Add files using upload-large-folder tool

output/preprocess/Cervical_Cancer/clinical_data/GSE107754.csv

@@ -1,3 +1,3 @@
 ,GSM2878070,GSM2878071,GSM2878072,GSM2878073,GSM2878074,GSM2878075,GSM2878076,GSM2878077,GSM2878078,GSM2878079,GSM2878080,GSM2878081,GSM2878082,GSM2891194,GSM2891195,GSM2891196,GSM2891197,GSM2891198,GSM2891199,GSM2891200,GSM2891201,GSM2891202,GSM2891203,GSM2891204,GSM2891205,GSM2891206,GSM2891207,GSM2891208,GSM2891209,GSM2891210,GSM2891211,GSM2891212,GSM2891213,GSM2891214,GSM2891215,GSM2891216,GSM2891217,GSM2891218,GSM2891219,GSM2891220,GSM2891221,GSM2891222,GSM2891223,GSM2891224,GSM2891225,GSM2891226,GSM2891227,GSM2891228,GSM2891229,GSM2891230,GSM2891231,GSM2891232,GSM2891233,GSM2891234,GSM2891235,GSM2891236,GSM2891237,GSM2891238,GSM2891239,GSM2891240,GSM2891241,GSM2891242,GSM2891243,GSM2891244,GSM2891245,GSM2891246,GSM2891247,GSM2891248,GSM2891249,GSM2891250,GSM2891251,GSM2891252,GSM2891253,GSM2891254,GSM2891255,GSM2891256,GSM2891257,GSM2891258,GSM2891259,GSM2891260,GSM2891261,GSM2891262,GSM2891263,GSM2891264
-Cervical_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Cervical_Cancer,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
 Gender,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0

output/preprocess/Cervical_Cancer/clinical_data/GSE131027.csv

@@ -1,2 +1,3 @@
-,Sample_2,Sample_3,Sample_4,Sample_5,Sample_6,Sample_7,Sample_8,Sample_9,Sample_10,Sample_11,Sample_12,Sample_13,Sample_14,Sample_15,Sample_16,Sample_17,Sample_18,Sample_19,Sample_20,Sample_21,Sample_22,Sample_23,Sample_24
-Cervical_Cancer,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0
+,GSM3759992,GSM3759993,GSM3759994,GSM3759995,GSM3759996,GSM3759997,GSM3759998,GSM3759999,GSM3760000,GSM3760001,GSM3760002,GSM3760003,GSM3760004,GSM3760005,GSM3760006,GSM3760007,GSM3760008,GSM3760009,GSM3760010,GSM3760011,GSM3760012,GSM3760013,GSM3760014,GSM3760015,GSM3760016,GSM3760017,GSM3760018,GSM3760019,GSM3760020,GSM3760021,GSM3760022,GSM3760023,GSM3760024,GSM3760025,GSM3760026,GSM3760027,GSM3760028,GSM3760029,GSM3760030,GSM3760031,GSM3760032,GSM3760033,GSM3760034,GSM3760035,GSM3760036,GSM3760037,GSM3760038,GSM3760039,GSM3760040,GSM3760041,GSM3760042,GSM3760043,GSM3760044,GSM3760045,GSM3760046,GSM3760047,GSM3760048,GSM3760049,GSM3760050,GSM3760051,GSM3760052,GSM3760053,GSM3760054,GSM3760055,GSM3760056,GSM3760057,GSM3760058,GSM3760059,GSM3760060,GSM3760061,GSM3760062,GSM3760063,GSM3760064,GSM3760065,GSM3760066,GSM3760067,GSM3760068,GSM3760069,GSM3760070,GSM3760071,GSM3760072,GSM3760073,GSM3760074,GSM3760075,GSM3760076,GSM3760077,GSM3760078,GSM3760079,GSM3760080,GSM3760081,GSM3760082,GSM3760083
+Cervical_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Gender,,,,,,,,,,,,,0.0,,0.0,,,,,,,,,0.0,,1.0,,,,,0.0,,,0.0,,0.0,,,,,,,,,,,,,,,,,,,,0.0,,,,,,0.0,,,,,,,,,,,,,,,,,,0.0,,,1.0,0.0,,0.0,,,1.0,,,

output/preprocess/Cervical_Cancer/clinical_data/GSE138080.csv

@@ -1,2 +1,2 @@
 ,GSM4098861,GSM4098862,GSM4098863,GSM4098864,GSM4098865,GSM4098866,GSM4098867,GSM4098868,GSM4098869,GSM4098870,GSM4098871,GSM4098872,GSM4098873,GSM4098874,GSM4098875,GSM4098876,GSM4098877,GSM4098878,GSM4098879,GSM4098880,GSM4098881,GSM4098882,GSM4098883,GSM4098884,GSM4098885,GSM4098886,GSM4098887,GSM4098888,GSM4098889,GSM4098890,GSM4098891,GSM4098892,GSM4098893,GSM4098894,GSM4098895
-Cervical_Cancer,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
+Cervical_Cancer,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

output/preprocess/Cervical_Cancer/clinical_data/GSE63678.csv

@@ -1,2 +1,2 @@
-,GSM1555075,GSM1555076,GSM1555077,GSM1555078,GSM1555079,GSM1555080,GSM1555081,GSM1555082,GSM1555083,GSM1555084,GSM1555085,GSM1555086,GSM1555087,GSM1555088,GSM1555089,GSM1555090,GSM1555091,GSM1555092,GSM1555093,GSM1555094,GSM1555095,GSM1555096,GSM1555097,GSM1555098,GSM1555099,GSM1555100,GSM1555101,GSM1555102,GSM1555103,GSM1555104,GSM1555105,GSM1555106,GSM1555107,GSM1555108,GSM1555109
-Cervical_Cancer,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+,GSM1555075,GSM1555076,GSM1555077,GSM1555078,GSM1555079,GSM1555080,GSM1555081,GSM1555082,GSM1555083,GSM1555084
+Cervical_Cancer,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Cervical_Cancer/clinical_data/GSE75132.csv

@@ -1,2 +1,2 @@
-GSM1943705,GSM1943706,GSM1943707,GSM1943708,GSM1943709,GSM1943710,GSM1943711,GSM1943712,GSM1943713,GSM1943714,GSM1943715,GSM1943716,GSM1943717,GSM1943718,GSM1943719,GSM1943720,GSM1943721,GSM1943722,GSM1943723,GSM1943724,GSM1943725,GSM1943726,GSM1943727,GSM1943728,GSM1943729,GSM1943730,GSM1943731,GSM1943732,GSM1943733,GSM1943734,GSM1943735,GSM1943736,GSM1943737,GSM1943738,GSM1943739,GSM1943740,GSM1943741,GSM1943742,GSM1943743,GSM1943744,GSM1943745
-0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+,GSM1943705,GSM1943706,GSM1943707,GSM1943708,GSM1943709,GSM1943710,GSM1943711,GSM1943712,GSM1943713,GSM1943714,GSM1943715,GSM1943716,GSM1943717,GSM1943718,GSM1943719,GSM1943720,GSM1943721,GSM1943722,GSM1943723,GSM1943724,GSM1943725,GSM1943726,GSM1943727,GSM1943728,GSM1943729,GSM1943730,GSM1943731,GSM1943732,GSM1943733,GSM1943734,GSM1943735,GSM1943736,GSM1943737,GSM1943738,GSM1943739,GSM1943740,GSM1943741,GSM1943742,GSM1943743,GSM1943744,GSM1943745
+Cervical_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Cervical_Cancer/code/GSE107754.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE107754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE107754"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE107754.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE107754.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE107754.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Gene expression availability
+is_gene_available = True  # Whole human genome gene expression microarrays per background
+
+# 2) Determine availability rows based on Sample Characteristics Dictionary
+trait_row = 2    # tissue info including 'tissue: Cervix/Cervical cancer'
+age_row = None   # No age field observed
+gender_row = 0   # 'gender: Female'/'gender: Male'
+
+# 2.2) Conversion functions
+def _after_colon(x):
+    try:
+        parts = str(x).split(":", 1)
+        return parts[1].strip() if len(parts) > 1 else str(x).strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    # Binary: 1 = Cervical/Cervix cancer, 0 = other tissues; unknown if not a tissue field
+    try:
+        s = str(x).strip()
+        # If header not present, try best-effort on the whole string
+        header = s.split(":", 1)[0].strip().lower() if ":" in s else ""
+        value = _after_colon(s)
+        if value is None:
+            return None
+        v = value.lower()
+        if "tissue" in header:
+            if ("cervix" in v) or ("cervical" in v):
+                return 1
+            else:
+                return 0
+        else:
+            # e.g., "biopsy location" or other non-tissue annotations in this row -> unknown
+            return None
+    except Exception:
+        return None
+
+def convert_age(x):
+    # Not available in this dataset
+    return None
+
+def convert_gender(x):
+    try:
+        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
+    except Exception:
+        return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait 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
+    )
+    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 the appropriate columns in the annotation: 'ID' (probe IDs) and 'GENE_SYMBOL' (gene symbols)
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize 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)
+
+# Prepare clinical dataframe from memory if available, else load from saved CSV
+try:
+    clinical_df = selected_clinical_df
+except NameError:
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2) Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Bias check and feature cleanup
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Debug: show resulting shape to aid troubleshooting
+print(f"Unbiased linked data shape: {unbiased_linked_data.shape}")
+
+# Availability flags as native Python bools
+is_gene_available_final = bool(normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+is_trait_available_final = bool((trait in clinical_df.index) and clinical_df.loc[trait].notna().any())
+
+note = "INFO: Trait derived from tissue field; Age unavailable; Gender available."
+
+# 5) Final quality validation and cohort info saving with robustness to legacy JSON issues
+def _finalize_and_save():
+    return 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
+    )
+
+try:
+    is_usable = _finalize_and_save()
+except Exception as e:
+    # If JSON serialization or legacy content issue occurs, reset the JSON file and retry once
+    if os.path.exists(json_path):
+        try:
+            os.remove(json_path)
+        except Exception:
+            pass
+    is_usable = _finalize_and_save()
+
+# 6) Conditionally save the linked data
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Cervical_Cancer/code/GSE114243.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE114243"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE114243"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE114243.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE114243.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE114243.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/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 (likely transcriptome data; not miRNA-only or methylation-only)
+is_gene_available = True
+
+# 2) Variable availability
+# From the sample characteristics, only tissue info (HEK293T cell line) is present; no human clinical variables.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Conversion functions (defined for completeness; not used since rows are None)
+def _after_colon(value):
+    if value is None:
+        return ""
+    s = str(value)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(value):
+    # Binary: 1 = Cervical cancer case; 0 = control/normal/non-cancer.
+    s = _after_colon(value).lower()
+    if s in {"", "na", "n/a", "none", "unknown"}:
+        return None
+    # Heuristics
+    positive_markers = ["cervical cancer", "cervix cancer", "cc", "case", "tumor", "tumour", "cancer", "malignant"]
+    negative_markers = ["normal", "control", "healthy", "benign", "adjacent normal", "non-cancer", "noncancer"]
+    if any(k in s for k in positive_markers):
+        return 1
+    if any(k in s for k in negative_markers):
+        return 0
+    return None
+
+def convert_age(value):
+    # Continuous age in years
+    s = _after_colon(value).lower()
+    if s in {"", "na", "n/a", "none", "unknown"}:
+        return None
+    m = re.search(r'(-?\d+(?:\.\d+)?)', s)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+    except ValueError:
+        return None
+    if age < 0 or age > 120:
+        return None
+    return age
+
+def convert_gender(value):
+    # Binary: female -> 0, male -> 1
+    s = _after_colon(value).lower()
+    if s in {"", "na", "n/a", "none", "unknown"}:
+        return None
+    female_tokens = {"f", "female", "woman", "women", "girl"}
+    male_tokens = {"m", "male", "man", "men", "boy"}
+    if s in female_tokens or any(tok in s for tok in female_tokens):
+        return 0
+    if s in male_tokens or any(tok in s for tok in male_tokens):
+        return 1
+    return None
+
+# 3) Save metadata with initial filtering
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction: skipped because trait_row is None (no clinical data available)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# The identifiers like 'A_23_P...' are Agilent microarray probe IDs, not human gene symbols.
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify columns for probe IDs and gene symbols based on annotation preview
+probe_col = 'ID'           # matches probe IDs like 'A_23_P100001' seen in gene_data index
+gene_symbol_col = 'GENE_SYMBOL'  # contains human gene symbols
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1) Normalize gene symbols and save gene-level matrix
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6) Proceed with linking and final validation only if clinical data exists (not the case for this cohort)
+if ('selected_clinical_data' in locals()) and (locals().get('trait_row', None) is not None):
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias checking and potential removal of biased covariates
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and cohort info saving
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical features available; data linked, missing handled, bias assessed."
+    )
+
+    # Save linked dataset only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # No clinical trait available: skip final validation/linking; only gene matrix saved
+    print("INFO: No clinical trait available; linking and final validation skipped. Saved gene-level matrix only.")

output/preprocess/Cervical_Cancer/code/GSE131027.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression availability
+is_gene_available = True  # Series design: investigation of expression features (not miRNA/methylation)
+
+# 2) Variable availability and converters
+# Trait and cancer types are in key 1. Age not available. Gender can be inferred from cancer type (key 1).
+trait_row = 1
+age_row = None
+gender_row = 1
+
+def _extract_value_after_colon(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        x = str(x)
+    parts = x.split(':')
+    val = parts[-1].strip() if parts else None
+    return val if val != '' else None
+
+def convert_trait(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Binary: 1 = cervical cancer, 0 = other cancers
+    return 1 if 'cervical' in v_low else 0
+
+def convert_age(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        if 0 <= age <= 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # High-confidence sex-specific mappings based on cancer type
+    if 'prostate' in v_low:
+        return 1  # male
+    if ('ovarian' in v_low) or ('cervical' in v_low) or ('vulvovaginal' in v_low):
+        return 0  # female
+    # Other cancer types are not sex-specific -> unknown
+    return None
+
+# 3) Initial filtering metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 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, 1053_at) are not gene symbols and require mapping.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2) Build the mapping dataframe from annotation
+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 data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Ensure clinical features are loaded, then link clinical and genetic data
+try:
+    selected_clinical_df
+except NameError:
+    # Reload clinical data saved in Step 2
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Bias check and 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_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: Gender inferred heuristically from sex-specific cancer types in clinical annotation "
+        "(female=ovarian/cervical/vulvovaginal -> 0; male=prostate -> 1). Other cancer types set to None and "
+        "imputed if needed. Age not available in this dataset.")
+
+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/Cervical_Cancer/code/GSE137034.py

