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+p3/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv

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p3/preprocess/Hypothyroidism/TCGA.csv

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p3/preprocess/Hypothyroidism/gene_data/GSE75678.csv

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p3/preprocess/Hypothyroidism/gene_data/TCGA.csv

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p3/preprocess/Insomnia/code/GSE208668.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Insomnia"
+cohort = "GSE208668"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Insomnia"
+in_cohort_dir = "../DATA/GEO/Insomnia/GSE208668"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Insomnia/GSE208668.csv"
+out_gene_data_file = "./output/preprocess/3/Insomnia/gene_data/GSE208668.csv"
+out_clinical_data_file = "./output/preprocess/3/Insomnia/clinical_data/GSE208668.csv"
+json_path = "./output/preprocess/3/Insomnia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# From background info, it's genome-wide transcriptional profiling of PBMCs
+# Though raw data was lost, it's still gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion 
+# 2.1 Data Availability
+trait_row = 0  # 'insomnia' is in row 0
+age_row = 1    # 'age' is in row 1
+gender_row = 2 # 'gender' is in row 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(": ")[-1].strip()
+    if value == "yes":
+        return 1
+    elif value == "no":
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    if not isinstance(value, str):
+        return None
+    try:
+        age = float(value.split(": ")[-1].strip())
+        return age
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(": ")[-1].strip()
+    if value == "female":
+        return 0
+    elif value == "male":
+        return 1
+    return None
+
+# 3. Save Metadata - Initial Filtering
+is_trait_available = trait_row is not None
+initial_validation = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical = 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 the processed data
+    preview = preview_df(selected_clinical)
+    print("Preview of processed clinical data:")
+    print(preview)
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+requires_gene_mapping = False  # The gene identifiers are already in human gene symbol format. No mapping needed.
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# Get clinical features
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row, 
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge whether features are biased and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains genome-wide transcriptional profiling of PBMCs from older adults with and without insomnia disorder."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Insomnia/code/TCGA.py

@@ -0,0 +1,32 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Insomnia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Insomnia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Insomnia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Insomnia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Insomnia/cohort_info.json"
+
+# Get subdirectories from TCGA root directory
+tcga_subdirs = os.listdir(tcga_root_dir)
+tcga_subdirs = [d for d in tcga_subdirs if not d.startswith('.')]
+
+# Review available subdirectories for insomnia-related cohorts
+# No suitable cohort found - all are cancer specific and not related to sleep disorders
+print(f"No suitable TCGA cohort found for {trait}.")
+print("Available cohorts are cancer-specific and do not contain relevant data for sleep disorders.")
+
+# Record that this trait should be skipped due to lack of suitable data
+is_gene_available = False
+is_trait_available = False
+validate_and_save_cohort_info(is_final=False, 
+                            cohort="TCGA",
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)

p3/preprocess/Intellectual_Disability/GSE192767.csv

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p3/preprocess/Intellectual_Disability/GSE273850.csv

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p3/preprocess/Intellectual_Disability/GSE63870.csv

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p3/preprocess/Intellectual_Disability/GSE98697.csv

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p3/preprocess/Intellectual_Disability/clinical_data/GSE100680.csv

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p3/preprocess/Intellectual_Disability/clinical_data/GSE158385.csv

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p3/preprocess/Intellectual_Disability/clinical_data/GSE192767.csv

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p3/preprocess/Intellectual_Disability/clinical_data/GSE200864.csv

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p3/preprocess/Intellectual_Disability/clinical_data/GSE273850.csv

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p3/preprocess/Intellectual_Disability/clinical_data/GSE59630.csv

