Add files using upload-large-folder tool

.gitattributes

@@ -1881,3 +1881,4 @@ p3/preprocess/Kidney_Papillary_Cell_Carcinoma/GSE42977.csv filter=lfs diff=lfs m
 p3/preprocess/lower_grade_glioma_and_glioblastoma/gene_data/GSE35158.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/lower_grade_glioma_and_glioblastoma/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Hypertrophic_Cardiomyopathy/GSE36961.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Kidney_Clear_Cell_Carcinoma/GSE119958.csv filter=lfs diff=lfs merge=lfs -text

p3/preprocess/Kidney_Clear_Cell_Carcinoma/GSE119958.csv

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p3/preprocess/Psoriatic_Arthritis/clinical_data/GSE141934.csv

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p3/preprocess/Psoriatic_Arthritis/clinical_data/GSE142049.csv

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p3/preprocess/Psoriatic_Arthritis/clinical_data/GSE57376.csv

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p3/preprocess/Psoriatic_Arthritis/clinical_data/GSE57383.csv

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p3/preprocess/Psoriatic_Arthritis/clinical_data/GSE57386.csv

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+,GSM1381406,GSM1381407,GSM1381408,GSM1381409,GSM1381410,GSM1381411,GSM1381412,GSM1381413,GSM1381414,GSM1381415,GSM1381416,GSM1381417,GSM1381418,GSM1381419,GSM1381420,GSM1381422,GSM1381423,GSM1381424,GSM1381425,GSM1381426,GSM1381427,GSM1381428,GSM1381429,GSM1381430,GSM1381431,GSM1381432,GSM1381433,GSM1381434,GSM1381435,GSM1381436,GSM1381437,GSM1381438,GSM1381524,GSM1381525,GSM1381526,GSM1381527,GSM1381528,GSM1381529,GSM1381530,GSM1381531,GSM1381532,GSM1381533,GSM1381534,GSM1381535,GSM1381536,GSM1381537,GSM1381538,GSM1381539,GSM1381540,GSM1381541,GSM1381542,GSM1381543,GSM1381544,GSM1381545,GSM1381546,GSM1381547,GSM1381548,GSM1381549,GSM1381550,GSM1381551,GSM1381552,GSM1381553,GSM1381554,GSM1381555,GSM1381556,GSM1381557,GSM1381558,GSM1381559,GSM1381560,GSM1381561,GSM1381562,GSM1381563,GSM1381564,GSM1381565,GSM1381566,GSM1381567,GSM1381568,GSM1381569,GSM1381570,GSM1381571,GSM1381572,GSM1381573,GSM1381574,GSM1381575,GSM1381576,GSM1381577,GSM1381578,GSM1381579,GSM1381580,GSM1381581,GSM1381582,GSM1381583,GSM1381584,GSM1381585,GSM1381586,GSM1381587,GSM1381588,GSM1381589,GSM1381590,GSM1381591,GSM1381592,GSM1381593,GSM1381594,GSM1381595,GSM1381596,GSM1381597,GSM1381598,GSM1381599,GSM1381600,GSM1381601,GSM1381602,GSM1381603