@@ -0,0 +1,136 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE137034"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE137034"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE137034.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE137034.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE137034.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step: Dataset Analysis and Clinical Feature Extraction for GSE137034
+
+# 1) Gene expression data availability
+# Background indicates chromatin accessibility (ATAC-seq) in a SuperSeries; not gene expression suitable for our analysis.
+is_gene_available = False
+
+# 2) Variable availability and converters
+# From the sample characteristics:
+# {0: ['tissue: THP1 cells', 'tissue: Stimulated human CD4 T-cells'],
+#  1: ['treatment: Cells cultured in full RPMI', 'treatment: Cells cultured in RPMI without arginine']}
+# No trait (Cervical Cancer), age, or gender information available.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    return s if s else None
+
+def convert_trait(value):
+    # Binary: 1 = cervical cancer case, 0 = non-cancer/controls.
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vlow = v.lower()
+    # Heuristics for cervical cancer labels
+    if any(k in vlow for k in ['cervical', 'cervix']) and any(k in vlow for k in ['cancer', 'carcinoma', 'scc', 'adenocarcinoma', 'tumor', 'tumour']):
+        return 1
+    if any(k in vlow for k in ['normal', 'healthy', 'control', 'benign', 'non-cancer', 'noncancer']):
+        return 0
+    # Explicit case/control labels
+    if vlow in {'case', 'patient', 'tumor', 'tumour'}:
+        return 1
+    if vlow in {'control', 'healthy', 'normal'}:
+        return 0
+    return None
+
+def convert_age(value):
+    # Continuous: extract a numeric age if present
+    v = _after_colon(value)
+    if v is None:
+        return None
+    import re
+    matches = re.findall(r'\d+\.?\d*', v)
+    if not matches:
+        return None
+    try:
+        age_val = float(matches[0])
+        # Filter unreasonable ages
+        if 0 < age_val < 120:
+            return age_val
+    except Exception:
+        pass
+    return None
+
+def convert_gender(value):
+    # Binary: female=0, male=1
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vlow = v.lower()
+    if vlow in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if vlow in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If at some point trait_row becomes available, the following scaffold shows how to proceed:
+if trait_row is not None:
+    selected = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected.to_csv(out_clinical_data_file, index=True)

output/preprocess/Cervical_Cancer/code/GSE138079.py

@@ -0,0 +1,114 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE138079"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE138079"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE138079.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE138079.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE138079.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/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
+# Decision rationale:
+# - Gene expression: YES (Agilent mRNA microarray on human keratinocyte cell lines)
+# - Trait (Cervical_Cancer): NOT available (in vitro HPV-transformed keratinocyte cell lines; no human case/control)
+# - Age/Gender: NOT available (cell lines; no subject-level age/gender; any implied sex would be constant and unusable)
+
+# Set availability flags and row indices
+is_gene_available = True
+trait_row = None
+age_row = None
+gender_row = None
+is_trait_available = trait_row is not None
+
+# Converters (defined for interface completeness; not used since trait_row is None)
+def convert_trait(x):
+    if x is None:
+        return None
+    # extract value after colon if present
+    val = str(x)
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    v = val.strip().lower()
+    if v in {'', 'na', 'n/a', 'none', 'unknown', 'missing'}:
+        return None
+    # Generic heuristic mapping: cancer/tumor/carcinoma -> 1; normal/control/healthy -> 0
+    if any(k in v for k in ['cancer', 'tumor', 'carcinoma', 'malignant', 'case']):
+        return 1
+    if any(k in v for k in ['normal', 'control', 'healthy', 'benign']):
+        return 0
+    # Not confidently mappable
+    return None
+
+def convert_age(x):
+    if x is None:
+        return None
+    val = str(x)
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    v = val.strip().lower()
+    if v in {'', 'na', 'n/a', 'none', 'unknown', 'missing'}:
+        return None
+    # Extract first numeric token as age (in years) if present
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    val = str(x)
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    v = val.strip().lower()
+    if v in {'', 'na', 'n/a', 'none', 'unknown', 'missing'}:
+        return None
+    # Map female->0, male->1
+    if any(k in v for k in ['female', 'f']):
+        return 0
+    if any(k in v for k in ['male', 'm']):
+        return 1
+    return None
+
+# Save metadata (initial filtering)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Since trait_row is None, skip clinical feature extraction.

output/preprocess/Cervical_Cancer/code/GSE138080.py

@@ -0,0 +1,223 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE138080"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE138080"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE138080.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE138080.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE138080.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # mRNA Agilent whole genome arrays -> gene expression available
+
+# 2) Variable Availability and Data Type Conversion
+
+# Data availability inferred from Sample Characteristics Dictionary:
+# 0: cell type (normal, CIN2/3, carcinoma) -> use for trait
+# 1: HPV status -> not our primary trait
+trait_row = 0
+age_row = None
+gender_row = None
+
+# 2.2 Conversion functions
+
+def _extract_value_after_colon(x):
+    if x is None:
+        return None
+    try:
+        s = str(x).strip()
+    except Exception:
+        return None
+    if not s:
+        return None
+    parts = s.split(":")
+    val = parts[-1].strip() if len(parts) > 1 else s.strip()
+    return val or None
+
+def convert_trait(x):
+    """
+    Binary: 1 = cervical cancer (carcinoma), 0 = normal; CIN2/3 -> None (exclude precancerous for Cervical_Cancer trait).
+    """
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if "carcinoma" in v or "squamous cell carcinoma" in v:
+        return 1
+    if "normal" in v:
+        return 0
+    # High-grade precancerous lesions (CIN2/3) are not cancer -> exclude
+    if "cin" in v or "intraepithelial" in v or "grade" in v:
+        return None
+    return None
+
+def convert_age(x):
+    """
+    Continuous: extract numeric age if present. Not used here (no age available), but provided for completeness.
+    """
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    """
+    Binary: female -> 0, male -> 1.
+    """
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_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
+# Observed gene identifiers are numeric (e.g., '12', '14'), not human gene symbols; mapping 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
+# Decide identifier and gene symbol columns based on annotation preview
+id_col = 'ID'
+possible_symbol_cols = ['GENE_SYMBOL', 'GENE', 'SYMBOL', 'Gene Symbol', 'GENE_NAME']
+gene_symbol_col = next((c for c in possible_symbol_cols if c in gene_annotation.columns), None)
+if gene_symbol_col is None:
+    raise ValueError("No suitable gene symbol column found in gene_annotation.")
+
+# Build mapping DataFrame (probe ID -> gene symbol)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+
+# Map probe-level data to gene-level data
+# Use the probe-level expression DF from previous steps (gene_data) and overwrite with gene-mapped data
+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 with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Ensure output directory exists and save normalized gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Optional sanity check
+print(f"Normalized gene data shape: {normalized_gene_data.shape}")
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+# Fix variable name to use 'selected_clinical_df' created in previous steps.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Optional sanity check
+print(f"Linked data shape (before missing handling): {linked_data.shape}")
+
+# 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.
+# Derive availability flags from data
+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 bool(selected_clinical_df.loc[trait].notna().any())
+
+note = "INFO: Trait derived from cell type (normal vs carcinoma); CIN2/3 excluded from trait."
+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. 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/Cervical_Cancer/code/GSE146114.py

@@ -0,0 +1,131 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE146114"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE146114"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE146114.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE146114.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE146114.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene Expression Data Availability
+# Background indicates Illumina WG-6 v3 / HT-12 v4 mRNA expression arrays.
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics:
+# - Trait (Cervical_Cancer): All samples are cervical tumor patients (case-only). No variability -> not available.
+# - Age: Not present.
+# - Gender: Cervical cancer patients are female; gender not explicitly listed and effectively constant -> not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Conversion functions (defined for interface consistency; may not be used due to unavailability)
+def convert_trait(x):
+    # Map to binary case/control if ever needed:
+    # 1 = Cervical cancer case; 0 = control/normal.
+    if x is None:
+        return None
+    try:
+        val = str(x)
+        # Extract value after colon if present
+        if ':' in val:
+            val = val.split(':', 1)[1].strip()
+        low = val.lower()
+        if any(k in low for k in ['cervical', 'cervix', 'tumor', 'carcinoma']):
+            return 1
+        if any(k in low for k in ['normal', 'healthy', 'control', 'adjacent normal']):
+            return 0
+        return None
+    except Exception:
+        return None
+
+def convert_age(x):
+    # Return age in years as float if a number is present; otherwise None
+    if x is None:
+        return None
+    try:
+        val = str(x)
+        if ':' in val:
+            val = val.split(':', 1)[1].strip()
+        m = re.search(r'(\d+(?:\.\d+)?)', val)
+        return float(m.group(1)) if m else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Female -> 0, Male -> 1
+    if x is None:
+        return None
+    try:
+        val = str(x)
+        if ':' in val:
+            val = val.split(':', 1)[1].strip()
+        low = val.lower()
+        if low in ['f', 'female', 'woman', 'women']:
+            return 0
+        if low in ['m', 'male', 'man', 'men']:
+            return 1
+        return None
+    except Exception:
+        return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Clinical preview:", clinical_preview)
+    # Save clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Cervical_Cancer/code/GSE163114.py

@@ -0,0 +1,128 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE163114"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE163114"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE163114.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE163114.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE163114.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/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  # Cell-line expression study; not miRNA-only or methylation-only per context.
+
+# 2) Variable availability and converters
+# Sample characteristics show only:
+#   0: ['cell line: HeLa'] -> constant; not usable for our 'Cervical_Cancer' trait association
+#   1: ['lentivirus: shRNA control', 'lentivirus: shRNA Ki-67'] -> experimental perturbation, not the trait of interest
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(val: str) -> str:
+    if val is None:
+        return ''
+    s = str(val)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip().strip('"').strip()
+
+def convert_trait(x):
+    # Binary: Cervical_Cancer present (1) vs not (0); heuristic on strings if ever needed.
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    # Heuristic: cell line derived from cervical cancer
+    if any(k in v for k in ['hela', 'cervical']):
+        return 1
+    if any(k in v for k in ['normal', 'control tissue', 'healthy']):
+        return 0
+    # Lentiviral labels are perturbations, not trait; return None to avoid misuse.
+    if 'lentivirus' in v or 'shrna' in v:
+        return None
+    return None
+
+def convert_age(x):
+    # Continuous age in years
+    v = _after_colon(x)
+    if not v:
+        return None
+    # Extract first numeric token (e.g., "45 years", "45.0", "Age: 45")
+    import re
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group(0))
+    except Exception:
+        return None
+    # Filter unrealistic ages
+    if age_val <= 0 or age_val > 120:
+        return None
+    return age_val
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    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 (skip because trait_row is None)
+# If trait_row becomes available in future steps, enable the following:
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age 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_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Cervical_Cancer/code/GSE63678.py

@@ -0,0 +1,275 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE63678"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE63678"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE63678.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE63678.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE63678.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/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
+# Affymetrix HG133A_2.0 microarray indicates mRNA gene expression data.
+is_gene_available = True
+
+# 2) Variable availability and conversion functions
+
+# Keys observed:
+# 0: tissue (vulvar/endometrium/cervix)
+# 1: disease state (carcinoma/normal)
+
+# Trait: Cervical_Cancer inferred from disease state within cervix tissue.
+trait_row = 1  # 'disease state'
+age_row = None  # not available
+gender_row = None  # not available
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    # Take the part after the first colon if present
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip().lower()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    # Map disease state to binary: carcinoma/tumor/cancer/malignant -> 1, normal/healthy/control/benign -> 0
+    if any(k in v for k in ['carcinoma', 'cancer', 'tumor', 'tumour', 'malignan', 'neoplas']):
+        return 1
+    if any(k in v for k in ['normal', 'healthy', 'control', 'benign']):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    # Extract a number possibly followed by units
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    if 'female' in v or v == 'f':
+        return 0
+    if 'male' in v 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 (only if trait data is available)
+if trait_row is not None:
+    # Subset to cervix tissue to align with the Cervical_Cancer trait definition
+    clinical_df = clinical_data.copy()
+    tissue_series = clinical_df.iloc[0, :].astype(str).str.lower()
+    cervix_mask = tissue_series.str.contains('cervix', na=False)
+
+    clinical_df_cervix = clinical_df.loc[:, cervix_mask]
+
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_df_cervix,
+        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
+import os
+import re
+
+requires_gene_mapping = True  # default to conservative mapping requirement
+
+def detect_requires_mapping(index_iterable, sample_size=2000):
+    ids = [str(x) for x in list(index_iterable)[:sample_size]]
+    if not ids:
+        return False
+
+    def is_affy(x):
+        xl = x.lower()
+        return xl.endswith('_at') or xl.endswith('_st') or xl.startswith('affx-')
+
+    def is_ensembl(x):
+        return bool(re.match(r'^ENS[A-Z]*G\d+', x))
+
+    def is_refseq(x):
+        return bool(re.match(r'^(NM|NR|XM|XR)_\d+', x))
+
+    def is_illumina(x):
+        return bool(re.match(r'^ILMN_\d+', x))
+
+    def is_agilent(x):
+        return bool(re.match(r'^A_\d+_P\d+', x))
+
+    def is_ucsc(x):
+        return bool(re.match(r'^uc[0-9a-z]+\.', x))
+
+    def is_symbol_like(x):
+        # Heuristic: alphanumerics with optional - or .; exclude clear non-symbol patterns
+        if any(sep in x for sep in [':', '/', '\\', '|']):
+            return False
+        if is_affy(x) or is_ensembl(x) or is_refseq(x) or is_illumina(x) or is_agilent(x) or is_ucsc(x):
+            return False
+        return bool(re.match(r'^[A-Za-z0-9\-\.\(\)]+$', x)) and any(c.isalpha() for c in x)
+
+    n = len(ids)
+    counts = {
+        'affy': sum(is_affy(x) for x in ids),
+        'ensembl': sum(is_ensembl(x) for x in ids),
+        'refseq': sum(is_refseq(x) for x in ids),
+        'illumina': sum(is_illumina(x) for x in ids),
+        'agilent': sum(is_agilent(x) for x in ids),
+        'ucsc': sum(is_ucsc(x) for x in ids),
+        'symbol_like': sum(is_symbol_like(x) for x in ids),
+    }
+    non_symbol_total = counts['affy'] + counts['ensembl'] + counts['refseq'] + counts['illumina'] + counts['agilent'] + counts['ucsc']
+    # Require mapping if majority are non-symbol identifiers
+    return (non_symbol_total / max(n, 1)) >= 0.5
+
+try:
+    if os.path.exists(out_gene_data_file):
+        import pandas as pd  # safe import if not already present
+        gene_df = pd.read_csv(out_gene_data_file, index_col=0)
+        requires_gene_mapping = detect_requires_mapping(gene_df.index)
+    else:
+        # Fallback for known Affymetrix-like probe IDs observed in this cohort/platform
+        requires_gene_mapping = True
+except Exception:
+    # On any unexpected issue, default to requiring 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 appropriate columns for probe IDs and gene symbols based on the annotation preview
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2) Build the probe-to-gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+# Preserve original probe-level data
+probe_level_data = gene_data
+gene_data = apply_gene_mapping(probe_level_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Ensure clinical features are available in the current scope; otherwise, load from file
+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
+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 cohort metadata
+is_gene_available_flag = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_flag = (trait in linked_data.columns)
+
+note = "INFO: Filtered to cervix tissue only; Affymetrix probes mapped via SOFT Gene Symbol; gene symbols normalized with NCBI synonyms."
+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/Cervical_Cancer/code/GSE75132.py

@@ -0,0 +1,186 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE75132"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE75132"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE75132.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE75132.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE75132.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+# Based on the series summary, this is an mRNA microarray dataset (gene expression).
+is_gene_available = True
+
+# 2) Variable availability and converters
+
+# From Sample Characteristics:
+# 0: 'tissue: cervix' -> constant, not useful.
+# 1: 'category (0 = normal, 1 = hpv without progression, 2 = hpv with progression)' -> relates to HPV progression, not the Cervical_Cancer trait.
+# 2: 'hpv status: ...' -> HPV status, not the Cervical_Cancer trait.
+# 3: 'disease state: none/severe dysplasia/CIS/moderate dysplasia/cancer' -> usable to derive Cervical_Cancer.
+trait_row = 3
+age_row = None
+gender_row = None
+
+# Converters
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ''
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip().strip('"').strip()
+
+def convert_trait(x):
+    v = _after_colon(x).lower()
+    if v in ['', 'na', 'n/a', 'nan', 'unknown']:
+        return None
+    # Binary trait: Cervical cancer present (1) vs not (0)
+    # Map explicit cancer to 1; dysplasia, CIS, none to 0
+    if v in ['cancer', 'cervical cancer']:
+        return 1
+    if v in ['none', 'normal', 'cis', 'carcinoma in situ', 'moderate dysplasia', 'severe dysplasia']:
+        return 0
+    if ('dysplasia' in v) or ('cin' in v):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x).lower()
+    if v in ['', 'na', 'n/a', 'nan', 'unknown']:
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    v = _after_colon(x).lower()
+    if v in ['', 'na', 'n/a', 'nan', 'unknown']:
+        return None
+    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 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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Clinical feature 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
+# 1. Identify the columns for probe IDs and gene symbols in the annotation dataframe
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2. Create the mapping dataframe from annotation
+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 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 some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data
+)
+
+# 6. If the linked data is usable, save it
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Cervical_Cancer/code/TCGA.py