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+,GSM1440797,GSM1440798,GSM1440799,GSM1440800,GSM1440801,GSM1440802,GSM1440803,GSM1440804,GSM1440805,GSM1440806,GSM1440807,GSM1440808,GSM1440809,GSM1440810,GSM1440811,GSM1440812,GSM1440813,GSM1440814,GSM1440815,GSM1440816,GSM1440817,GSM1440818,GSM1440819,GSM1440820,GSM1440821,GSM1440822,GSM1440823,GSM1440824,GSM1440825,GSM1440826,GSM1440827,GSM1440828,GSM1440829,GSM1440830,GSM1440831,GSM1440832,GSM1440833,GSM1440834,GSM1440835,GSM1440836,GSM1440837,GSM1440838,GSM1440839,GSM1440840,GSM1440841,GSM1440842,GSM1440843,GSM1440844,GSM1440845,GSM1440846,GSM1440847,GSM1440848,GSM1440849,GSM1440850,GSM1440851,GSM1440852,GSM1440853,GSM1440854,GSM1440855,GSM1440856,GSM1440857,GSM1440858,GSM1440859,GSM1440860,GSM1440861,GSM1440862,GSM1440863,GSM1440864,GSM1440865,GSM1440866,GSM1440867,GSM1440868,GSM1440869,GSM1440870,GSM1440871,GSM1440872,GSM1440873,GSM1440874,GSM1440875,GSM1440876,GSM1440877,GSM1440878,GSM1440879,GSM1440880,GSM1440881,GSM1440882,GSM1440883,GSM1440884,GSM1440885,GSM1440886,GSM1440887,GSM1440888,GSM1440889,GSM1440890,GSM1440891,GSM1440892,GSM1440893,GSM1440894,GSM1440895,GSM1440896,GSM1440897,GSM1440898,GSM1440899,GSM1440900,GSM1440901,GSM1440902,GSM1440903,GSM1440904,GSM1440905,GSM1440906,GSM1440907,GSM1440908,GSM1440909,GSM1440910,GSM1440911,GSM1440912
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+Gender,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0

p3/preprocess/Intellectual_Disability/clinical_data/GSE63870.csv

@@ -0,0 +1,3 @@
+,GSM1558696,GSM1558697,GSM1558698,GSM1558699,GSM1558700,GSM1558701,GSM1558702,GSM1558703,GSM1558704,GSM1558705,GSM1558706,GSM1558707,GSM1558708,GSM1558709,GSM1558710,GSM1558711,GSM1558712,GSM1558713,GSM1558714,GSM1558715,GSM1558716,GSM1558717,GSM1558718,GSM1558719,GSM1558720,GSM1558721,GSM1558722,GSM1558723,GSM1558724,GSM1558725,GSM1558726,GSM1558727,GSM1558728,GSM1558729,GSM1558730,GSM1558731,GSM1558732,GSM1558733,GSM1558734,GSM1558735,GSM1558736,GSM1558737,GSM1558738,GSM1558739,GSM1558740,GSM1558741,GSM1558742,GSM1558743
+Intellectual_Disability,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0
+Age,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Intellectual_Disability/clinical_data/GSE89594.csv

@@ -0,0 +1,4 @@
+,GSM2384988,GSM2384989,GSM2384990,GSM2384991,GSM2384992,GSM2384993,GSM2384994,GSM2384995,GSM2384996,GSM2384997,GSM2384998,GSM2384999,GSM2385000,GSM2385001,GSM2385002,GSM2385003,GSM2385004,GSM2385005,GSM2385006,GSM2385007,GSM2385008,GSM2385009,GSM2385010,GSM2385011,GSM2385012,GSM2385013,GSM2385014,GSM2385015,GSM2385016,GSM2385017,GSM2385018,GSM2385019,GSM2385020,GSM2385021,GSM2385022,GSM2385023,GSM2385024,GSM2385025,GSM2385026,GSM2385027,GSM2385028,GSM2385029,GSM2385030,GSM2385031,GSM2385032,GSM2385033,GSM2385034,GSM2385035,GSM2385036,GSM2385037,GSM2385038,GSM2385039,GSM2385040,GSM2385041,GSM2385042,GSM2385043,GSM2385044,GSM2385045,GSM2385046,GSM2385047,GSM2385048,GSM2385049,GSM2385050,GSM2385051,GSM2385052,GSM2385053,GSM2385054,GSM2385055,GSM2385056,GSM2385057,GSM2385058,GSM2385059,GSM2385060,GSM2385061,GSM2385062,GSM2385063,GSM2385064,GSM2385065,GSM2385066,GSM2385067,GSM2385068,GSM2385069,GSM2385070,GSM2385071,GSM2385072,GSM2385073,GSM2385074,GSM2385075,GSM2385076,GSM2385077,GSM2385078,GSM2385079,GSM2385080,GSM2385081
+Intellectual_Disability,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,22.0,23.0,24.0,24.0,33.0,22.0,24.0,21.0,24.0,20.0,28.0,21.0,21.0,22.0,25.0,23.0,20.0,21.0,20.0,32.0,36.0,24.0,21.0,30.0,28.0,22.0,24.0,21.0,22.0,20.0,27.0,22.0,23.0,20.0,31.0,27.0,32.0,20.0,36.0,22.0,28.0,25.0,35.0,22.0,22.0,10.0,16.0,10.0,33.0,21.0,11.0,10.0,35.0,12.0,38.0,24.0,34.0,32.0,21.0,29.0,20.0,19.0,24.0,13.0,23.0,15.0,43.0,10.0,13.0,16.0,27.0,24.0,11.0,24.0,32.0,24.0,27.0,16.0,14.0,11.0,24.0,28.0,17.0,15.0,34.0,39.0,12.0,15.0,21.0,29.0,23.0,26.0,19.0,21.0
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p3/preprocess/Intellectual_Disability/clinical_data/GSE98697.csv