,GSM1381604,GSM1381605,GSM1381606,GSM1381607,GSM1381608,GSM1381609,GSM1381610,GSM1381611,GSM1381612,GSM1381613,GSM1381614,GSM1381615,GSM1381616,GSM1381617,GSM1381618,GSM1381619,GSM1381620,GSM1381621,GSM1381622,GSM1381623,GSM1381624,GSM1381625,GSM1381626,GSM1381627,GSM1381628,GSM1381629,GSM1381630,GSM1381631,GSM1381632,GSM1381633,GSM1381634,GSM1381635,GSM1382105,GSM1382106,GSM1382107,GSM1382108,GSM1382109,GSM1382110,GSM1382111,GSM1382112,GSM1382113,GSM1382114,GSM1382115,GSM1382116,GSM1382117,GSM1382118,GSM1382119,GSM1382120,GSM1382121,GSM1382122,GSM1382123,GSM1382124,GSM1382125,GSM1382126,GSM1382127,GSM1382128,GSM1382129,GSM1382130,GSM1382131,GSM1382132,GSM1382133,GSM1382134,GSM1382135,GSM1382136,GSM1382137,GSM1382138,GSM1382139,GSM1382140,GSM1382141,GSM1382142,GSM1382143,GSM1382144,GSM1382145,GSM1382146,GSM1382147,GSM1382148,GSM1382149,GSM1382150,GSM1382151,GSM1382152,GSM1382153,GSM1382154,GSM1382155,GSM1382156,GSM1382157,GSM1382158,GSM1382159,GSM1382160,GSM1382161,GSM1382162,GSM1382163,GSM1382164,GSM1382165,GSM1382166,GSM1382167,GSM1382168,GSM1382169,GSM1382170,GSM1382171,GSM1382172,GSM1382173,GSM1382174,GSM1382175,GSM1382176,GSM1382177,GSM1382178,GSM1382179,GSM1382180,GSM1382181,GSM1382182,GSM1382183,GSM1382184,GSM1382185,GSM1382186,GSM1382187,GSM1382188,GSM1382189,GSM1382190,GSM1382191,GSM1382192,GSM1382193,GSM1382194,GSM1382195,GSM1382196,GSM1382197,GSM1382198,GSM1382199,GSM1382200,GSM1382201,GSM1382202,GSM1382203,GSM1382204,GSM1382205,GSM1382206,GSM1382207,GSM1382208,GSM1382209,GSM1382210,GSM1382211,GSM1382212,GSM1382213,GSM1382214,GSM1382215
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+Age,51.0,28.0,46.0,57.0,61.0,35.0,28.0,19.0,28.0,61.0,57.0,35.0,19.0,67.0,38.0,55.0,39.0,55.0,19.0,61.0,28.0,35.0,57.0,51.0,28.0,28.0,46.0,44.0,67.0,52.0,39.0,55.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.0,40.0,64.0,23.0,60.0,32.0,46.0,24.0,23.0,62.0,42.0,36.0,36.0,40.0,44.0,23.0,56.0,46.0,47.0,50.0,51.0,62.0,51.0,46.0,66.0,28.0,58.0,45.0,66.0,51.0,46.0,50.0,28.0,45.0,58.0,62.0,51.0,46.0,51.0,51.0,45.0,58.0,28.0,66.0,62.0,50.0,46.0,19.0,59.0,44.0,57.0,53.0,24.0,28.0,35.0,61.0,61.0,44.0,35.0,57.0,59.0,19.0,28.0,53.0,24.0,46.0,53.0,24.0,57.0,46.0,61.0,59.0,44.0,35.0,19.0,28.0,39.0,55.0,38.0,60.0,52.0,44.0,67.0,68.0,39.0,58.0,70.0,31.0,39.0,31.0,58.0,67.0,39.0,55.0,38.0,68.0,60.0,52.0,44.0,70.0,60.0,55.0,52.0,67.0,68.0,38.0,70.0,44.0,31.0,58.0,39.0
+Gender,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0