@@ -0,0 +1,238 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cervical_Cancer/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Cervical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import re
+import pandas as pd
+
+# 1) Select the most relevant TCGA cohort directory for the trait
+all_entries = os.listdir(tcga_root_dir)
+subdirs = [d for d in all_entries if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Prefer names containing "cervic" or "cesc"
+pattern = re.compile(r'(cervic|cesc)', re.IGNORECASE)
+matches = [d for d in subdirs if pattern.search(d)]
+
+selected_cohort_dir = None
+if matches:
+    # Prefer the most specific match containing "cervical" first, else pick the first match
+    cervical_matches = [d for d in matches if re.search(r'cervical', d, re.IGNORECASE)]
+    selected = cervical_matches[0] if cervical_matches else matches[0]
+    selected_cohort_dir = os.path.join(tcga_root_dir, selected)
+
+if selected_cohort_dir is None:
+    # No suitable directory found; record and skip
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    tcga_skip_trait = True
+else:
+    tcga_skip_trait = False
+
+# 2) Identify clinical and genetic file paths
+clinical_file_path = None
+genetic_file_path = None
+
+if not tcga_skip_trait:
+    try:
+        clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(selected_cohort_dir)
+    except Exception:
+        # Fallback manual search if helper fails
+        files = os.listdir(selected_cohort_dir)
+        clinical_candidates = [f for f in files if 'clinicalmatrix' in f.lower()]
+        genetic_candidates = [f for f in files if 'pancan' in f.lower()]
+        if clinical_candidates and genetic_candidates:
+            clinical_file_path = os.path.join(selected_cohort_dir, clinical_candidates[0])
+            genetic_file_path = os.path.join(selected_cohort_dir, genetic_candidates[0])
+        else:
+            validate_and_save_cohort_info(
+                is_final=False,
+                cohort="TCGA",
+                info_path=json_path,
+                is_gene_available=bool(genetic_candidates),
+                is_trait_available=bool(clinical_candidates)
+            )
+            tcga_skip_trait = True
+
+# 3) Load both files as DataFrames
+tcga_clinical_df = None
+tcga_genetic_df = None
+
+if not tcga_skip_trait:
+    tcga_clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+    tcga_genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+    # 4) Print the column names of the clinical data
+    print(list(tcga_clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import re
+import pandas as pd
+
+# The list of column names from the previous step
+previous_columns = ['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'additional_treatment_completion_success_outcome', 'adjuvant_rad_therapy_prior_admin', 'age_at_initial_pathologic_diagnosis', 'age_began_smoking_in_years', 'agent_total_dose_count', 'assessment_timepoint_category', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'birth_control_pill_history_usage_category', 'brachytherapy_administered_status', 'brachytherapy_first_reference_point_administered_total_dose', 'brachytherapy_method_other_specify_text', 'brachytherapy_method_type', 'cervical_carcinoma_corpus_uteri_involvement_indicator', 'cervical_carcinoma_pelvic_extension_text', 'cervical_neoplasm_pathologic_margin_involved_text', 'cervical_neoplasm_pathologic_margin_involved_type', 'chemotherapy_negation_radiation_therapy_concurrent_adminstrd_txt', 'chemotherapy_negation_radiation_therapy_concurrnt_nt_dmnstrd_rsn', 'chemotherapy_regimen_type', 'clinical_stage', 'concurrent_chemotherapy_dose', 'days_to_birth', 'days_to_brachytherapy_begin_occurrence', 'days_to_brachytherapy_end_occurrence', 'days_to_chemotherapy_end', 'days_to_chemotherapy_start', 'days_to_collection', 'days_to_death', 'days_to_diagnostic_computed_tomography_performed', 'days_to_diagnostic_mri_performed', 'days_to_fdg_or_ct_pet_performed', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'days_to_performance_status_assessment', 'days_to_radiation_therapy_end', 'days_to_radiation_therapy_start', 'death_cause_text', 'diagnostic_ct_result_outcome', 'diagnostic_mri_result_outcome', 'dose_frequency_text', 'eastern_cancer_oncology_group', 'ectopic_pregnancy_count', 'external_beam_radiation_therapy_administered_status', 'external_beam_radiation_therapy_administrd_prrtc_rgn_lymph_nd_ds', 'fdg_or_ct_pet_performed_outcome', 'female_breast_feeding_or_pregnancy_status_indicator', 'followup_case_report_form_submission_reason', 'form_completion_date', 'gender', 'height', 'histological_type', 'history_of_neoadjuvant_treatment', 'human_papillomavirus_laboratory_procedure_performed_name', 'human_papillomavirus_laboratory_procedure_performed_text', 'human_papillomavirus_other_type_text', 'human_papillomavirus_type', 'hysterectomy_performed_text', 'hysterectomy_performed_type', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'init_pathology_dx_method_other', 'initial_pathologic_diagnosis_method', 'initial_weight', 'is_ffpe', 'keratinizing_squamous_cell_carcinoma_present_indicator', 'lost_follow_up', 'lymph_node_examined_count', 'lymph_node_location_positive_pathology_name', 'lymph_node_location_positive_pathology_text', 'lymphovascular_invasion_indicator', 'menopause_status', 'neoplasm_histologic_grade', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_post_initial_therapy_diagnosis_method_text', 'new_neoplasm_event_post_initial_therapy_diagnosis_method_type', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'number_of_lymphnodes_positive_by_he', 'number_of_lymphnodes_positive_by_ihc', 'number_of_successful_pregnancies_which_resultd_n_t_lst_1_lv_brth', 'number_pack_years_smoked', 'oct_embedded', 'oligonucleotide_primer_pair_laboratory_procedure_performed_name', 'other_chemotherapy_agent_administration_specify', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathology_report_file_name', 'patient_death_reason', 'patient_history_immune_system_and_related_disorders_name', 'patient_history_immune_system_and_related_disorders_text', 'patient_id', 'patient_pregnancy_spontaneous_abortion_count', 'patient_pregnancy_therapeutic_abortion_count', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'pregnancy_stillbirth_count', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'radiation_therapy', 'radiation_therapy_not_administered_reason', 'radiation_therapy_not_administered_specify', 'radiation_type_notes', 'residual_disease_post_new_tumor_event_margin_status', 'rt_administered_type', 'rt_pelvis_administered_total_dose', 'sample_type', 'sample_type_id', 'standardized_uptake_value_cervix_uteri_assessment_measurement', 'stopped_smoking_year', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'total_number_of_pregnancies', 'tumor_response_cdus_type', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_CESC_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_CESC_miRNA_HiSeq', '_GENOMIC_ID_TCGA_CESC_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_data/public/TCGA/CESC/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_CESC_PDMRNAseq', '_GENOMIC_ID_TCGA_CESC_RPPA', '_GENOMIC_ID_TCGA_CESC_hMethyl450', '_GENOMIC_ID_TCGA_CESC_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_CESC_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_CESC_mutation', '_GENOMIC_ID_TCGA_CESC_mutation_broad_gene', '_GENOMIC_ID_TCGA_CESC_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_CESC_mutation_curated_wustl_gene', '_GENOMIC_ID_TCGA_CESC_exp_HiSeqV2', '_GENOMIC_ID_TCGA_CESC_gistic2', '_GENOMIC_ID_TCGA_CESC_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_CESC_gistic2thd']
+
+# Refined patterns for age and gender
+age_pattern = re.compile(r'(^|[_\W])age([_\W]|$)')
+birth_pattern = re.compile(r'(^|[_\W])(days_to_birth|year_of_birth|date_of_birth|birth_year)([_\W]|$)')
+gender_pattern = re.compile(r'(^|[_\W])gender([_\W]|$)|(^|[_\W])sex([_\W]|$)')
+
+candidate_age_cols = []
+candidate_gender_cols = []
+
+for col in previous_columns:
+    low = col.lower()
+    if (age_pattern.search(low) or birth_pattern.search(low)) and ('birth_control' not in low and 'stillbirth' not in low):
+        candidate_age_cols.append(col)
+    if gender_pattern.search(low):
+        candidate_gender_cols.append(col)
+
+# Print the required lists in the specified format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Load clinical data and preview candidate columns if available
+clinical_df = None
+try:
+    cohort_dir = None
+    for entry in os.scandir(tcga_root_dir):
+        if entry.is_dir() and 'CESC' in entry.name.upper():
+            cohort_dir = entry.path
+            break
+    if cohort_dir is None:
+        for root, dirs, _ in os.walk(tcga_root_dir):
+            for d in dirs:
+                if 'CESC' in d.upper():
+                    cohort_dir = os.path.join(root, d)
+                    break
+            if cohort_dir is not None:
+                break
+
+    if cohort_dir is not None:
+        clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+        clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, dtype=str)
+except Exception:
+    clinical_df = None
+
+# Extract and preview
+age_preview = {}
+gender_preview = {}
+
+if isinstance(clinical_df, pd.DataFrame):
+    age_cols_existing = [c for c in candidate_age_cols if c in clinical_df.columns]
+    gender_cols_existing = [c for c in candidate_gender_cols if c in clinical_df.columns]
+
+    if age_cols_existing:
+        age_preview = preview_df(clinical_df[age_cols_existing])
+    if gender_cols_existing:
+        gender_preview = preview_df(clinical_df[gender_cols_existing])
+
+print("age_preview =", age_preview)
+print("gender_preview =", gender_preview)
+
+# Step 3: Select Demographic Features
+# Select columns based on preview and typical TCGA conventions
+age_col = 'age_at_initial_pathologic_diagnosis' if 'age_at_initial_pathologic_diagnosis' in candidate_age_cols else None
+gender_col = 'gender' if 'gender' in candidate_gender_cols else None
+
+# Explicitly print chosen columns and their previews
+print("Selected age_col:", age_col)
+print("Preview of selected age_col:", age_preview.get(age_col) if age_col else None)
+
+print("Selected gender_col:", gender_col)
+print("Preview of selected gender_col:", gender_preview.get(gender_col) if gender_col else None)
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# 1) Extract and standardize clinical features (trait, Age, Gender)
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=tcga_clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols and save normalized gene data
+def _looks_like_sample_ids(idx):
+    try:
+        return any(str(i).startswith('TCGA-') for i in list(idx)[:10])
+    except Exception:
+        return any(str(i).startswith('TCGA-') for i in idx)
+
+gene_df = tcga_genetic_df
+# Ensure genes are in index for normalization
+if _looks_like_sample_ids(gene_df.index):
+    gene_df = gene_df.T
+
+normalized_gene_df = normalize_gene_symbols_in_index(gene_df)
+
+# Optional post-check to ensure samples are columns
+if not _looks_like_sample_ids(normalized_gene_df.columns) and _looks_like_sample_ids(normalized_gene_df.index):
+    normalized_gene_df = normalized_gene_df.T
+
+# Save normalized gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on intersecting sample IDs
+common_samples = selected_clinical_df.index.intersection(normalized_gene_df.columns)
+linked_clinical = selected_clinical_df.loc[common_samples]
+linked_gene = normalized_gene_df[common_samples].T  # samples x genes
+linked_data = pd.concat([linked_clinical, linked_gene], axis=1)
+
+# 4) Handle missing values
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine severe bias; remove biased demographics
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+
+# 6) Final validation and save cohort info
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_after = [c for c in linked_data.columns if c not in covariate_cols]
+is_gene_available = bool(len(gene_cols_after) > 0)
+is_trait_available = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+
+note_parts = [
+    f"INFO: Cohort TCGA CESC. Samples linked: {len(linked_data)}.",
+    f"INFO: Gene features retained: {len(gene_cols_after)}.",
+    f"INFO: Age included: {'Age' in linked_data.columns}.",
+    f"INFO: Gender included: {'Gender' in linked_data.columns}.",
+]
+note = " ".join(note_parts)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=bool(trait_biased),
+    df=linked_data,
+    note=note
+)
+
+# 7) Save linked data only 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/Chronic_Fatigue_Syndrome/code/GSE251792.py

@@ -0,0 +1,239 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_Fatigue_Syndrome"
+cohort = "GSE251792"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
+in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE251792"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/GSE251792.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE251792.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE251792.csv"
+json_path = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability (SuperSeries with parsed matrix, likely gene expression)
+is_gene_available = True
+
+# 2) Variable availability based on Sample Characteristics Dictionary
+trait_row = 2    # 'group: Patient' vs 'group: Control'
+age_row = 1      # 'age: <number>'
+gender_row = 0   # 'Sex: Female'/'Sex: Male'
+
+# 2.2) Conversion functions
+def _extract_after_colon(x: str) -> str:
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return str(x)
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"patient", "case", "me/cfs", "mecfs", "cfs", "me/cfs patient"}:
+        return 1
+    if v in {"control", "healthy", "normal", "hc"}:
+        return 0
+    # Heuristics
+    if "patient" in v or "case" in v:
+        return 1
+    if "control" in v or "healthy" in v:
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    # Extract first integer/float in the string
+    m = re.search(r"[-+]?\d*\.?\d+", str(val))
+    if not m:
+        return None
+    num = float(m.group())
+    # Return int if it is an integer value
+    return int(num) if abs(num - int(num)) < 1e-9 else num
+
+def convert_gender(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Save metadata using initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait 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
+    )
+    selected_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
+# Decide which columns to use for mapping
+probe_col = 'ID'  # Matches probe identifiers like 'SL000001', 'HCE000104' seen in expression data
+gene_symbol_col = 'EntrezGeneSymbol'  # Contains human gene symbols
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import json
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+if 'selected_clinical_df' not in globals():
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise FileNotFoundError(f"Clinical data not found in memory or on disk at: {out_clinical_data_file}")
+
+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 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 = bool(normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+is_trait_available_flag = bool((trait in selected_clinical_df.index) and (selected_clinical_df.loc[trait].notna().sum() > 0))
+
+note = ("INFO: Probes mapped to EntrezGeneSymbol and normalized to standard gene symbols; "
+        "female=0, male=1; missing values handled (genes >20% missing removed, samples >5% missing removed, "
+        "mean/mode imputation applied).")
+
+# Ensure the cohort info JSON exists and is a dict
+os.makedirs(os.path.dirname(json_path), exist_ok=True)
+needs_reset = False
+if os.path.exists(json_path):
+    try:
+        with open(json_path, "r") as f:
+            existing = json.load(f)
+        if not isinstance(existing, dict):
+            needs_reset = True
+    except Exception:
+        needs_reset = True
+else:
+    needs_reset = True
+
+if needs_reset:
+    with open(json_path, "w") as f:
+        json.dump({}, f)
+
+# Call validate_and_save_cohort_info with robust handling in case of serialization issues
+try:
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=bool(is_gene_available_flag),
+        is_trait_available=bool(is_trait_available_flag),
+        is_biased=bool(is_trait_biased),
+        df=unbiased_linked_data,
+        note=note
+    )
+except TypeError:
+    # Reset JSON file and retry once in case prior content was incompatible
+    if os.path.exists(json_path):
+        os.remove(json_path)
+    with open(json_path, "w") as f:
+        json.dump({}, f)
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=bool(is_gene_available_flag),
+        is_trait_available=bool(is_trait_available_flag),
+        is_biased=bool(is_trait_biased),
+        df=unbiased_linked_data,
+        note=note
+    )
+
+# 6. Conditionally save the linked dataset
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_Fatigue_Syndrome/code/GSE39684.py

@@ -0,0 +1,132 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_Fatigue_Syndrome"
+cohort = "GSE39684"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
+in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE39684"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/GSE39684.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE39684.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE39684.csv"
+json_path = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Determine data availability
+# Based on the series description, this is a ViroChip viral microarray, not human gene expression.
+is_gene_available = False
+
+# From the provided sample characteristics, there is no CFS/CFS-related status, no age, and no gender.
+trait_row = None   # Chronic Fatigue Syndrome status not present
+age_row = None     # No age field
+gender_row = None  # Gender not provided; also prostate tissue implies male-only and thus constant/useless
+
+# 2) Define conversion functions (robust, though not used since corresponding rows are None)
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s if s != '' else None
+
+def convert_trait(x):
+    # Map CFS-related labels to binary: CFS=1, controls=0
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = v.lower()
+    # Positive CFS indicators
+    if any(k in s for k in ['cfs', 'chronic fatigue syndrome', 'me/cfs', 'myalgic encephalomyelitis']):
+        return 1
+    # Common control indicators
+    if any(k in s for k in ['control', 'healthy', 'normal', 'non-cfs', 'no cfs']):
+        return 0
+    # Irrelevant fields (e.g., tissue or cohort info) -> unknown for trait
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Extract first integer/float present
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group(1))
+        # Reasonable human age range filter
+        if 0 <= age_val <= 120:
+            return age_val
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = v.lower()
+    # Standard mappings
+    if s in ['male', 'm', 'man', 'boy']:
+        return 1
+    if s in ['female', 'f', 'woman', 'girl']:
+        return 0
+    # Sometimes encoded as 1/0
+    if s in ['1', '0']:
+        return int(s)
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row 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 if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    # Preview and save
+    _ = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Chronic_Fatigue_Syndrome/code/GSE67311.py

