
+            return 0
+
+    # Handle osteopenia conservatively as unknown (ambiguous phenotype)
+    if "osteopenia" in vl:
+        return None
+
+    # T-score rule if present (<= -2.5 -> OP; >= -1.0 -> normal; else unknown)
+    # e.g., "T-score -2.7", "t score:-2.6"
+    tscore_match = re.search(r"t[\s-]*score[^-+\d]*([+-]?\d+(\.\d+)?)", vl)
+    if tscore_match:
+        try:
+            t = float(tscore_match.group(1))
+            if t <= -2.5:
+                return 1
+            if t >= -1.0:
+                return 0
+            return None
+        except Exception:
+            pass
+
+    return None
+
+def convert_age(x):
+    """
+    Continuous age in years (float). Extracts first number found.
+    Returns None if not parsable.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r"([0-9]+(\.[0-9]+)?)", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Binary: female -> 0, male -> 1; unknown -> None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in ["f", "female", "woman", "women"]:
+        return 0
+    if vl in ["m", "male", "man", "men"]:
+        return 1
+    return None
+
+# 3) Initial filtering metadata save
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If in future data provides trait_row, 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)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)