p3/preprocess/Psoriatic_Arthritis/clinical_data/GSE57405.csv

@@ -0,0 +1,4 @@
+,GSM1382105,GSM1382106,GSM1382107,GSM1382108,GSM1382109,GSM1382110,GSM1382111,GSM1382112,GSM1382113,GSM1382114,GSM1382115,GSM1382116,GSM1382117,GSM1382118,GSM1382119,GSM1382120,GSM1382121,GSM1382122,GSM1382123,GSM1382124,GSM1382125,GSM1382126,GSM1382127,GSM1382128,GSM1382129,GSM1382130,GSM1382131,GSM1382132,GSM1382133,GSM1382134,GSM1382135,GSM1382136,GSM1382137,GSM1382138,GSM1382139,GSM1382140,GSM1382141,GSM1382142,GSM1382143,GSM1382144,GSM1382145,GSM1382146,GSM1382147,GSM1382148,GSM1382149,GSM1382150,GSM1382151,GSM1382152,GSM1382153,GSM1382154,GSM1382155,GSM1382156,GSM1382157,GSM1382158,GSM1382159,GSM1382160,GSM1382161,GSM1382162,GSM1382163,GSM1382164,GSM1382165,GSM1382166,GSM1382167,GSM1382168,GSM1382169,GSM1382170,GSM1382171,GSM1382172,GSM1382173,GSM1382174,GSM1382175,GSM1382176,GSM1382177,GSM1382178,GSM1382179,GSM1382180,GSM1382181,GSM1382182,GSM1382183,GSM1382184,GSM1382185,GSM1382186,GSM1382187,GSM1382188,GSM1382189,GSM1382190,GSM1382191,GSM1382192,GSM1382193,GSM1382194,GSM1382195,GSM1382196,GSM1382197,GSM1382198,GSM1382199,GSM1382200,GSM1382201,GSM1382202,GSM1382203,GSM1382204,GSM1382205,GSM1382206,GSM1382207,GSM1382208,GSM1382209,GSM1382210,GSM1382211,GSM1382212,GSM1382213,GSM1382214,GSM1382215
+Psoriatic_Arthritis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,54.0,40.0,64.0,23.0,60.0,32.0,46.0,24.0,23.0,62.0,42.0,36.0,36.0,40.0,44.0,23.0,56.0,46.0,47.0,50.0,51.0,62.0,51.0,46.0,66.0,28.0,58.0,45.0,66.0,51.0,46.0,50.0,28.0,45.0,58.0,62.0,51.0,46.0,51.0,51.0,45.0,58.0,28.0,66.0,62.0,50.0,46.0,19.0,59.0,44.0,57.0,53.0,24.0,28.0,35.0,61.0,61.0,44.0,35.0,57.0,59.0,19.0,28.0,53.0,24.0,46.0,53.0,24.0,57.0,46.0,61.0,59.0,44.0,35.0,19.0,28.0,39.0,55.0,38.0,60.0,52.0,44.0,67.0,68.0,39.0,58.0,70.0,31.0,39.0,31.0,58.0,67.0,39.0,55.0,38.0,68.0,60.0,52.0,44.0,70.0,60.0,55.0,52.0,67.0,68.0,38.0,70.0,44.0,31.0,58.0,39.0
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p3/preprocess/Psoriatic_Arthritis/clinical_data/GSE61281.csv