@@ -0,0 +1,183 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_Fatigue_Syndrome"
+cohort = "GSE67311"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
+in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE67311"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/GSE67311.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE67311.csv"
+json_path = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Affymetrix Human Gene arrays -> gene expression data
+
+# 2) Variable Availability and Conversion Functions
+
+# Availability based on provided Sample Characteristics Dictionary:
+# - trait (Chronic_Fatigue_Syndrome): key 8 with values Yes/No/-
+trait_row = 8
+# - age: not available
+age_row = None
+# - gender: not available
+gender_row = None
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    s = str(value)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(value):
+    v = _after_colon(value).strip().lower()
+    if v in {"yes", "y", "1", "true", "positive", "pos"}:
+        return 1
+    if v in {"no", "n", "0", "false", "negative", "neg"}:
+        return 0
+    if v in {"-", "na", "n/a", "none", "unknown", ""}:
+        return None
+    # Default heuristic: treat any unrecognized non-empty affirmative-looking token as None for safety
+    return None
+
+def convert_age(value):
+    # Not used (age_row is None), but provided per instruction.
+    v = _after_colon(value).lower()
+    if v in {"", "-", "na", "n/a", "none", "unknown"}:
+        return None
+    # Extract first numeric (integer or float)
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        if 0 <= age <= 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(value):
+    # Not used (gender_row is None), but provided per instruction.
+    v = _after_colon(value).strip().lower()
+    if v in {"female", "f", "woman", "girl", "0"}:
+        return 0
+    if v in {"male", "m", "man", "boy", "1"}:
+        return 1
+    if v in {"-", "na", "n/a", "none", "unknown", ""}:
+        return None
+    # Some datasets code gender as 1/2; map 1->male, 2->female if seen
+    if v == "2":
+        return 0
+    return None
+
+# 3) Save metadata using initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+        # age_row and gender_row are None; converters not needed
+    )
+    clinical_selected_preview = preview_df(selected_clinical_df, n=5)
+    print(clinical_selected_preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine columns for mapping: Probe IDs are in 'ID', gene symbols are embedded in 'gene_assignment'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 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 gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available = (normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+is_trait_available = trait in selected_clinical_df.index
+
+note = "INFO: No age/gender available; trait derived from 'chronic fatigue syndrome' field."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, is_gene_available, is_trait_available, is_trait_biased, unbiased_linked_data, note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_Fatigue_Syndrome/code/TCGA.py

@@ -0,0 +1,84 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_Fatigue_Syndrome"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Chronic_Fatigue_Syndrome/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+
+# Step 1: Select the most relevant TCGA cohort directory for Chronic Fatigue Syndrome (CFS)
+def _normalize_name(s: str) -> str:
+    return (
+        s.lower()
+        .replace("(", "_")
+        .replace(")", "_")
+        .replace("-", "_")
+        .replace("/", "_")
+        .replace(" ", "_")
+    )
+
+synonyms = [
+    "chronic_fatigue_syndrome",
+    "chronicfatiguesyndrome",
+    "myalgic_encephalomyelitis",
+    "myalgicencephalomyelitis",
+    "me_cfs",
+    "me-cfs",
+    "mecfs",
+    "cfs",
+    "fatigue",
+]
+
+dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+matched = []
+for d in dirs:
+    norm = _normalize_name(d)
+    if any(syn in norm for syn in synonyms):
+        matched.append(d)
+
+selected_dir = None
+if matched:
+    # If multiple, choose the one with the longest matching synonym (more specific)
+    def match_score(dname):
+        norm = _normalize_name(dname)
+        hits = [len(s) for s in synonyms if s in norm]
+        return max(hits) if hits else 0
+    matched.sort(key=match_score, reverse=True)
+    selected_dir = matched[0]
+
+# If no suitable directory is found, skip this trait and record metadata
+if not selected_dir:
+    print(f"No suitable TCGA cohort found for trait '{trait}'. Skipping.")
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    clinical_df = None
+    genetic_df = None
+    clinical_file_path = None
+    genetic_file_path = None
+else:
+    # Step 2: Identify clinical and genetic file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    clinical_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/Chronic_Fatigue_Syndrome/cohort_info.json

@@ -1,42 +1 @@
-{
-  "GSE67311": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 133
-  },
-  "GSE39684": {
-    "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
-  },
-  "GSE251792": {
-    "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": 84
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  }
-}
+{"GSE67311": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 133, "note": "INFO: No age/gender available; trait derived from 'chronic fatigue syndrome' field."}, "GSE39684": {"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}, "GSE251792": {"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": 84, "note": "INFO: Probes mapped to EntrezGeneSymbol and normalized to standard gene symbols; female=0, male=1; missing values handled (genes >20% missing removed, samples >5% missing removed, mean/mode imputation applied)."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Chronic_kidney_disease/clinical_data/GSE104948.csv

@@ -1,2 +1,2 @@
 ,GSM2810645,GSM2810646,GSM2810647,GSM2810648,GSM2810649,GSM2810650,GSM2810651,GSM2810652,GSM2810653,GSM2810654,GSM2810655,GSM2810656,GSM2810657,GSM2810658,GSM2810659,GSM2810660,GSM2810661,GSM2810662,GSM2810663,GSM2810664,GSM2810665,GSM2810666,GSM2810667,GSM2810668,GSM2810669,GSM2810670,GSM2810671,GSM2810672,GSM2810673,GSM2810674,GSM2810675,GSM2810676,GSM2810677,GSM2810678,GSM2810679,GSM2810680,GSM2810681,GSM2810682,GSM2810683,GSM2810684,GSM2810685,GSM2810686,GSM2810687,GSM2810688,GSM2810689,GSM2810690,GSM2810691,GSM2810692,GSM2810693,GSM2810694,GSM2810695,GSM2810696,GSM2810697,GSM2810698,GSM2810699,GSM2810700,GSM2810701,GSM2810702,GSM2810703,GSM2810704,GSM2810705,GSM2810706,GSM2810707,GSM2810708,GSM2810709,GSM2810710,GSM2810711,GSM2810712,GSM2810713,GSM2810714,GSM2810715,GSM2810716,GSM2810717,GSM2810718,GSM2810719,GSM2810720,GSM2810721,GSM2810722,GSM2810723,GSM2810724,GSM2810725,GSM2810726,GSM2810727,GSM2810728,GSM2810729,GSM2810730,GSM2810731,GSM2810732,GSM2810733,GSM2810734,GSM2810735,GSM2810736,GSM2810737,GSM2810738,GSM2810739,GSM2810740,GSM2810741,GSM2810742,GSM2810743,GSM2810744,GSM2810745,GSM2810746,GSM2810747,GSM2810748,GSM2810749,GSM2810750,GSM2810751,GSM2810752,GSM2810753,GSM2810754,GSM2810755,GSM2810756,GSM2810757,GSM2810758,GSM2810759,GSM2810760,GSM2810761,GSM2810762,GSM2810763,GSM2810764,GSM2810765,GSM2810766,GSM2810767,GSM2810768,GSM2810769
-Chronic_kidney_disease,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Chronic_kidney_disease,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Chronic_kidney_disease/clinical_data/GSE104954.csv

@@ -1,2 +1,2 @@
-,GSM2810894,GSM2810895,GSM2810896,GSM2810897,GSM2810898,GSM2810899,GSM2810900,GSM2810901,GSM2810902,GSM2810903,GSM2810904,GSM2810905,GSM2810906,GSM2810907,GSM2810908,GSM2810909,GSM2810910,GSM2810911,GSM2810912,GSM2810913,GSM2810914,GSM2810915,GSM2810916,GSM2810917,GSM2810918,GSM2810919,GSM2810920,GSM2810921,GSM2810922,GSM2810923,GSM2810924,GSM2810925,GSM2810926,GSM2810927,GSM2810928,GSM2810929,GSM2810930,GSM2810931,GSM2810932,GSM2810933,GSM2810934,GSM2810935,GSM2810936,GSM2810937,GSM2810938,GSM2810939,GSM2810940,GSM2810941,GSM2810942,GSM2810943,GSM2810944,GSM2810945,GSM2810946,GSM2810947,GSM2810948,GSM2810949,GSM2810950,GSM2810951,GSM2810952,GSM2810953,GSM2810954,GSM2810955,GSM2810956,GSM2810957,GSM2810958,GSM2810959,GSM2810960,GSM2810961,GSM2810962,GSM2810963,GSM2810964,GSM2810965,GSM2810966,GSM2810967,GSM2810968,GSM2810969,GSM2810970,GSM2810971,GSM2810972,GSM2810973,GSM2810974,GSM2810975,GSM2810976,GSM2810977,GSM2810978,GSM2810979,GSM2810980,GSM2810981,GSM2810982,GSM2810983,GSM2810984,GSM2810985,GSM2810986,GSM2810987,GSM2810988,GSM2810989,GSM2810990,GSM2810991,GSM2810992,GSM2810993,GSM2810994,GSM2810995,GSM2810996,GSM2810997,GSM2810998,GSM2810999,GSM2811000,GSM2811001,GSM2811002,GSM2811003,GSM2811004,GSM2811005,GSM2811006,GSM2811007,GSM2811008,GSM2811009,GSM2811010,GSM2811011,GSM2811012,GSM2811013,GSM2811014,GSM2811015,GSM2811016,GSM2811017,GSM2811018,GSM2811019,GSM2811020,GSM2811021,GSM2811022,GSM2811023,GSM2811024,GSM2811025,GSM2811026,GSM2811027,GSM2811028
+Feature,GSM2810894,GSM2810895,GSM2810896,GSM2810897,GSM2810898,GSM2810899,GSM2810900,GSM2810901,GSM2810902,GSM2810903,GSM2810904,GSM2810905,GSM2810906,GSM2810907,GSM2810908,GSM2810909,GSM2810910,GSM2810911,GSM2810912,GSM2810913,GSM2810914,GSM2810915,GSM2810916,GSM2810917,GSM2810918,GSM2810919,GSM2810920,GSM2810921,GSM2810922,GSM2810923,GSM2810924,GSM2810925,GSM2810926,GSM2810927,GSM2810928,GSM2810929,GSM2810930,GSM2810931,GSM2810932,GSM2810933,GSM2810934,GSM2810935,GSM2810936,GSM2810937,GSM2810938,GSM2810939,GSM2810940,GSM2810941,GSM2810942,GSM2810943,GSM2810944,GSM2810945,GSM2810946,GSM2810947,GSM2810948,GSM2810949,GSM2810950,GSM2810951,GSM2810952,GSM2810953,GSM2810954,GSM2810955,GSM2810956,GSM2810957,GSM2810958,GSM2810959,GSM2810960,GSM2810961,GSM2810962,GSM2810963,GSM2810964,GSM2810965,GSM2810966,GSM2810967,GSM2810968,GSM2810969,GSM2810970,GSM2810971,GSM2810972,GSM2810973,GSM2810974,GSM2810975,GSM2810976,GSM2810977,GSM2810978,GSM2810979,GSM2810980,GSM2810981,GSM2810982,GSM2810983,GSM2810984,GSM2810985,GSM2810986,GSM2810987,GSM2810988,GSM2810989,GSM2810990,GSM2810991,GSM2810992,GSM2810993,GSM2810994,GSM2810995,GSM2810996,GSM2810997,GSM2810998,GSM2810999,GSM2811000,GSM2811001,GSM2811002,GSM2811003,GSM2811004,GSM2811005,GSM2811006,GSM2811007,GSM2811008,GSM2811009,GSM2811010,GSM2811011,GSM2811012,GSM2811013,GSM2811014,GSM2811015,GSM2811016,GSM2811017,GSM2811018,GSM2811019,GSM2811020,GSM2811021,GSM2811022,GSM2811023,GSM2811024,GSM2811025,GSM2811026,GSM2811027,GSM2811028
 Chronic_kidney_disease,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,

output/preprocess/Chronic_kidney_disease/clinical_data/GSE180393.csv

@@ -1,2 +1,2 @@
-0
-0.0
+,GSM5607752,GSM5607753,GSM5607754,GSM5607755,GSM5607756,GSM5607757,GSM5607758,GSM5607759,GSM5607760,GSM5607761,GSM5607762,GSM5607763,GSM5607764,GSM5607765,GSM5607766,GSM5607767,GSM5607768,GSM5607769,GSM5607770,GSM5607771,GSM5607772,GSM5607773,GSM5607774,GSM5607775,GSM5607776,GSM5607777,GSM5607778,GSM5607779,GSM5607780,GSM5607781,GSM5607782,GSM5607783,GSM5607784,GSM5607785,GSM5607786,GSM5607787,GSM5607788,GSM5607789,GSM5607790,GSM5607791,GSM5607792,GSM5607793,GSM5607794,GSM5607795,GSM5607796,GSM5607797,GSM5607798,GSM5607799,GSM5607800,GSM5607801,GSM5607802,GSM5607803,GSM5607804,GSM5607805,GSM5607806,GSM5607807,GSM5607808,GSM5607809,GSM5607810,GSM5607811,GSM5607812,GSM5607813
+Chronic_kidney_disease,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Chronic_kidney_disease/clinical_data/GSE180394.csv

@@ -1,2 +1,2 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21
-0.0,1.0,1.0,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0
+,GSM5607814,GSM5607815,GSM5607816,GSM5607817,GSM5607818,GSM5607819,GSM5607820,GSM5607821,GSM5607822,GSM5607823,GSM5607824,GSM5607825,GSM5607826,GSM5607827,GSM5607828,GSM5607829,GSM5607830,GSM5607831,GSM5607832,GSM5607833,GSM5607834,GSM5607835,GSM5607836,GSM5607837,GSM5607838,GSM5607839,GSM5607840,GSM5607841,GSM5607842,GSM5607843,GSM5607844,GSM5607845,GSM5607846,GSM5607847,GSM5607848,GSM5607849,GSM5607850,GSM5607851,GSM5607852,GSM5607853,GSM5607854,GSM5607855,GSM5607856,GSM5607857,GSM5607858,GSM5607859,GSM5607860,GSM5607861,GSM5607862,GSM5607863,GSM5607864,GSM5607865,GSM5607866,GSM5607867,GSM5607868,GSM5607869,GSM5607870,GSM5607871,GSM5607872
+Chronic_kidney_disease,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Chronic_kidney_disease/clinical_data/GSE45980.csv