@@ -0,0 +1,2 @@
+,GSM2610219,GSM2610220,GSM2610221,GSM2610222,GSM2610223,GSM2610224,GSM2610225,GSM2610226,GSM2610227,GSM2610228,GSM2610229,GSM2610230,GSM2610231,GSM2610232,GSM2610233,GSM2610234,GSM2610235,GSM2610236,GSM2610237,GSM2610238,GSM2610239,GSM2610240,GSM2610241,GSM2610242,GSM2610243,GSM2610244,GSM2610245,GSM2610246,GSM2610247,GSM2610248,GSM2610249,GSM2610250,GSM2610251,GSM2610252,GSM2610253,GSM2610254,GSM2610255,GSM2610256,GSM2610257,GSM2610258,GSM2610259,GSM2610260,GSM2610261,GSM2610262,GSM2610263,GSM2610264,GSM2610265,GSM2610266
+Intellectual_Disability,1.0,1.0,1.0,1.0,1.0,1.0,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Intellectual_Disability/code/GSE100680.py

@@ -0,0 +1,183 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE100680"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE100680"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE100680.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE100680.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE100680.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes, this dataset appears to contain gene expression data based on the background information
+# which mentions measuring APP expression levels and genome-wide effects
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# Trait (DS vs Control) can be inferred from field 3 (description)
+trait_row = 3
+# Age is in field 2
+age_row = 2  
+# Gender is not available
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert description to binary indicating if sample is DS (1) or control (0)"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1]
+    if 'DS Clone' in value:
+        return 1 
+    elif 'Euploid Clone' in value:
+        return 0
+    return None
+
+def convert_age(value):
+    """Convert age string to numeric days"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1]
+    if 'Day' in value:
+        try:
+            return float(value.replace('Day ', ''))
+        except:
+            return None
+    return None
+
+def convert_gender(value):
+    """Not used since gender data is not available"""
+    return None
+
+# 3. Save Initial Metadata 
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract Clinical Features
+clinical_features = 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 the extracted features
+preview_df(clinical_features)
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These identifiers are ILMN (Illumina) probe IDs, not gene symbols
+# The ILMN_ prefix indicates they are from an Illumina microarray platform
+# They need to be mapped to official gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Drop rows where Symbol is null or contains phage/virus/bacteria
+gene_metadata = gene_metadata[gene_metadata['Symbol'].notna()]
+gene_metadata = gene_metadata[~gene_metadata['Symbol'].str.contains('phage|virus|bacteria', 
+                                                                   case=False, na=False)]
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# Get gene mapping data from annotation
+# 'ID' column matches the ILMN probe IDs in expression data
+# 'Symbol' column contains the gene symbols
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE158385.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE158385"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE158385"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE158385.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE158385.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE158385.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Yes, this appears to be gene expression data based on the background which studies effects in human amniocytes
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Key identification
+trait_row = 2  # Karyotype indicates T21 (Trisomy 21) status which represents Intellectual Disability
+age_row = None  # No age data available 
+gender_row = None  # Although gender info is embedded in karyotype, we can't reliably extract it since some patients could have multiple records
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].strip()
+    if '47' in value and 'T21' in value:  # Trisomy 21 cases
+        return 1
+    elif '46' in value and '2N' in value:  # Normal karyotype
+        return 0
+    return None
+
+convert_age = None  # No age data
+convert_gender = None  # No reliable gender data
+
+# 3. 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
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(clinical_data,
+                                                   trait=trait,
+                                                   trait_row=trait_row,
+                                                   convert_trait=convert_trait)
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# The identifiers in the gene expression data appear to be Affymetrix transcript cluster IDs (TC.....hg.1)
+# These are probe set IDs that need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Identify all platform sections in the SOFT file
+with gzip.open(soft_file_path, 'rt') as f:
+    platform_sections = []
+    current_platform = None
+    for line in f:
+        if line.startswith('^PLATFORM'):
+            if current_platform:
+                platform_sections.append(current_platform)
+            current_platform = {'id': line.strip()}
+        elif current_platform is not None and line.startswith('!Platform_title'):
+            current_platform['title'] = line.strip()
+            if 'human' in line.lower() or 'homo sapiens' in line.lower():
+                current_platform['is_human'] = True
+        elif not line.startswith('^'):  # End of platform section
+            if current_platform:
+                platform_sections.append(current_platform)
+                current_platform = None
+    if current_platform:  # Handle last platform if exists
+        platform_sections.append(current_platform)
+
+print("Found Platform Sections:")
+for platform in platform_sections:
+    print(platform)
+
+# Look for human gene annotations
+with gzip.open(soft_file_path, 'rt') as f:
+    human_data = []
+    is_human_section = False
+    for line in f:
+        if line.startswith('^PLATFORM'):
+            is_human_section = False
+            platform_id = line.strip()
+        elif line.startswith('!Platform_title') and ('human' in line.lower() or 'homo sapiens' in line.lower()):
+            is_human_section = True
+            print(f"\nFound human platform section: {platform_id}")
+            print(f"Platform title: {line.strip()}")
+        elif is_human_section and not line.startswith(('!', '#', '^')):
+            human_data.append(line)
+
+if human_data:
+    # Convert human annotation data to dataframe
+    human_annotation_df = pd.read_csv(io.StringIO(''.join(human_data)), sep='\t')
+    
+    print("\nColumn names:")
+    print(human_annotation_df.columns.tolist())
+    
+    print("\nData shape:", human_annotation_df.shape)
+    
+    print("\nPreview of the annotation data:")
+    print(json.dumps(preview_df(human_annotation_df), indent=2))
+else:
+    print("\nNo human gene annotation data found in the SOFT file.")
+# Extract probe and gene mapping from annotation data
+prob_col = 'ID'  # The gene expression data uses TC.....hg.1 identifiers, which match the ID column
+gene_col = 'gene_assignment'  # This column contains gene symbol information
+
+# Get initial mapping between probes and genes
+mapping_df = get_gene_mapping(human_annotation_df, prob_col, gene_col)
+
+# Convert probe-level expression data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Normalize gene symbols to ensure consistency
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Preview the result
+print("\nGene expression data shape:", gene_data.shape)
+print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE192767.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE192767"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE192767"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE192767.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE192767.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE192767.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - this dataset uses Affymetrix microarrays for gene expression profiling
+is_gene_available = True
+
+# 2.1 Data Availability
+
+# Trait (ID) data is available in row 0 (phenotype field)
+trait_row = 0
+
+# Age data not available
+age_row = None  
+
+# Gender data not available 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+
+def convert_trait(value: str) -> int:
+    """Convert ATR-X syndrome phenotype to binary values"""
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split("phenotype:")[-1].strip()
+    if "atr-x syndrome" in value:
+        return 1
+    elif "unaffected" in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float - not used since age not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary - not used since gender not available"""
+    return None
+
+# 3. Save metadata with 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=(trait_row is not None))
+
+# 4. Extract clinical features
+if trait_row is not None:
+    clinical_features = 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 the extracted features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These are Affymetrix probe IDs from HuGene arrays that need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# 1. Identify the columns for mapping
+# 'ID' in gene_metadata matches the probe IDs in genetic_data
+# 'Gene Symbol' contains the gene symbols we want to map to
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# 2. Apply gene mapping
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the first few rows of the mapped data
+print("\nPreview of mapped gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE200864.py