@@ -1,4 +1,4 @@
-GSM1121040,GSM1121041,GSM1121042,GSM1121043,GSM1121044,GSM1121045,GSM1121046,GSM1121047,GSM1121048,GSM1121049,GSM1121050,GSM1121051,GSM1121052,GSM1121053,GSM1121054,GSM1121055,GSM1121056,GSM1121057,GSM1121058,GSM1121059,GSM1121060,GSM1121061,GSM1121062,GSM1121063,GSM1121064,GSM1121065,GSM1121066,GSM1121067,GSM1121068,GSM1121069,GSM1121070,GSM1121071,GSM1121072,GSM1121073,GSM1121074,GSM1121075,GSM1121076,GSM1121077,GSM1121078,GSM1121079,GSM1121080,GSM1121081,GSM1121082
-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
-72.0,20.0,64.0,17.0,46.0,55.0,74.0,49.0,20.0,42.0,73.0,63.0,33.0,74.0,24.0,45.0,70.0,60.0,67.0,31.0,53.0,67.0,22.0,54.0,40.0,38.0,19.0,28.0,65.0,74.0,65.0,54.0,58.0,56.0,34.0,31.0,64.0,59.0,70.0,58.0,67.0,54.0,61.0
-1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0
+,GSM1121040,GSM1121041,GSM1121042,GSM1121043,GSM1121044,GSM1121045,GSM1121046,GSM1121047,GSM1121048,GSM1121049,GSM1121050,GSM1121051,GSM1121052,GSM1121053,GSM1121054,GSM1121055,GSM1121056,GSM1121057,GSM1121058,GSM1121059,GSM1121060,GSM1121061,GSM1121062,GSM1121063,GSM1121064,GSM1121065,GSM1121066,GSM1121067,GSM1121068,GSM1121069,GSM1121070,GSM1121071,GSM1121072,GSM1121073,GSM1121074,GSM1121075,GSM1121076,GSM1121077,GSM1121078,GSM1121079,GSM1121080,GSM1121081,GSM1121082
+Chronic_kidney_disease,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,72.0,20.0,64.0,17.0,46.0,55.0,74.0,49.0,20.0,42.0,73.0,63.0,33.0,74.0,24.0,45.0,70.0,60.0,67.0,31.0,53.0,67.0,22.0,54.0,40.0,38.0,19.0,28.0,65.0,74.0,65.0,54.0,58.0,56.0,34.0,31.0,64.0,59.0,70.0,58.0,67.0,54.0,61.0
+Gender,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0

output/preprocess/Chronic_kidney_disease/clinical_data/GSE66494.csv

@@ -1,2 +1,2 @@
-#01,#02,#03,#04,#05,#06,#07,#08,#09,#10,#11,#12,#13,#14,#15,#16,#17,#18,#19,#20,#21,#22,#23,#24,#26,#27,#28,#29,#30,#31
-,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,GSM1623299,GSM1623300,GSM1623301,GSM1623302,GSM1623303,GSM1623304,GSM1623305,GSM1623306,GSM1623307,GSM1623308,GSM1623309,GSM1623310,GSM1623311,GSM1623312,GSM1623313,GSM1623314,GSM1623315,GSM1623316,GSM1623317,GSM1623318,GSM1623319,GSM1623320,GSM1623321,GSM1623322,GSM1623323,GSM1623324,GSM1623325,GSM1623326,GSM1623327,GSM1623328,GSM1623329,GSM1623330,GSM1623331,GSM1623332,GSM1623333,GSM1623334,GSM1623335,GSM1623336,GSM1623337,GSM1623338,GSM1623339,GSM1623340,GSM1623341,GSM1623342,GSM1623343,GSM1623344,GSM1623345,GSM1623346,GSM1623347,GSM1623348,GSM1623349,GSM1623350,GSM1623351,GSM1623352,GSM1623353,GSM1623354,GSM1623355,GSM1623356,GSM1623357,GSM1623358,GSM1623359
+Chronic_kidney_disease,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0

output/preprocess/Chronic_kidney_disease/code/GSE104948.py

@@ -0,0 +1,181 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE104948"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE104948"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE104948.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE104948.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE104948.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Affymetrix microarrays -> gene expression data
+
+# 2) Variable Availability and Converters
+# From the sample characteristics dictionary:
+# 0: tissue
+# 1: diagnosis
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    return s if s else None
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    low = val.lower()
+    # Controls (non-CKD) heuristics
+    if any(k in low for k in ['tumor', 'donor', 'living', 'control', 'healthy', 'normal']):
+        return 0
+    if low in {'na', 'n/a', 'unknown', 'not available', 'missing'}:
+        return None
+    # All other diagnoses are considered CKD cases
+    return 1
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    # Extract the first number (years assumed)
+    m = re.search(r'[-+]?\d*\.?\d+', val)
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    low = val.lower()
+    if low in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    if low in {'male', 'm', 'man', 'boy'}:
+        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 trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age 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
+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 mapping columns and create mapping dataframe
+# From preview, probe IDs in expression data match 'ID' in annotation; gene symbols are in 'Symbol'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3) Apply gene 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.
+# Fallback: load clinical data from saved CSV if not present in memory
+if 'selected_clinical_df' not in globals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values 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: Diagnosis-based heuristic used for case/control: donors/tumor/normal/healthy treated as controls (0); "
+        "all other diagnoses treated as CKD cases (1).")
+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/Chronic_kidney_disease/code/GSE104954.py

@@ -0,0 +1,209 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE104954"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE104954"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE104954.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE104954.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE104954.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix microarrays with Human Entrez Gene ID -> gene expression
+
+# 2) Variable availability and conversion
+
+# Available keys from sample characteristics:
+# 0: tissue (constant)
+# 1: diagnosis (variable; can infer CKD status)
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+
+    # Controls / non-CKD indicators
+    control_markers = ['tumor nephrectomy', 'living donor', 'donor', 'control', 'healthy']
+    if any(k in v_low for k in control_markers):
+        return 0
+
+    # CKD / kidney disease indicators
+    ckd_markers = [
+        'nephro',                # captures nephropathy, nephritis
+        'glomerulo',             # glomerulonephropathy
+        'iga',                   # IgA nephropathy
+        'lupus',                 # lupus nephritis
+        'diabetic',
+        'hypertensive',
+        'focal segmental glomerulosclerosis',
+        'fsgs',
+        'minimal change',
+        'thin membr',            # thin membrane disease
+        'membranous'
+    ]
+    if any(k in v_low for k in ckd_markers):
+        return 1
+
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    age = float(m.group(1))
+    if 0 <= age <= 120:
+        return age
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    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 (since trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.index.name = 'Feature'
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping based on preview:
+# Expression IDs look like '10000_at', which match the 'ID' column in annotation.
+# Gene symbols are in the 'Symbol' column.
+probe_col = 'ID'
+gene_symbol_col = 'Symbol'
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping to convert probe-level 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 check (and remove biased demographic features if any)
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Trait appears highly imbalanced (few controls vs many CKD cases); dataset likely flagged as biased."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_kidney_disease/code/GSE127136.py

@@ -0,0 +1,134 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE127136"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE127136"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE127136.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE127136.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE127136.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine gene expression availability (scRNA-seq => gene expression data available)
+is_gene_available = True
+
+# Determine variable availability from Sample Characteristics Dictionary:
+# 0: patients, 1: disease state (IgAN, kidney cancer, normal), 2: tissue/cell type
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Conversion functions
+def _extract_value(val):
+    if val is None:
+        return None
+    try:
+        s = str(val)
+    except Exception:
+        return None
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(val):
+    v = _extract_value(val)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Map to CKD presence: IgAN is CKD; normal and kidney cancer are not CKD
+    if "igan" in v_low or "iga nephropathy" in v_low:
+        return 1
+    if "normal" in v_low:
+        return 0
+    if "kidney cancer" in v_low or "cancer" in v_low:
+        return 0
+    return None
+
+def convert_age(val):
+    v = _extract_value(val)
+    if v is None:
+        return None
+    # Extract 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(val):
+    v = _extract_value(val)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {"male", "m"}:
+        return 1
+    if v_low in {"female", "f"}:
+        return 0
+    return None
+
+# Initial filtering metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction 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=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Selected clinical features preview:", preview)
+
+    # Ensure output directory exists and save
+    import os
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])

output/preprocess/Chronic_kidney_disease/code/GSE142153.py

@@ -0,0 +1,206 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE142153"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE142153"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE142153.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE142153.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE142153.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # PBMC transcriptional profiling via microarray implies gene expression data
+
+# 2) Variable availability and converters
+trait_row = 1  # 'diagnosis' field
+age_row = None
+gender_row = None
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower().strip()
+    # CKD cases: diabetic nephropathy or ESRD (include common synonyms)
+    if (
+        "esrd" in v_low
+        or "end stage renal disease" in v_low
+        or "end-stage renal disease" in v_low
+        or "end stage renal" in v_low
+        or "end-stage renal" in v_low
+    ):
+        return 1
+    if "diabetic nephropathy" in v_low or v_low == "dn":
+        return 1
+    if "chronic kidney disease" in v_low or "ckd" in v_low:
+        return 1
+    # Controls
+    if (
+        "healthy" in v_low
+        or "healthy control" in v_low
+        or "healthy donor" in v_low
+        or "normal" in v_low
+        or "control" in v_low
+    ):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower().strip()
+    if v_low in {"male", "m"}:
+        return 1
+    if v_low in {"female", "f"}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+# Note: is_final=False will only record metadata for datasets that fail initial filtering.
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print(clinical_preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for mapping based on the preview:
+# Probe identifiers match the 'ID' column; gene symbols are in 'GENE_SYMBOL'.
+probe_id_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# Preserve the original probe-level data
+probe_level_data = gene_data
+
+# Build mapping and apply to convert probe-level to gene-level expression
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+gene_data = apply_gene_mapping(probe_level_data, 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
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Explicitly cast to built-in Python bool to avoid serialization issues
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+is_trait_biased = bool(is_trait_biased)
+
+note = str("INFO: Only trait available; no age/gender fields in clinical annotations.")
+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/Chronic_kidney_disease/code/GSE180393.py

@@ -0,0 +1,310 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE180393"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE180393"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE180393.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE180393.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE180393.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import math
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix microarray platform with glomerular gene expression
+
+# 2) Variable availability and conversion functions
+# From the sample characteristics, key 0 contains disease/control grouping; key 1 is constant tissue info.
+trait_row = 0
+age_row = None
+gender_row = None
+
+def _extract_value_after_colon(x):
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return None
+    s = str(x).strip().strip('"').strip("'")
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip() if s.strip() != '' else None
+
+def convert_trait(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Controls (no CKD): living donors and unaffected parts of tumor nephrectomy
+    if 'living donor' in vl or 'unaffected parts of tumor nephrectomy' in vl:
+        return 0
+    # All other disease groups are considered CKD cases in this cohort context
+    return 1
+
+def convert_age(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    v = v.replace('years', '').replace('year', '').strip()
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in ['male', 'm', 'man', 'boy']:
+        return 1
+    if vl in ['female', 'f', 'woman', 'girl']:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 and choose mapping columns robustly
+annot_cols = list(gene_annotation.columns)
+
+# 1) Choose probe/ID column
+id_candidates = ['ID', 'ID_REF', 'Probe Set ID', 'probeset_id', 'PROBESET_ID']
+id_col = next((c for c in id_candidates if c in annot_cols), None)
+if id_col is None:
+    id_col = annot_cols[0]
+
+# 2) Try to find a true gene symbol column
+symbol_priority = [
+    'Gene Symbol', 'GENE_SYMBOL', 'GeneSymbol', 'Symbol', 'SYMBOL',
+    'gene_assignment', 'Gene Assignment', 'GENE_ASSIGNMENT',
+    'Associated Gene Name', 'ASSOCIATED_GENE_NAME', 'Associated_Gene_Name',
+    'Gene symbol', 'gene symbol', 'Gene Title', 'GENE_TITLE', 'gene title',
+    'DESCRIPTION', 'Description'
+]
+# Build candidates: prioritize known names, then any column containing 'symbol' or 'assign' or 'title'/'desc'
+symbol_candidates = [c for c in symbol_priority if c in annot_cols]
+symbol_candidates += [c for c in annot_cols if ('symbol' in c.lower()) and (c not in symbol_candidates)]
+symbol_candidates += [c for c in annot_cols if ('assign' in c.lower()) and (c not in symbol_candidates)]
+symbol_candidates += [c for c in annot_cols if (('title' in c.lower()) or ('desc' in c.lower())) and (c not in symbol_candidates)]
+# Exclude columns that are clearly IDs (e.g., Entrez) from symbol candidates
+symbol_candidates = [c for c in symbol_candidates if 'entrez' not in c.lower()]
+
+best_col = None
+best_ratio = -1.0
+sample_n = 2000
+for c in symbol_candidates:
+    series_sample = gene_annotation[c].dropna().astype(str).head(sample_n)
+    if len(series_sample) == 0:
+        continue
+    extracted = series_sample.apply(extract_human_gene_symbols)
+    non_empty = extracted.apply(lambda lst: isinstance(lst, list) and len(lst) > 0).sum()
+    ratio = non_empty / len(series_sample)
+    if ratio > best_ratio:
+        best_ratio = ratio
+        best_col = c
+
+probe_data = gene_data  # original probe-level expression
+
+if best_col is not None and best_ratio > 0:
+    # Map probes to true gene symbols
+    gene_symbol_col = best_col
+    mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+    gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+    print(f"Mapped probe-level data to gene symbols via column '{gene_symbol_col}'. Genes: {gene_data.shape[0]}")
+else:
+    # Strict fallback: map to Entrez IDs only if necessary, using controlled parsing to avoid token explosion
+    if 'ENTREZ_GENE_ID' not in annot_cols:
+        raise RuntimeError(
+            "No suitable gene symbol column found in annotation, and ENTREZ_GENE_ID is unavailable. "
+            "Cannot perform probe-to-gene mapping safely."
+        )
+
+    def extract_entrez_ids_strict(cell):
+        # Accept only clean numeric Entrez IDs separated by common delimiters
+        s = '' if cell is None else str(cell)
+        # Normalize delimiters
+        for sep in ['///', '//', '||', '|', ';', ',', '\t', '\r', '\n']:
+            s = s.replace(sep, ' ')
+        tokens = [t for t in s.strip().split() if t]
+        ids = [t for t in tokens if t.isdigit() and t != '0']
+        # deduplicate preserving order
+        seen = set()
+        out = []
+        for i in ids:
+            if i not in seen:
+                seen.add(i)
+                out.append(i)
+        return out
+
+    # Build mapping to Entrez IDs
+    mapping_df = gene_annotation.loc[:, [id_col, 'ENTREZ_GENE_ID']].dropna()
+    mapping_df = mapping_df.rename(columns={id_col: 'ID', 'ENTREZ_GENE_ID': 'Gene'})
+    mapping_df['ID'] = mapping_df['ID'].astype(str).str.strip()
+    mapping_df = mapping_df[mapping_df['ID'] != '']
+    mapping_df['Gene'] = mapping_df['Gene'].apply(extract_entrez_ids_strict)
+    mapping_df['num_genes'] = mapping_df['Gene'].apply(len)
+    mapping_df = mapping_df.explode('Gene').dropna(subset=['Gene'])
+    if mapping_df.empty:
+        raise RuntimeError("After strict Entrez parsing, no probe-to-gene mappings remain. Aborting mapping step.")
+    mapping_df.set_index('ID', inplace=True)
+
+    merged_df = mapping_df.join(probe_data, how='inner')
+    expr_cols = [col for col in merged_df.columns if col not in ['Gene', 'num_genes']]
+    merged_df[expr_cols] = merged_df[expr_cols].div(merged_df['num_genes'].replace(0, 1), axis=0)
+    gene_data = merged_df.groupby('Gene')[expr_cols].sum()
+
+    # Sanity check on gene count to avoid spurious explosions
+    if gene_data.shape[0] > 100000:
+        raise RuntimeError(
+            f"Implausible number of unique genes after Entrez fallback ({gene_data.shape[0]}). "
+            "Stopping to prevent propagating erroneous mappings."
+        )
+    print(f"WARNING: No reliable gene symbol column found; aggregated to Entrez Gene IDs instead. "
+          f"Entrez genes: {gene_data.shape[0]}")
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# Ensure clinical features are available in this step
+try:
+    selected_clinical_df  # check existence
+except NameError:
+    # Load the clinical dataframe saved in Step 2
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols or skip if Entrez IDs
+note_msgs = []
+# gene_data is produced in Step 6
+try:
+    gene_data
+except NameError:
+    # Fallback: if prior variable is missing but normalized gene data was saved, reload it as gene_data for downstream linking
+    if os.path.exists(out_gene_data_file):
+        gene_data = pd.read_csv(out_gene_data_file, index_col=0)
+    else:
+        raise RuntimeError("gene_data is not available and no saved gene data file was found.")
+
+# Determine if indices are Entrez IDs (all digits)
+is_entrez = pd.Series(gene_data.index.astype(str)).str.fullmatch(r'\d+').all()
+if bool(is_entrez):
+    normalized_gene_data = gene_data.copy()
+    note_msgs.append("INFO: Gene indices are Entrez IDs; skipped symbol normalization.")
+else:
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    note_msgs.append("INFO: Gene symbols normalized with synonym dictionary.")
+
+# Ensure output directory exists and save normalized 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. Determine bias and remove biased demographic features (trait kept for usability decision)
+if (trait in linked_data.columns) and (len(linked_data) > 0):
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+else:
+    is_trait_biased, unbiased_linked_data = True, linked_data
+
+# 5. Final validation and save cohort info
+# Cast to native Python bool to avoid JSON serialization errors
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+trait_in_index = bool(trait in selected_clinical_df.index)
+trait_has_values = bool(selected_clinical_df.loc[trait].notna().any()) if trait_in_index else False
+is_trait_available_final = bool(trait_in_index and trait_has_values)
+
+note = " ".join(note_msgs)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data 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/Chronic_kidney_disease/code/GSE180394.py

@@ -0,0 +1,394 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE180394"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE180394"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE180394.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE180394.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE180394.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix ST2.1 microarray platform indicates mRNA expression data
+
+# 2) Variable availability and converters
+# 0 -> 'sample group: ...' (variable for trait)
+# 1 -> 'tissue: ...' (constant; not useful)
+trait_row = 0
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    s = s.strip().strip('"').strip("'")
+    return s if s else None
+
+def convert_trait(x):
+    """Binary: CKD (1) vs non-CKD control (0). Controls: living donors and unaffected parts of tumor nephrectomy."""
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower().replace('  ', ' ').strip()
+
+    controls = {
+        'living donor',
+        'living donors',
+        'unaffected parts of tumor nephrectomy',
+        'unaffected part of tumor nephrectomy',
+        'unaffected parts of tumour nephrectomy',
+        'unaffected part of tumour nephrectomy',
+        'unaffected tumour nephrectomy',
+        'unaffected tumor nephrectomy',
+        'healthy',
+        'control',
+        'normal'
+    }
+    if vl in controls:
+        return 0
+    return 1  # all other sample groups are CKD/disease
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        if 0 <= age <= 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if vl in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    assert 'clinical_data' in globals(), "clinical_data is not 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, 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
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import re
+
+# Preserve original expression data
+expr_df = gene_data
+
+# 1) Identify probe and gene identifier columns in the annotation
+if 'ID' not in gene_annotation.columns:
+    raise ValueError("Probe ID column 'ID' not found in gene annotation.")
+probe_col = 'ID'
+
+if 'ENTREZ_GENE_ID' not in gene_annotation.columns:
+    raise ValueError("Entrez ID column 'ENTREZ_GENE_ID' not found in gene annotation.")
+entrez_col = 'ENTREZ_GENE_ID'
+
+print(f"Selected probe ID column: {probe_col}")
+print(f"Selected gene identifier column: {entrez_col} (Entrez IDs)")
+
+# 2) Build mapping: probe ID -> Entrez gene IDs (handle multiple Entrez IDs per probe)
+ann = gene_annotation[[probe_col, entrez_col]].dropna()
+ann[probe_col] = ann[probe_col].astype(str).str.strip()
+
+# Keep only probes present in the expression matrix
+ann = ann[ann[probe_col].isin(expr_df.index)]
+
+# Extract all numeric Entrez IDs from the annotation field (handles multi-mapping)
+ann['__entrez_list__'] = ann[entrez_col].astype(str).apply(lambda s: re.findall(r'\d+', s))
+# Explode to one mapping per (probe, entrez)
+m = ann[[probe_col, '__entrez_list__']].explode('__entrez_list__').dropna()
+m = m.rename(columns={probe_col: 'ID', '__entrez_list__': 'Gene'})
+# Drop empty entries if any
+m = m[m['Gene'].astype(str).str.len() > 0]
+
+# If no mappings found, fail explicitly
+if m.empty:
+    raise ValueError("No valid Entrez mappings were extracted from the annotation; mapping would be empty.")
+
+# 3) Distribute probe signal equally across mapped genes and aggregate to Entrez level
+counts = m.groupby('ID').size().rename('num_genes')
+m = m.join(counts, on='ID')
+
+# Join expression values
+m = m.set_index('ID').join(expr_df, how='inner')
+
+# If join yields no rows, something is inconsistent
+if m.empty:
+    raise ValueError("After joining mappings with expression data, no rows remain. "
+                     "Probe IDs in annotation may not match expression matrix.")
+
+expr_cols = [c for c in m.columns if c not in ['Gene', 'num_genes']]
+# Avoid division by zero just in case
+m['num_genes'] = m['num_genes'].replace(0, 1)
+m[expr_cols] = m[expr_cols].div(m['num_genes'], axis=0)
+
+# Sum per Entrez Gene ID
+gene_data = m.groupby('Gene')[expr_cols].sum()
+
+# Final sanity check
+if gene_data.shape[0] == 0:
+    raise ValueError("Gene mapping produced an empty gene_data (0 genes) after aggregation.")
+
+print(f"Mapped gene_data shape (Entrez-level): {gene_data.shape}")
+print(f"First 10 Entrez IDs: {list(gene_data.index[:10])}")
+
+# Step 7: Gene Identifier Mapping
+import os
+import re
+import pandas as pd
+
+# Reload probe-level expression to ensure we start from probes
+expr_df = get_genetic_data(matrix_file)
+
+# Work on a copy of the annotation
+ann = gene_annotation.copy()
+ann_cols = list(ann.columns)
+print(f"Annotation columns: {ann_cols}")
+
+# Identify probe ID column
+probe_col = 'ID' if 'ID' in ann.columns else ('ID_REF' if 'ID_REF' in ann.columns else None)
+if probe_col is None:
+    raise ValueError(f"No probe ID column found among: {ann_cols}")
+
+# Helper to find a gene-symbol-like column (not used in this cohort but kept for robustness)
+preferred_symbol_cols = [
+    'Gene Symbol', 'GENE_SYMBOL', 'SYMBOL', 'GeneSymbol', 'Gene symbol',
+    'gene_assignment', 'GENE_ASSIGNMENT', 'Gene Assignment', 'Gene assignment',
+    'Associated Gene', 'ASSOCIATED_GENE', 'Associated Genes', 'ASSOCIATED_GENES',
+    'GENE_SYMBOLS', 'Gene Symbols', 'gene_symbols', 'Symbol', 'Symbols'
+]
+
+def find_column(candidates, columns):
+    lower_map = {c.lower(): c for c in columns}
+    for cand in candidates:
+        if cand.lower() in lower_map:
+            return lower_map[cand.lower()]
+    return None
+
+symbol_col = find_column(preferred_symbol_cols, ann_cols)
+
+# If a symbol-bearing column exists and is not Entrez, try symbol mapping first (fallback to Entrez otherwise)
+use_symbol_mapping = symbol_col is not None and symbol_col != 'ENTREZ_GENE_ID'
+gene_expr = None
+
+if use_symbol_mapping:
+    print(f"Selected probe ID column: {probe_col}")
+    print(f"Selected gene-related column for mapping (symbol-bearing): {symbol_col}")
+    mapping_df = get_gene_mapping(ann, prob_col=probe_col, gene_col=symbol_col)
+    mapping_df = mapping_df[mapping_df['ID'].isin(expr_df.index)]
+    gene_expr = apply_gene_mapping(expression_df=expr_df, mapping_df=mapping_df)
+    # Try normalization; skip on failure
+    try:
+        gene_expr = normalize_gene_symbols_in_index(gene_expr)
+    except Exception as e:
+        print(f"WARNING: Gene symbol normalization skipped due to: {e}")
+
+    if gene_expr.shape[0] == 0:
+        print("WARNING: Symbol-based mapping yielded 0 genes. Falling back to Entrez ID-based aggregation.")
+        gene_expr = None
+
+# Robust fallback: aggregate at Entrez ID level using ENTREZ_GENE_ID parsing
+if gene_expr is None:
+    if 'ENTREZ_GENE_ID' not in ann.columns:
+        raise ValueError("ENTREZ_GENE_ID column not found; cannot perform Entrez-level mapping.")
+    print("Proceeding with Entrez ID-based mapping from ENTREZ_GENE_ID.")
+
+    # Prepare the subset annotation and keep only probes present in expression matrix
+    ann_sub = ann[[probe_col, 'ENTREZ_GENE_ID']].dropna()
+    ann_sub[probe_col] = ann_sub[probe_col].astype(str).str.strip()
+    ann_sub = ann_sub[ann_sub[probe_col].isin(expr_df.index)]
+
+    # Parse ENTREZ_GENE_ID with strict tokenization to keep only full numeric IDs; normalize leading zeros
+    def parse_entrez_list(s):
+        if pd.isna(s):
+            return []
+        s = str(s).strip()
+        # Normalize common separators to whitespace
+        for sep in ['///', ';', ',', '|']:
+            s = s.replace(sep, ' ')
+        toks = [t for t in s.split() if t]
+        toks = [t for t in toks if t.isdigit()]
+        # Normalize leading zeros by casting to int then back to str; drop zeros or invalid
+        out = []
+        for t in toks:
+            try:
+                v = int(t)
+                if v > 0:
+                    out.append(str(v))
+            except Exception:
+                continue
+        # Deduplicate while preserving order
+        return list(dict.fromkeys(out))
+
+    ann_sub['__entrez_list__'] = ann_sub['ENTREZ_GENE_ID'].apply(parse_entrez_list)
+
+    # Explode to one (probe, entrez) per row
+    m = ann_sub[[probe_col, '__entrez_list__']].explode('__entrez_list__').dropna()
+    m = m.rename(columns={probe_col: 'ID', '__entrez_list__': 'Gene'})
+    # Drop any empty strings
+    m = m[m['Gene'].astype(str).str.len() > 0]
+
+    if m.empty:
+        raise ValueError("Entrez parsing produced an empty mapping; cannot aggregate to gene level.")
+
+    # Count mapped genes per probe and distribute probe signal equally
+    counts = m.groupby('ID').size().rename('num_genes')
+    m = m.set_index('ID')
+    m = m.join(expr_df, how='inner')
+    m = m.join(counts)
+
+    if m.empty:
+        raise ValueError("After joining mapping with expression data, no rows remain for Entrez aggregation.")
+
+    expr_cols = [c for c in m.columns if c not in ['Gene', 'num_genes']]
+    m['num_genes'] = m['num_genes'].replace(0, 1)
+    m[expr_cols] = m[expr_cols].div(m['num_genes'], axis=0)
+
+    # Aggregate to Entrez ID
+    gene_expr = m.groupby('Gene')[expr_cols].sum()
+
+    # Sanity checks and warnings
+    n_genes = gene_expr.shape[0]
+    if n_genes < 1000 or n_genes > 100000:
+        print(f"WARNING: Unusual number of Entrez genes after mapping: {n_genes}. "
+              "Please verify the platform annotation parsing.")
+
+# Final assignment and reporting
+gene_data = gene_expr
+print(f"Mapped gene_data shape: {gene_data.shape}")
+print(f"First 10 gene identifiers: {list(gene_data.index[:10])}")
+
+# Save the processed gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file, index=True)
+
+# Step 8: Data Normalization and Linking
+import os
+import re
+
+# 1. Normalize gene symbols only if the index looks like gene symbols; skip if Entrez IDs (digits only)
+def _looks_like_entrez(idx_iter):
+    return all(re.fullmatch(r"\d+", str(x)) is not None for x in idx_iter)
+
+if _looks_like_entrez(gene_data.index):
+    normalized_gene_data = gene_data.copy()
+    note = "INFO: Gene symbol normalization skipped because indices are Entrez Gene IDs; proceeding with Entrez-level data."
+else:
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    note = "INFO: Gene symbols normalized using synonym dictionary."
+
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 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. Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    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, index=True)

output/preprocess/Chronic_kidney_disease/code/GSE45980.py

@@ -0,0 +1,214 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE45980"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE45980"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE45980.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE45980.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE45980.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import json
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # mRNA profiling is reported in the series description
+
+# 2) Variable Availability and Data Type Conversion
+# Keys from Sample Characteristics Dictionary:
+trait_row = 3    # 'clinical course: stable/progressive' -> progression status
+age_row = 1      # 'age (yrs): <number>'
+gender_row = 0   # 'gender: male/female'
+
+def _after_colon(x):
+    if x is None:
+        return None
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.strip().lower()
+    if v_low in {"progressive", "progression", "progressor"}:
+        return 1
+    if v_low in {"stable", "non-progressive", "nonprogressive", "non progressor", "non-progressor"}:
+        return 0
+    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:
+        val = float(m.group())
+        return int(val) if val.is_integer() else val
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.strip().lower()
+    if v_low in {"male", "m"}:
+        return 1
+    if v_low in {"female", "f"}:
+        return 0
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide mapping columns based on annotation preview and typical Agilent formats
+prob_col = 'ID'
+if 'GENE_SYMBOL' in gene_annotation.columns:
+    gene_col = 'GENE_SYMBOL'
+elif 'GENE' in gene_annotation.columns:
+    gene_col = 'GENE'
+else:
+    raise ValueError("No suitable gene symbol column found in annotation (expected 'GENE_SYMBOL' or 'GENE').")
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save 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
+# Ensure clinical dataframe is available (load from CSV if needed)
+if 'selected_clinical_df' not in locals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_before = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Track pre-filter stats
+covariate_cols = [trait, 'Age', 'Gender']
+pre_samples = len(linked_before)
+pre_gene_cols = [c for c in linked_before.columns if c not in covariate_cols]
+pre_genes = len(pre_gene_cols)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_before, trait)
+
+# 4. Assess bias and remove biased covariates if necessary
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Track post-filter stats and removals
+post_samples = len(unbiased_linked_data)
+post_gene_cols = [c for c in unbiased_linked_data.columns if c not in [trait, 'Age', 'Gender']]
+post_genes = len(post_gene_cols)
+age_dropped = 'Age' not in unbiased_linked_data.columns and 'Age' in linked_data.columns
+gender_dropped = 'Gender' not in unbiased_linked_data.columns and 'Gender' in linked_data.columns
+
+note_parts = []
+note_parts.append(f"INFO: Linked samples before/after missing-value filtering: {pre_samples} -> {post_samples}.")
+note_parts.append(f"INFO: Gene features before/after filtering: {pre_genes} -> {post_genes}.")
+if age_dropped:
+    note_parts.append("INFO: Age removed due to biased distribution.")
+if gender_dropped:
+    note_parts.append("INFO: Gender removed due to biased distribution.")
+note = " ".join(note_parts)
+
+# 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=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Chronic_kidney_disease/code/GSE60861.py