@@ -0,0 +1,130 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE200864"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE200864"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE200864.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE200864.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE200864.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on Series_title and Series_overall_design mentioning Affymetrix platform and gene expression
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability 
+# Trait: Down Syndrome mentioned in title and summary, everyone has Down Syndrome based on background info
+trait_row = None  # Everyone has intellectual disability (Down Syndrome), so constant feature
+
+# Age and gender: Not available in characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    return 1  # Would return 1 for all samples since all have Down Syndrome
+
+def convert_age(x):
+    return None  # Not used since age data not available
+
+def convert_gender(x):
+    return None  # Not used since gender data not available
+
+# 3. Save Metadata
+is_trait_available = True  # Although trait_row is None, we know everyone has intellectual disability
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None (constant feature)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These indices appear to be probe set IDs from Affymetrix microarray platform
+# They are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# Create gene mapping from probe IDs to gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Since trait is constant (all Down Syndrome), create a clinical features dataframe of all 1's
+clinical_features = pd.DataFrame(1, index=gene_data.columns, columns=['trait'])
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, 'trait')
+
+# Early exit if trait values are all NaN
+if linked_data['trait'].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'trait')
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric patients with Down Syndrome. Since all samples have Down Syndrome, the trait is constant (all 1's)."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE273850.py