@@ -0,0 +1,214 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE60861"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE60861"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE60861.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE60861.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE60861.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine data availability
+is_gene_available = True  # SuperSeries includes mRNA expression; likely contains suitable gene expression data
+
+# Step 2: Identify rows for variables based on the Sample Characteristics Dictionary
+# Trait (Chronic_kidney_disease) is constant (all diseased biopsies); treat as not available for association analysis
+trait_row = None
+
+# Age and Gender are available:
+age_row = 1     # contains multiple "age (yrs):" entries (also includes some gender entries which we'll ignore in conversion)
+gender_row = 0  # contains "gender: male/female"
+
+# Step 2.2: Conversion functions
+import re
+from typing import Optional
+
+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: str) -> Optional[int]:
+    """
+    Binary: Chronic kidney disease presence (1) vs absence (0).
+    In this dataset, samples are CKD kidney biopsies; if used, most fields imply CKD=1.
+    This function is defined for completeness but trait_row is None, so it won't be used.
+    """
+    if x is None:
+        return None
+    s = str(x).strip().lower()
+    val = _after_colon(s)
+
+    # Map common controls to 0
+    if any(k in s for k in ["control", "normal", "healthy"]):
+        return 0
+
+    # Diagnosis or clinical course imply CKD presence
+    if "diagnosis" in s:
+        return 1 if val and val not in ["", "unknown", "other/unknown"] else None
+    if "clinical course" in s:
+        if val in ["stable", "progressive"]:
+            return 1
+        return None
+    if "tissue" in s and "kidney biopsy" in s:
+        return 1  # kidney disease biopsy implies CKD case in this context
+
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    """
+    Continuous age in years. Extract numeric value when header mentions age.
+    """
+    if x is None:
+        return None
+    s = str(x).strip().lower()
+    if "age" not in s:
+        return None
+    val = _after_colon(s)
+    # Extract first numeric token
+    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: str) -> Optional[int]:
+    """
+    Binary: female -> 0, male -> 1.
+    """
+    if x is None:
+        return None
+    s = str(x).strip().lower()
+    if "gender" not in s:
+        return None
+    val = _after_colon(s)
+    if "female" in val:
+        return 0
+    if "male" in val:
+        return 1
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical Feature Extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = 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
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for probe IDs and gene symbols from the annotation
+id_col = 'ID' if 'ID' in gene_annotation.columns else ('SPOT_ID' if 'SPOT_ID' in gene_annotation.columns else None)
+if id_col is None:
+    raise ValueError("No suitable probe ID column found in gene annotation.")
+
+# Prefer canonical gene symbol column, with fallbacks
+for candidate in ['GENE_SYMBOL', 'GENE', 'GENE_NAME']:
+    if candidate in gene_annotation.columns:
+        gene_symbol_col = candidate
+        break
+else:
+    raise ValueError("No suitable gene symbol column found in gene annotation.")
+
+# Build mapping and convert probe-level data to gene-level expression
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+# Normalize and save gene expression data; trait is unavailable for this cohort.
+import os
+import json
+
+# 1. Normalize the obtained gene data and save to file
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. If trait is unavailable (trait_row was None), skip linking and final validation.
+# Avoid overwriting initial metadata recorded in Step 2.
+if 'trait_row' in globals() and trait_row is None:
+    print("Trait is unavailable (trait_row is None). Skipping linking, missing value handling, bias checks, and final validation.")
+    need_initial_record = True
+    if os.path.exists(json_path):
+        try:
+            with open(json_path, "r") as f:
+                records = json.load(f)
+            if cohort in records:
+                need_initial_record = False
+        except Exception:
+            need_initial_record = True
+    if need_initial_record:
+        _ = validate_and_save_cohort_info(
+            is_final=False,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=True,
+            is_trait_available=False
+        )

output/preprocess/Chronic_kidney_disease/code/GSE66494.py

@@ -0,0 +1,238 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE66494"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE66494"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE66494.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE66494.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE66494.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Microarray gene expression data per background info
+
+# Step 2: Define availability and converters
+# Trait: Use sample type as proxy (Renal biopsy specimens -> CKD, Normal kidney total RNA -> control)
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if s.lower() in {"na", "nan", ""}:
+        return None
+    if ":" in s:
+        return s.split(":", 1)[1].strip()
+    return s
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if "renal biopsy" in vl:
+        return 1
+    if "chronic kidney disease" in vl or "ckd" in vl:
+        return 1
+    if "normal kidney" in vl:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Extract first number as age
+    import re
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if m:
+        try:
+            val = float(m.group(1))
+            return val
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"male", "m"}:
+        return 1
+    if vl in {"female", "f"}:
+        return 0
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction, 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)
+    print(preview)
+
+    import os
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Affymetrix/Agilent-like probe IDs (e.g., A_23_P100001) are not human gene symbols
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping: probe IDs ('ID') to gene symbols ('GENE_SYMBOL')
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply the mapping to convert probe-level data to gene-level expression
+probe_data = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import json
+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
+# Use the clinical features from memory; if not present, load from saved file.
+try:
+    selected_clinical_df  # noqa: F401
+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
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and removal for demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Force-cast to native Python bools to avoid numpy/pandas scalar issues in JSON serialization
+is_gene_available_final = bool(normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+is_trait_available_final = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+is_trait_biased_bool = bool(is_trait_biased)
+
+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_bool,
+        df=unbiased_linked_data,
+        note="INFO: Trait derived from sample type (renal biopsy specimens vs normal kidney RNA)."
+    )
+except TypeError:
+    # Fallback: sanitize and write record manually if JSON serialization fails
+    df = unbiased_linked_data
+    ig = bool(is_gene_available_final)
+    it = bool(is_trait_available_final)
+    if len(df) <= 0 or len(df.columns) <= 4:
+        print(f"Abnormality detected in the cohort: {cohort}. Preprocessing failed.")
+        ig = False
+        if len(df) <= 0:
+            it = False
+    is_available = bool(ig and it)
+    usable = bool(is_available and (is_trait_biased_bool is False))
+
+    record = {
+        "is_usable": bool(usable),
+        "is_gene_available": bool(ig),
+        "is_trait_available": bool(it),
+        "is_available": bool(is_available),
+        "is_biased": (bool(is_trait_biased_bool) if is_available else None),
+        "has_age": ("Age" in df.columns if is_available else None),
+        "has_gender": ("Gender" in df.columns if is_available else None),
+        "sample_size": (int(len(df)) if is_available else None),
+        "note": "INFO: Trait derived from sample type (renal biopsy specimens vs normal kidney RNA)."
+    }
+
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+    if os.path.exists(json_path):
+        try:
+            with open(json_path, "r") as f:
+                records = json.load(f)
+        except Exception:
+            records = {}
+    else:
+        records = {}
+    records[cohort] = record
+    with open(json_path, "w") as f:
+        json.dump(records, f)
+    is_usable = usable
+
+# 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/Chronic_kidney_disease/code/GSE69438.py

@@ -0,0 +1,202 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+cohort = "GSE69438"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
+in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE69438"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/GSE69438.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/GSE69438.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/GSE69438.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability (from "Tissue Transcriptome..." title -> gene expression likely available)
+is_gene_available = True
+
+# 2) Variable availability from Sample Characteristics Dictionary
+# Given dictionary from previous step
+sample_char_dict = {0: ['tissue: Tubulointerstitium from kidney biopsy']}
+
+# No trait/age/gender keys present; only tissue information and it's constant
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    # Map CKD-related disease labels to 1; controls/healthy to 0; else None
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    s = v.lower()
+
+    # positive CKD indicators
+    positive_terms = [
+        "ckd", "chronic kidney disease", "end-stage renal disease", "esrd",
+        "kidney disease", "renal failure", "diabetic nephropathy",
+        "lupus nephritis", "focal segmental glomerulosclerosis", "fsgs",
+        "membranous glomerulonephritis", "iga nephropathy", "nephropathy"
+    ]
+    if any(t in s for t in positive_terms):
+        return 1
+
+    negative_terms = ["control", "healthy", "normal", "non-disease", "donor", "reference"]
+    if any(t in s for t in negative_terms):
+        return 0
+
+    # If explicitly states "no kidney disease"
+    if "no kidney disease" in s or "without kidney disease" in s:
+        return 0
+
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    s = v.lower()
+    if s in {"na", "n/a", "nan", "none", "unknown", "not available"}:
+        return None
+    # extract first number as age (years); handle formats like "45", "45 years", "45.0", "age 45"
+    m = re.search(r"(\d+(\.\d+)?)", s)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group(1))
+    except Exception:
+        return None
+    # Filter unreasonable ages
+    if 0 <= age_val <= 120:
+        return age_val
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    s = v.strip().lower()
+    # female -> 0, male -> 1
+    if s in {"female", "f", "woman", "women", "girl"}:
+        return 0
+    if s in {"male", "m", "man", "men", "boy"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction skipped because trait_row is None
+# (No action needed here)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping: 'ID' matches the expression identifiers, 'ORF' holds gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')
+
+# Apply the mapping to convert probe/ID-level data to gene symbol-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine availability flags
+is_gene_available_flag = normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0
+
+# 2-6. If clinical data exists, proceed to link; otherwise, perform initial filtering metadata update only.
+if 'selected_clinical_data' in globals() and isinstance(selected_clinical_data, pd.DataFrame):
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Judge and remove biased features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        True, cohort, json_path,
+        is_gene_available_flag, True,
+        is_trait_biased, unbiased_linked_data
+    )
+
+    # 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)
+
+else:
+    # Trait not available -> record initial filtering metadata only
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available_flag,
+        is_trait_available=False
+    )

output/preprocess/Chronic_kidney_disease/code/TCGA.py

@@ -0,0 +1,59 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_kidney_disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Chronic_kidney_disease/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Chronic_kidney_disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Chronic_kidney_disease/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Chronic_kidney_disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Inspect subdirectories under TCGA root
+all_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Define strict synonyms for Chronic Kidney Disease; TCGA cancer cohorts (e.g., KIRC/KIRP/KICH) are not CKD and should be skipped
+ckd_keywords_strict = [
+    'chronic_kidney_disease', 'ckd', 'chronic kidney disease',
+    'renal failure', 'chronic renal', 'nephropathy',
+    'kidney disease', 'end-stage renal', 'end stage renal', 'esrd'
+]
+
+def normalize_name(name: str) -> str:
+    return name.lower().replace('-', '_').replace(' ', '_').replace('(', '').replace(')', '')
+
+normalized_map = {d: normalize_name(d) for d in all_subdirs}
+strict_matches = [d for d, n in normalized_map.items() if any(k in n for k in ckd_keywords_strict)]
+
+if len(strict_matches) == 0:
+    # No suitable cohort for CKD in TCGA; skip this trait
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Choose the most specific match (longest name as a simple heuristic)
+    cohort_name = sorted(strict_matches, key=lambda x: len(x), reverse=True)[0]
+    cohort_dir = os.path.join(tcga_root_dir, cohort_name)
+
+    # Identify clinical and genetic files
+    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 further analysis
+    print(list(clinical_df.columns))

output/preprocess/Chronic_kidney_disease/cohort_info.json

@@ -1,92 +1 @@
-{
-  "GSE69438": {
-    "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
-  },
-  "GSE66494": {
-    "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
-  },
-  "GSE60861": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": false,
-    "sample_size": 29
-  },
-  "GSE45980": {
-    "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": 43
-  },
-  "GSE180393": {
-    "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
-  },
-  "GSE142153": {
-    "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": 40
-  },
-  "GSE104954": {
-    "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": 132
-  },
-  "GSE104948": {
-    "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": 122
-  },
-  "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": 323
-  }
-}
+{"GSE69438": {"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}, "GSE66494": {"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": 61, "note": "INFO: Trait derived from sample type (renal biopsy specimens vs normal kidney RNA)."}, "GSE60861": {"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}, "GSE45980": {"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": 43, "note": "INFO: Linked samples before/after missing-value filtering: 43 -> 43. INFO: Gene features before/after filtering: 8116 -> 8116."}, "GSE180394": {"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": 59, "note": "INFO: Gene symbol normalization skipped because indices are Entrez Gene IDs; proceeding with Entrez-level data."}, "GSE180393": {"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": 62, "note": "INFO: Gene indices are Entrez IDs; skipped symbol normalization."}, "GSE142153": {"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": 40, "note": "INFO: Only trait available; no age/gender fields in clinical annotations."}, "GSE104954": {"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": 132, "note": "INFO: Trait appears highly imbalanced (few controls vs many CKD cases); dataset likely flagged as biased."}, "GSE104948": {"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": 125, "note": "INFO: Diagnosis-based heuristic used for case/control: donors/tumor/normal/healthy treated as controls (0); all other diagnoses treated as CKD cases (1)."}, "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/Colon_and_Rectal_Cancer/code/GSE46517.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Colon_and_Rectal_Cancer"
+cohort = "GSE46517"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Colon_and_Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Colon_and_Rectal_Cancer/GSE46517"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/GSE46517.csv"
+out_gene_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46517.csv"
+out_clinical_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/clinical_data/GSE46517.csv"
+json_path = "./output/z2/preprocess/Colon_and_Rectal_Cancer/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 U133A mRNA microarray => yes)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# Trait is Colon_and_Rectal_Cancer, but this series is melanoma/nevi/normal skin;
+# no field indicates colon/rectal cancer status -> treat as unavailable.
+trait_row = None
+
+# Age and gender appear across multiple rows; choose one with many entries observed.
+age_row = 11
+gender_row = 11
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    s = str(value)
+    return s.split(":", 1)[1].strip() if ":" in s else s.strip()
+
+def convert_trait(x):
+    # Binary mapping for Colon_and_Rectal_Cancer (1 = CRC, 0 = not CRC)
+    # Not used since trait_row is None, but kept for completeness.
+    val = _after_colon(x).lower()
+    if not val:
+        return None
+    # Positive indicators for CRC
+    crc_pos = [
+        "colorectal", "colon adenocarcinoma", "rectal adenocarcinoma",
+        "colon cancer", "rectal cancer", "colorectal cancer", "crc"
+    ]
+    if any(k in val for k in crc_pos):
+        return 1
+    # If value explicitly indicates other diseases (e.g., melanoma), map to 0
+    non_crc_indicators = ["melanoma", "nevus", "normal skin", "melanocytes", "skin"]
+    if any(k in val for k in non_crc_indicators):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _after_colon(x).lower()
+    if not val:
+        return None
+    # Parse formats like "72y 4m", "41y", "85y 5 m"
+    y_match = re.search(r'(\d+)\s*y', val)
+    if not y_match:
+        return None
+    years = int(y_match.group(1))
+    m_match = re.search(r'(\d+)\s*m', val)
+    months = int(m_match.group(1)) if m_match else 0
+    return years + months / 12.0
+
+def convert_gender(x):
+    val = _after_colon(x).lower()
+    if not val:
+        return None
+    if val.startswith('male') or val == 'm':
+        return 1
+    if val.startswith('female') or val == 'f':
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row were available:
+# if trait_row is not None:
+#     selected_clinical = geo_select_clinical_features(
+#         clinical_df=clinical_data,
+#         trait=trait,
+#         trait_row=trait_row,
+#         convert_trait=convert_trait,
+#         age_row=age_row,
+#         convert_age=convert_age,
+#         gender_row=gender_row,
+#         convert_gender=convert_gender
+#     )
+#     _ = preview_df(selected_clinical)
+#     selected_clinical.to_csv(out_clinical_data_file)

output/preprocess/Colon_and_Rectal_Cancer/code/GSE46862.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Colon_and_Rectal_Cancer"
+cohort = "GSE46862"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Colon_and_Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Colon_and_Rectal_Cancer/GSE46862"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/GSE46862.csv"
+out_gene_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46862.csv"
+out_clinical_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/clinical_data/GSE46862.csv"
+json_path = "./output/z2/preprocess/Colon_and_Rectal_Cancer/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 GeneChip arrays -> mRNA expression
+
+# 2) Variable availability and converters
+# Trait (Colon_and_Rectal_Cancer) is constant across samples in this series (all rectal cancer patients) -> not available
+trait_row = None
+
+# Age and Gender are available
+age_row = 1
+gender_row = 2
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    parts = str(value).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(value).strip()
+
+def convert_trait(value):
+    # Not applicable for this dataset as everyone has rectal cancer; return None
+    return None
+
+def convert_age(value):
+    v = _after_colon(value).lower()
+    # Extract the first integer/float number found
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    v = _after_colon(value).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
+    )
+    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, 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
+# Identify columns for probe IDs and gene symbols from the annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'gene_assignment'
+
+# 2) Build mapping dataframe
+gene_mapping = 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=gene_mapping)
+
+# 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)
+
+# Since trait is unavailable (trait_row was None), skip linking and downstream steps.
+linked_data = None
+unbiased_linked_data = None
+
+# 5) Final validation and save cohort info
+note = ("INFO: Trait unavailable for this cohort (all rectal cancer; no usable trait variation for "
+        f"{trait}). Gene expression processed and saved; skipped clinical-genetic linking and downstream steps.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=normalized_gene_data.T,
+    note=note
+)
+
+# 6) Do not save linked data since dataset is not usable for the current trait
+# (Guard retained for completeness)
+if is_usable and unbiased_linked_data is not None:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Colon_and_Rectal_Cancer/code/GSE56699.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Colon_and_Rectal_Cancer"
+cohort = "GSE56699"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Colon_and_Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Colon_and_Rectal_Cancer/GSE56699"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/GSE56699.csv"
+out_gene_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE56699.csv"
+out_clinical_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/clinical_data/GSE56699.csv"
+json_path = "./output/z2/preprocess/Colon_and_Rectal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # mRNA microarray/RNAseq-based study on rectal cancer FFPE specimens
+
+# 2) Variable availability based on provided Sample Characteristics Dictionary
+trait_row = None   # No case/control or disease presence variability; all are rectal cancer cases
+age_row = None     # No age field present
+gender_row = None  # No gender/sex field present
+
+# 2.2) Conversion functions
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(x):
+    v = _extract_after_colon(x)
+    if v is None or v == "" or v.lower() in {"na", "n/a", "nan", "none", "unknown"}:
+        return None
+    vl = v.lower()
+    # Map clear cancer indications to 1; normal/healthy/control to 0
+    cancer_terms = ["cancer", "carcinoma", "adenocarcinoma", "tumor", "tumour", "crc", "rectal", "rectum", "colon"]
+    normal_terms = ["normal", "healthy", "control", "adjacent normal", "benign"]
+    if any(t in vl for t in cancer_terms):
+        return 1
+    if any(t in vl for t in normal_terms):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"na", "n/a", "nan", "none", "unknown"}:
+        return None
+    nums = re.findall(r"[-+]?\d*\.?\d+", vl)
+    if not nums:
+        return None
+    try:
+        return float(nums[0])
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"na", "n/a", "nan", "none", "unknown"}:
+        return None
+    # Female -> 0, Male -> 1
+    if vl in {"female", "f", "woman", "girl"}:
+        return 0
+    if vl in {"male", "m", "man", "boy"}:
+        return 1
+    # Sometimes encoded as 0/1
+    if vl == "0":
+        return 0
+    if vl == "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 is skipped because trait_row is None
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the probe ID and gene symbol columns from the annotation preview:
+# Probe identifiers match 'ID' (e.g., 'ILMN_1343291'), and gene symbols are in 'Symbol'
+
+# 2. Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Apply the mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Link clinical and genetic data only if clinical features (trait) are available
+has_clinical = ('selected_clinical_data' in globals() 
+                and isinstance(selected_clinical_data, pd.DataFrame) 
+                and (trait in selected_clinical_data.index))
+
+if not has_clinical:
+    # No clinical trait available: update metadata accordingly and do not proceed with linking/QC
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+else:
+    # Proceed with linking and downstream processing
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Bias check and 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 available; linked with normalized gene expression."
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    # 6) Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Colon_and_Rectal_Cancer/code/TCGA.py

@@ -0,0 +1,269 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Colon_and_Rectal_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Colon_and_Rectal_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Colon_and_Rectal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# 1) Select the most appropriate TCGA cohort directory for Colon and Rectal Cancer
+subdirs = os.listdir(tcga_root_dir)
+preferred_patterns = [
+    "TCGA_Colon_and_Rectal_Cancer_(COADREAD)",
+    "COADREAD",
+    "Colon_and_Rectal_Cancer",
+]
+selected_dir = None
+for pat in preferred_patterns:
+    candidates = [d for d in subdirs if pat.lower() in d.lower()]
+    if candidates:
+        # If multiple options exist, choose the most specific match (first by our preference order)
+        selected_dir = sorted(candidates, key=len)[0]
+        break
+
+if selected_dir is None:
+    # No suitable directory found -> mark as completed and skip
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA_Colon_and_Rectal_Cancer_NotFound",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # 2) Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3) Load both files as DataFrames
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # 4) Print the column names of the clinical data
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+# Use available clinical_df columns if present; otherwise fall back to the provided list
+provided_columns = ['AWG_MLH1_silencing', 'AWG_cancer_type_Oct62011', 'CDE_ID_3226963', 'CIMP', 'MSI_updated_Oct62011', '_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_COADREAD', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mutation_PANCAN', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'anatomic_neoplasm_subdivision', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'braf_gene_analysis_performed', 'braf_gene_analysis_result', 'circumferential_resection_margin', 'colon_polyps_present', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'disease_code', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'gender', 'height', 'histological_type', 'history_of_colon_polyps', 'history_of_neoadjuvant_treatment', 'hypermutation', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'kras_gene_analysis_performed', 'kras_mutation_codon', 'kras_mutation_found', 'longest_dimension', 'loss_expression_of_mismatch_repair_proteins_by_ihc', 'loss_expression_of_mismatch_repair_proteins_by_ihc_result', 'lost_follow_up', 'lymph_node_examined_count', 'lymphatic_invasion', 'microsatellite_instability', 'new_neoplasm_event_type', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'non_nodal_tumor_deposits', 'non_silent_mutation', 'non_silent_rate_per_Mb', 'number_of_abnormal_loci', 'number_of_first_degree_relatives_with_cancer_diagnosis', 'number_of_loci_tested', 'number_of_lymphnodes_positive_by_he', 'number_of_lymphnodes_positive_by_ihc', 'oct_embedded', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'perineural_invasion_present', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'preoperative_pretreatment_cea_level', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'project_code', 'radiation_therapy', 'residual_disease_post_new_tumor_event_margin_status', 'residual_tumor', 'sample_type', 'sample_type_id', 'shortest_dimension', 'silent_mutation', 'silent_rate_per_Mb', 'site_of_additional_surgery_new_tumor_event_mets', 'synchronous_colon_cancer_present', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'total_mutation', 'tumor_tissue_site', 'venous_invasion', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_COADREAD_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_COADREAD_PDMRNAseq', '_GENOMIC_ID_TCGA_COADREAD_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_COADREAD_hMethyl450', '_GENOMIC_ID_TCGA_COADREAD_gistic2thd', '_GENOMIC_ID_TCGA_COADREAD_hMethyl27', '_GENOMIC_ID_TCGA_COADREAD_G4502A_07_3', '_GENOMIC_ID_TCGA_COADREAD_PDMarrayCNV', '_GENOMIC_ID_TCGA_COADREAD_exp_HiSeqV2', '_GENOMIC_ID_TCGA_COADREAD_PDMarray', '_GENOMIC_ID_TCGA_COADREAD_gistic2', '_GENOMIC_ID_TCGA_COADREAD_mutation', '_GENOMIC_ID_TCGA_COADREAD_RPPA_RBN', '_GENOMIC_ID_TCGA_COADREAD_PDMRNAseqCNV']
+
+all_columns = list(clinical_df.columns) if 'clinical_df' in globals() else provided_columns
+
+# Identify candidate columns with careful patterns to avoid false positives like "pathologic_stage"
+candidate_age_cols = []
+candidate_gender_cols = []
+
+for col in all_columns:
+    col_l = col.lower()
+    # Age candidates
+    if (
+        col_l == 'age' or
+        col_l.startswith('age_') or
+        'age_at' in col_l or
+        col_l.endswith('_age') or
+        col_l in {'days_to_birth', 'years_to_birth', 'year_of_birth', 'dob'}
+    ):
+        candidate_age_cols.append(col)
+    # Gender candidates (avoid partial matches like "seq")
+    if (
+        col_l in {'gender', 'sex'} or
+        col_l.endswith('_gender') or
+        col_l.endswith('_sex') or
+        col_l.startswith('gender_') or
+        col_l.startswith('sex_')
+    ):
+        candidate_gender_cols.append(col)
+
+# Print required lists in strict format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Preview extracted data if clinical_df is available
+if 'clinical_df' in globals():
+    age_cols_present = [c for c in candidate_age_cols if c in clinical_df.columns]
+    gender_cols_present = [c for c in candidate_gender_cols if c in clinical_df.columns]
+
+    if age_cols_present:
+        print(preview_df(clinical_df[age_cols_present], n=5))
+    if gender_cols_present:
+        print(preview_df(clinical_df[gender_cols_present], n=5))
+
+# Step 3: Select Demographic Features
+import pandas as pd
+import numpy as np
+
+# Defaults
+age_col = None
+gender_col = None
+
+# Heuristics thresholds
+min_non_missing_ratio = 0.6
+
+# Helper to check if a variable exists
+def var_exists(name):
+    return name in globals() or name in locals()
+
+# Select age column
+if var_exists('candidate_age_cols'):
+    if 'clinical_df' in globals() or 'clinical_df' in locals():
+        df = clinical_df
+        best_score = -np.inf
+        best_col = None
+        for col in candidate_age_cols:
+            if col in df.columns:
+                s = pd.to_numeric(df[col], errors='coerce')
+                non_missing_ratio = s.notna().mean()
+
+                # Plausible human age range in years
+                plausible_ratio = ((s >= 0) & (s <= 120)).mean(skipna=True)
+
+                # Small bonus if column name suggests age in years
+                name_bonus = 0.1 if 'age' in col.lower() and 'birth' not in col.lower() else 0.0
+
+                score = plausible_ratio * 1.0 + non_missing_ratio * 0.2 + name_bonus
+
+                if non_missing_ratio >= min_non_missing_ratio and score > best_score:
+                    best_score = score
+                    best_col = col
+
+        age_col = best_col
+    else:
+        # Fallback to commonly correct choice if DataFrame not available
+        age_col = 'age_at_initial_pathologic_diagnosis' if 'age_at_initial_pathologic_diagnosis' in candidate_age_cols else None
+
+# Select gender column
+if var_exists('candidate_gender_cols'):
+    if 'clinical_df' in globals() or 'clinical_df' in locals():
+        df = clinical_df
+        best_score = -np.inf
+        best_col = None
+
+        allowed = {'male', 'female', 'm', 'f'}
+        for col in candidate_gender_cols:
+            if col in df.columns:
+                s = df[col].astype(str).str.strip().str.lower()
+                non_missing_ratio = df[col].notna().mean()
+                in_allowed = s.isin(allowed)
+                allowed_ratio = in_allowed.mean()
+
+                # Score prioritizes valid gender values and completeness
+                score = allowed_ratio * 1.0 + non_missing_ratio * 0.2
+
+                if non_missing_ratio >= min_non_missing_ratio and score > best_score:
+                    best_score = score
+                    best_col = col
+
+        gender_col = best_col
+    else:
+        gender_col = 'gender' if 'gender' in candidate_gender_cols else None
+
+# Print selected columns and a brief preview if available
+print("Selected age_col:", age_col)
+if age_col is not None and ('clinical_df' in globals() or 'clinical_df' in locals()):
+    print("age_col preview (first 5):", clinical_df[age_col].head(5).tolist())
+
+print("Selected gender_col:", gender_col)
+if gender_col is not None and ('clinical_df' in globals() or 'clinical_df' in locals()):
+    print("gender_col preview (first 5):", clinical_df[gender_col].head(5).tolist())
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+import numpy as np
+
+# 1) Extract and standardize clinical features (Trait, Age, Gender)
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Prepare genetic data with genes as index, samples as columns
+def _tcga_prop_tcga_prefix(labels):
+    if len(labels) == 0:
+        return 0.0
+    return np.mean([isinstance(x, str) and x.startswith('TCGA') for x in labels])
+
+# Detect orientation: are TCGA sample IDs in index or columns?
+p_idx = _tcga_prop_tcga_prefix(genetic_df.index.tolist())
+p_col = _tcga_prop_tcga_prefix(genetic_df.columns.tolist())
+
+if p_idx >= 0.5 and p_idx > p_col:
+    # Index are samples; transpose to get genes as index
+    gene_df_raw = genetic_df.T
+else:
+    gene_df_raw = genetic_df
+
+# Ensure numeric and drop all-nan rows/cols safely
+gene_df_raw = gene_df_raw.apply(pd.to_numeric, errors='coerce')
+gene_df_raw = gene_df_raw.dropna(axis=0, how='all').dropna(axis=1, how='all')
+
+# Normalize gene symbols using NCBI synonyms and aggregate duplicates
+normalized_gene_df = normalize_gene_symbols_in_index(gene_df_raw)
+
+# Save normalized gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on sample IDs
+# Harmonize sample identifiers to first 15 chars (e.g., TCGA-XX-XXXX-01)
+def _to_sample15(s):
+    return str(s)[:15] if isinstance(s, str) else s
+
+E = normalized_gene_df.T.copy()  # samples x genes
+E.index = E.index.map(_to_sample15)
+E = E[~E.index.duplicated(keep='first')]
+
+clinical_harmonized = selected_clinical_df.copy()
+clinical_harmonized.index = clinical_harmonized.index.map(_to_sample15)
+clinical_harmonized = clinical_harmonized[~clinical_harmonized.index.duplicated(keep='first')]
+
+linked_data = clinical_harmonized.join(E, how='inner')
+
+# 4) Handle missing values systematically
+processed_df = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine bias in trait and demographics; remove biased demographics
+is_biased, debiased_df = judge_and_remove_biased_features(processed_df, trait=trait)
+is_biased = bool(is_biased)  # ensure Python-native bool
+
+# 6) Final quality validation and save cohort metadata
+cohort_name = selected_dir if 'selected_dir' in globals() else "TCGA_Colon_and_Rectal_Cancer_(COADREAD)"
+is_gene_available = bool((normalized_gene_df.shape[0] > 0) and (normalized_gene_df.shape[1] > 0))
+is_trait_available = bool((trait in debiased_df.columns) and bool(debiased_df[trait].notna().any()))
+
+note = (
+    f"INFO: Linked clinical and gene expression data for {cohort_name}. "
+    f"Normalized genes: {normalized_gene_df.shape[0]}; samples in gene data: {normalized_gene_df.shape[1]}. "
+    f"Linked samples after harmonization: {linked_data.shape[0]}; final samples after QC: {debiased_df.shape[0]}; "
+    f"final features: {debiased_df.shape[1]}."
+)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=str(cohort_name),
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_biased,
+    df=debiased_df,
+    note=note
+)
+
+# 7) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    debiased_df.to_csv(out_data_file)

output/preprocess/Colon_and_Rectal_Cancer/cohort_info.json

@@ -1,42 +1 @@
-{
-  "GSE56699": {
-    "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
-  },
-  "GSE46862": {
-    "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": 69
-  },
-  "GSE46517": {
-    "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": 121
-  },
-  "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": 434
-  }
-}
+{"GSE56699": {"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}, "GSE46862": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait unavailable for this cohort (all rectal cancer; no usable trait variation for Colon_and_Rectal_Cancer). Gene expression processed and saved; skipped clinical-genetic linking and downstream steps."}, "GSE46517": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA_Colon_and_Rectal_Cancer_(COADREAD)": {"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": 434, "note": "INFO: Linked clinical and gene expression data for TCGA_Colon_and_Rectal_Cancer_(COADREAD). Normalized genes: 19848; samples in gene data: 434. Linked samples after harmonization: 434; final samples after QC: 434; final features: 19851."}}

output/preprocess/Congestive_heart_failure/clinical_data/GSE182600.csv

@@ -0,0 +1,4 @@
+,GSM5532093,GSM5532094,GSM5532095,GSM5532096,GSM5532097,GSM5532098,GSM5532099,GSM5532100,GSM5532101,GSM5532102,GSM5532103,GSM5532104,GSM5532105,GSM5532106,GSM5532107,GSM5532108,GSM5532109,GSM5532110,GSM5532111,GSM5532112,GSM5532113,GSM5532114,GSM5532115,GSM5532116,GSM5532117,GSM5532118,GSM5532119,GSM5532120,GSM5532121,GSM5532122,GSM5532123,GSM5532124,GSM5532125,GSM5532126,GSM5532127,GSM5532128,GSM5532129,GSM5532130,GSM5532131,GSM5532132,GSM5532133,GSM5532134,GSM5532135,GSM5532136,GSM5532137,GSM5532138,GSM5532139,GSM5532140,GSM5532141,GSM5532142,GSM5532143,GSM5532144,GSM5532145,GSM5532146,GSM5532147,GSM5532148,GSM5532149,GSM5532150,GSM5532151,GSM5532152,GSM5532153,GSM5532154,GSM5532155,GSM5532156,GSM5532157,GSM5532158,GSM5532159,GSM5532160,GSM5532161,GSM5532162,GSM5532163,GSM5532164,GSM5532165,GSM5532166,GSM5532167,GSM5532168,GSM5532169,GSM5532170
+Congestive_heart_failure,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0
+Age,33.4,51.2,51.9,47.8,41.5,67.3,52.8,16.1,78.9,53.2,70.9,59.9,21.9,45.2,52.4,32.3,52.8,55.8,47.0,55.8,57.3,31.7,49.3,66.1,55.9,49.1,63.0,21.0,53.6,50.1,37.4,71.5,56.5,33.4,51.2,51.9,47.8,41.5,67.3,52.8,78.9,53.2,70.9,59.9,21.9,45.2,52.4,32.3,55.8,47.0,55.8,57.3,31.7,49.3,66.1,55.9,49.1,63.0,21.0,53.6,50.1,37.4,71.5,56.5,33.4,51.2,51.9,47.8,52.8,53.2,21.9,55.8,47.0,49.3,66.1,53.6,50.1,56.5
+Gender,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0