@@ -0,0 +1,177 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE273850"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE273850"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE273850.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE273850.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE273850.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Affymetrix array data indicates gene expression data is available
+is_gene_available = True
+
+# 2. Variable and Data Type Analysis
+
+# 2.1 Row identifiers
+trait_row = 0  # Genotype info in row 0 indicates T21 vs control 
+gender_row = 1  # Sex info in row 1
+age_row = None  # Age not available
+
+# 2.2 Conversion functions
+def convert_trait(value: str) -> int:
+    """Convert T21 status to binary: 1 for T21, 0 for control"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 't21' in value:
+        return 1
+    elif 'euploid' in value:
+        return 0
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary: 0 for female, 1 for male"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Placeholder function since age is not available"""
+    return None
+
+# 3. Save metadata
+_ = validate_and_save_cohort_info(is_final=False, 
+                                 cohort=cohort,
+                                 info_path=json_path,
+                                 is_gene_available=is_gene_available, 
+                                 is_trait_available=trait_row is not None)
+
+# 4. Clinical feature extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Clinical features preview:", preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# The transcript IDs are in the format "TCxxxxxxxx.hg.1" which are not standard human gene symbols
+# They appear to be transcript cluster identifiers that need mapping to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# 1. Identify columns for gene mapping
+# Based on observation:
+# - 'ID' column in gene annotation contains probe IDs like 'TC0100006437.hg.1'
+# - Gene symbols are contained in 'SPOT_ID.1' column within RefSeq/ENSEMBL descriptions
+
+# 2. Create gene mapping dataframe from the annotation data
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='SPOT_ID.1')
+
+# 3. Apply gene mapping to convert probe-level data to gene expression data 
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the gene data
+print("\nShape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE285666.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE285666"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE285666"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE285666.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE285666.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE285666.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene expression availability - Yes, it uses Affymetrix exon arrays
+is_gene_available = True
+
+# 2.1 Get row numbers for clinical features
+trait_row = 0  # disease state row
+age_row = None  # age not available 
+gender_row = None  # gender not available
+
+# 2.2 Define conversion functions
+def convert_trait(value: str) -> int:
+    """Convert disease state to binary: 1 for Williams syndrome, 0 for control"""
+    if pd.isna(value) or value is None:
+        return None
+    if ':' in value:
+        value = value.split(':')[1].strip().lower()
+    if 'williams syndrome' in value:
+        return 1
+    elif 'unaffected' in value or 'control' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Placeholder function since age is not available"""
+    return None
+    
+def convert_gender(value: str) -> int:
+    """Placeholder function since gender is not available"""
+    return None
+
+# 3. Save metadata about dataset usability
+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. Extract clinical features since trait data is available
+clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
+                                               trait=trait,
+                                               trait_row=trait_row,
+                                               convert_trait=convert_trait)
+
+# Preview results
+preview_result = preview_df(clinical_features)
+print("Preview of extracted clinical features:")
+print(preview_result)
+
+# Save clinical data
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These appear to be probe IDs from a microarray platform, not standard human gene symbols
+# Examining the numeric format and length pattern confirms they need mapping
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Display non-NaN value counts for key gene identifier columns
+print("\nNumber of non-NaN values in key columns:")
+for col in ['ID', 'gene_assignment']:
+    print(f"{col}: {gene_metadata[col].notna().sum()}")
+
+# Preview rows with actual gene information
+print("\nPreview of rows with gene information:")
+gene_rows = gene_metadata[gene_metadata['gene_assignment'].notna()].head()
+print(json.dumps(preview_df(gene_rows), indent=2))
+# Get gene mapping dataframe
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
+mapping_data = mapping_data[mapping_data['Gene'] != '---']
+
+# Extract gene symbols from gene_assignment strings
+def extract_gene_symbol(text):
+    if pd.isna(text):
+        return None
+    parts = text.split('//')
+    if len(parts) >= 2:
+        return parts[1].strip()
+    return None
+
+mapping_data['Gene'] = mapping_data['Gene'].apply(extract_gene_symbol)
+mapping_data = mapping_data.dropna()
+
+# Apply gene mapping to convert probe data to gene data 
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Print info about the converted data
+print("Shape of probe-level data:", genetic_data.shape)
+print("Shape of gene-level data:", gene_data.shape)
+print("\nFirst few genes:")
+print(gene_data.index[:10].tolist())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE59630.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE59630"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE59630"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE59630.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE59630.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE59630.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# From the background info, we can see this is a gene expression study analyzing transcriptome, so:
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# From sample characteristics, we can find:
+trait_row = 2  # 'disease status' indicates DS vs Control
+age_row = 4    # Age data available
+gender_row = 3 # Sex data available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert disease status to binary (0: Control, 1: DS)"""
+    if x is None:
+        return None
+    value = x.split(': ')[-1].strip()
+    if value == 'CTL':
+        return 0
+    elif value == 'DS':
+        return 1
+    return None
+
+def convert_age(x):
+    """Convert age to continuous numeric value in years"""
+    if x is None:
+        return None
+    value = x.split(': ')[-1].strip().lower()
+    
+    # Extract number and unit
+    try:
+        num = float(''.join(filter(str.isdigit, value)))
+        if 'wg' in value:  # weeks of gestation
+            return num/52  # convert to years
+        elif 'mo' in value:  # months
+            return num/12  # convert to years
+        elif 'yr' in value:  # years
+            return num
+        return None
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender to binary (0: Female, 1: Male)"""
+    if x is None:
+        return None
+    value = x.split(': ')[-1].strip()
+    if value == 'F':
+        return 0
+    elif value == 'M':
+        return 1
+    return None
+
+# 3. Save Metadata 
+validate_and_save_cohort_info(is_final=False, 
+                             cohort=cohort,
+                             info_path=json_path,
+                             is_gene_available=is_gene_available,
+                             is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(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 the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These appear to be probe IDs from a microarray platform rather than gene symbols
+# They are numeric IDs which need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Display non-NaN value counts for key gene identifier columns
+print("\nNumber of non-NaN values in key columns:")
+for col in ['ID', 'gene_assignment']:
+    print(f"{col}: {gene_metadata[col].notna().sum()}")
+
+# Preview rows with actual gene information
+print("\nPreview of rows with gene information:")
+gene_rows = gene_metadata[gene_metadata['gene_assignment'].notna()].head()
+print(json.dumps(preview_df(gene_rows), indent=2))
+# From the previous output, we can see:
+# - Gene identifiers are in the 'ID' column
+# - Gene symbols are in 'gene_assignment' column and need to be extracted
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
+
+# Apply the mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Print information about the mapping result
+print("\nOriginal probes:", len(genetic_data))
+print("Mapped genes:", len(gene_data))
+print("\nPreview of first few genes and their expression values:")
+print(json.dumps(preview_df(gene_data), indent=2))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE63870.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE63870"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE63870"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE63870.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE63870.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE63870.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - it's analyzing whole genome expression in white blood cells
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 1  # Found in "condition" field
+age_row = 0    # Found in "age" field
+gender_row = None  # Gender data not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert cognitive disability status to binary"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    # 1 for having severe cognitive disability/dementia, 0 for without
+    if 'without' in value:
+        return 0
+    elif 'severe cognitive disability' in value or 'early dementia' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> int:
+    """Convert age group to binary"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    # Convert to binary: 0 for young, 1 for old
+    if value == 'young':
+        return 0
+    elif value == 'old':
+        return 1
+    return None
+
+def convert_gender(value: str) -> int:
+    """Placeholder function - not used since gender data unavailable"""
+    return None
+
+# 3. 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
+if trait_row is not None:
+    clinical_features = 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 the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of extracted clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These identifiers are not standard human gene symbols. They appear to be Agilent microarray probe IDs.
+# Probe IDs like 'A_19_P00315452' need to be mapped to gene symbols for analysis.
+
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Display non-NaN value counts for key gene identifier columns
+print("\nNumber of non-NaN values in key columns:")
+for col in ['ID', 'GENE_SYMBOL']:
+    print(f"{col}: {gene_metadata[col].notna().sum()}")
+
+# Preview rows with actual gene information
+print("\nPreview of rows with gene information:")
+gene_rows = gene_metadata[gene_metadata['GENE_SYMBOL'].notna()].head()
+print(json.dumps(preview_df(gene_rows), indent=2))
+# 1. Identify mapping columns
+# 'ID' in gene metadata matches the identifiers in genetic_data
+# 'GENE_SYMBOL' contains the target gene symbols
+gene_id_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# 2. Get mapping dataframe
+gene_mapping = get_gene_mapping(gene_metadata, gene_id_col, gene_symbol_col)
+
+# 3. Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, gene_mapping)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE89594.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE89594"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE89594"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE89594.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE89594.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE89594.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info, this is a gene expression study using peripheral blood
+is_gene_available = True
+
+# 2.1 Data Availability
+trait_row = 0  # "diagnosis" in row 0 contains trait info
+age_row = 2    # "age" in row 2
+gender_row = 3  # "gender" in row 3
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert trait status to binary"""
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    # Williams Syndrome is intellectual disability
+    if 'williams syndrome' in value or 'ws' in value:
+        return 1
+    elif 'control' in value:
+        return 0
+    # ASD samples counted as None since not relevant
+    return None
+
+def convert_age(x):
+    """Convert age to continuous values"""
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    try:
+        # Extract numeric value before 'y'
+        return float(value.replace('y',''))
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender to binary (0=female, 1=male)"""
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 3. 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
+if trait_row is not None:
+    clinical_features = 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 the processed clinical data
+    preview = preview_df(clinical_features)
+    print("Preview of processed clinical data:")
+    print(preview)
+    
+    # Save clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# Based on the provided sample row IDs which are just sequential numbers, 
+# we need to map these identifiers to proper gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Display non-NaN value counts for key gene identifier columns
+print("\nNumber of non-NaN values in key columns:")
+for col in ['ID', 'GENE_SYMBOL']:
+    print(f"{col}: {gene_metadata[col].notna().sum()}")
+
+# Preview rows with actual gene information
+print("\nPreview of rows with gene information:")
+gene_rows = gene_metadata[gene_metadata['GENE_SYMBOL'].notna()].head()
+print(json.dumps(preview_df(gene_rows), indent=2))
+# 1. Identify mapping columns
+# ID in expression data corresponds to ID in annotation
+# GENE_SYMBOL contains gene symbols for mapping
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 2. Apply mapping and aggregate to get gene expression data 
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the processed data
+print("\nPreview of mapped gene expression data:")
+print(f"Shape: {gene_data.shape}")
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10].tolist())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/GSE98697.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+cohort = "GSE98697"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Intellectual_Disability"
+in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE98697"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE98697.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE98697.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE98697.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - the dataset contains both coding and non-coding gene expression data according to title and design
+is_gene_available = True
+
+# 2.1 Data Row Numbers
+# Trait: Not directly given but subtype shows Down syndrome cases, can infer from aml subtype
+trait_row = 2
+# Age not available
+age_row = None  
+# Gender not available
+gender_row = None
+
+# 2.2 Type Conversion Functions
+def convert_trait(x):
+    # Extract value after colon
+    if ':' in x:
+        x = x.split(':', 1)[1].strip()
+    # Convert to binary - 1 for Down syndrome AMKL, 0 for other types
+    if 'Down-syndrome' in x:
+        return 1
+    elif 'aml' in x.lower():  # Other AML types
+        return 0
+    return None
+
+def convert_age(x):
+    return None  # Not used as age is not available
+
+def convert_gender(x):
+    return None  # Not used as gender is not available
+
+# 3. 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
+if trait_row is not None:
+    clinical_features = 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 the extracted features
+    preview_df(clinical_features)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# Observe that the identifiers are just '1', '2', '3' etc
+# These are numeric indices and not standard gene symbols
+# Therefore we need to map these IDs to proper gene symbols
+
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics 
+print("\nData shape:", gene_metadata.shape)
+
+# Display non-NaN value counts for key gene identifier columns
+print("\nNumber of non-NaN values in key columns:")
+for col in ['ID', 'FINAL_SYMBOL']:
+    print(f"{col}: {gene_metadata[col].notna().sum()}")
+
+# Preview rows with actual gene information
+print("\nPreview of rows with gene information:")
+gene_rows = gene_metadata[gene_metadata['FINAL_SYMBOL'].notna()].head()
+print(json.dumps(preview_df(gene_rows), indent=2))
+# Extract the gene mapping data
+# From observing the data, we need to map numeric 'ID' to 'FINAL_SYMBOL'
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='FINAL_SYMBOL')
+
+# Apply the gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Display the shape of the gene expression data before and after mapping
+print(f"Shape before mapping (probes × samples): {genetic_data.shape}")
+print(f"Shape after mapping (genes × samples): {gene_data.shape}")
+
+# Preview the first few gene symbols
+print("\nFirst few gene symbols:")
+print(gene_data.index[:5].tolist())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
+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_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Intellectual_Disability/code/TCGA.py

@@ -0,0 +1,32 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Intellectual_Disability"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Intellectual_Disability/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"
+
+# Get subdirectories from TCGA root directory
+tcga_subdirs = os.listdir(tcga_root_dir)
+tcga_subdirs = [d for d in tcga_subdirs if not d.startswith('.')]
+
+# Review available subdirectories for insomnia-related cohorts
+# No suitable cohort found - all are cancer specific and not related to sleep disorders
+print(f"No suitable TCGA cohort found for {trait}.")
+print("Available cohorts are cancer-specific and do not contain relevant data for sleep disorders.")
+
+# Record that this trait should be skipped due to lack of suitable data
+is_gene_available = False
+is_trait_available = False
+validate_and_save_cohort_info(is_final=False, 
+                            cohort="TCGA",
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)

p3/preprocess/Intellectual_Disability/cohort_info.json

@@ -0,0 +1 @@
+{"GSE98697": {"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": 44, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "GSE89594": {"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": 62, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "GSE63870": {"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": 48, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "GSE59630": {"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": 116, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "GSE285666": {"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": 52, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "GSE273850": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 51, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "GSE192767": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 48, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "GSE158385": {"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": 28, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "GSE100680": {"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": 34, "note": "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."}, "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}}

p3/preprocess/Intellectual_Disability/gene_data/GSE192767.csv

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p3/preprocess/Intellectual_Disability/gene_data/GSE200864.csv

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p3/preprocess/Intellectual_Disability/gene_data/GSE273850.csv

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p3/preprocess/Intellectual_Disability/gene_data/GSE59630.csv

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p3/preprocess/Intellectual_Disability/gene_data/GSE63870.csv

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p3/preprocess/Intellectual_Disability/gene_data/GSE89594.csv

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p3/preprocess/Intellectual_Disability/gene_data/GSE98697.csv

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p3/preprocess/Irritable_bowel_syndrome_(IBS)/GSE36701.csv

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