@@ -0,0 +1,3 @@
+,GSM1501512,GSM1501513,GSM1501514,GSM1501515,GSM1501516,GSM1501517,GSM1501518,GSM1501519,GSM1501520,GSM1501521,GSM1501522,GSM1501523,GSM1501524,GSM1501525,GSM1501526,GSM1501527,GSM1501528,GSM1501529,GSM1501530,GSM1501531,GSM1501532,GSM1501533,GSM1501534,GSM1501535,GSM1501536,GSM1501537,GSM1501538,GSM1501539,GSM1501540,GSM1501541,GSM1501542,GSM1501543,GSM1501544,GSM1501545,GSM1501546,GSM1501547,GSM1501548,GSM1501549,GSM1501550,GSM1501551,GSM1501552,GSM1501553,GSM1501554,GSM1501555,GSM1501556,GSM1501557,GSM1501558,GSM1501559,GSM1501560,GSM1501561,GSM1501562,GSM1501563
+Psoriatic_Arthritis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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p3/preprocess/Psoriatic_Arthritis/code/GSE141934.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Psoriatic_Arthritis"
+cohort = "GSE141934"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
+in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE141934"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE141934.csv"
+out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE141934.csv"
+out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE141934.csv"
+json_path = "./output/preprocess/3/Psoriatic_Arthritis/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# This dataset contains T cell transcriptional data according to background, so gene expression data is available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Row Identification
+trait_row = 6  # working_diagnosis contains Psoriatic Arthritis
+age_row = 2    # Age data available
+gender_row = 1  # Gender data available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    # Binary: 1 for Psoriatic Arthritis, 0 for others
+    if not value or ':' not in value:
+        return None
+    diagnosis = value.split(': ')[1].strip()
+    if diagnosis == 'Psoriatic Arthritis':
+        return 1
+    elif diagnosis in ['Rheumatoid Arthritis', 'Reactive Arthritis', 'Crystal Arthritis', 
+                      'Osteoarthritis', 'Non-Inflammatory', 'Undifferentiated Inflammatory Arthritis',
+                      'Other Inflammatory Arthritis', 'Enteropathic Arthritis', 
+                      'Undifferentiated Spondylo-Arthropathy', 'Unknown']:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    # Continuous
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    # Binary: 0 for female, 1 for male
+    if not value or ':' not in value:
+        return None
+    gender = value.split(': ')[1].strip()
+    if gender == 'F':
+        return 0
+    elif gender == 'M':
+        return 1
+    return None
+
+# 3. 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=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,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the data
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The identifiers start with "ILMN_", indicating these are Illumina probe IDs
+# We need to map these probe IDs to human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Get gene mapping data - 'ID' is probe ID, 'Symbol' is gene symbol
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level measurements to gene expression
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Print dimensions after mapping
+print("\nShape after mapping:", gene_data.shape)
+
+# Preview first few gene symbols 
+print("\nFirst few genes:")
+print(list(gene_data.index)[:10])
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Psoriatic_Arthritis/code/GSE142049.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Psoriatic_Arthritis"
+cohort = "GSE142049"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
+in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE142049"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE142049.csv"
+out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE142049.csv"
+out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE142049.csv"
+json_path = "./output/preprocess/3/Psoriatic_Arthritis/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# This dataset has transcriptional data from B cells, which indicates gene expression data
+is_gene_available = True 
+
+# 2. Data Type Conversion Functions
+def convert_trait(x):
+    if x is None or ':' not in x:
+        return None
+    # Extract diagnosis after colon
+    diagnosis = x.split(': ')[1].strip()
+    # Binary classification: Psoriatic Arthritis vs others
+    return 1 if diagnosis == 'Psoriatic Arthritis' else 0
+
+def convert_age(x):
+    if x is None or ':' not in x:
+        return None
+    try:
+        # Extract age value after colon and convert to float
+        return float(x.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(x):
+    if x is None or ':' not in x:
+        return None
+    gender = x.split(': ')[1].strip()
+    # Convert to binary: Female=0, Male=1
+    if gender == 'F':
+        return 0
+    elif gender == 'M':
+        return 1
+    return None
+
+# Identify row indices for variables
+trait_row = 6  # working_diagnosis contains trait info
+age_row = 2    # age is available
+gender_row = 1 # gender is available
+
+# 3. Save initial metadata
+is_trait_available = trait_row is not None
+is_usable = 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 if trait data is available
+if trait_row is not None:
+    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 the processed clinical data
+    preview = preview_df(clinical_df)
+    print("Preview of processed clinical data:")
+    print(preview)
+    
+    # Save clinical data
+    clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Observe identifiers start with "ILMN_" - these are Illumina probe IDs that need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Extract mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Look at the first few rows of mapped gene data
+print("Gene expression data after mapping:")
+print(gene_data.head())
+print("\nShape:", gene_data.shape)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Psoriatic_Arthritis/code/GSE57376.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Psoriatic_Arthritis"
+cohort = "GSE57376"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
+in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE57376"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE57376.csv"
+out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE57376.csv"
+out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE57376.csv"
+json_path = "./output/preprocess/3/Psoriatic_Arthritis/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Looking at the background info, this is a gene expression study of tissue biopsies
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (disease status) is in row 5, Age in row 1, Gender in row 0
+trait_row = 5
+age_row = 1  
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert disease status to binary (0: no PsA, 1: has PsA)"""
+    if not value or ':' not in value:
+        return None
+    disease = value.split(': ')[1].strip()
+    if disease == 'Psoriatic Arthritis':
+        return 1
+    elif disease in ['Rheumatoid Arthritis', 'Psoriasis']:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0: female, 1: male)"""
+    if not value or ':' not in value:
+        return None
+    gender = value.split(': ')[1].strip()
+    if gender.upper() == 'F':
+        return 0
+    elif gender.upper() == 'M':
+        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. 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
+    preview = preview_df(clinical_features)
+    
+    # Save clinical data
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Looking at identifiers like '1007_PM_s_at', these are Affymetrix probe IDs
+# rather than standard human gene symbols
+# They need to be mapped to proper gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. Get probe ID and gene symbol columns
+# In gene_annotation, 'ID' contains probe IDs and 'Gene Symbol' contains gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 2. Apply the mapping to convert probe measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Psoriatic_Arthritis/code/GSE57383.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Psoriatic_Arthritis"
+cohort = "GSE57383"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
+in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE57383"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE57383.csv"
+out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE57383.csv"
+out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE57383.csv"
+json_path = "./output/preprocess/3/Psoriatic_Arthritis/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Dataset appears to be gene expression data from CD14+ cells
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Identify row indices
+trait_row = 6  # 'disease' row contains trait info
+age_row = 2    # 'age' row contains age info 
+gender_row = 1 # 'Sex' row contains gender info
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert trait value to binary (0=control, 1=case)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip()
+    if value == 'Psoriatic Arthritis':
+        return 1
+    elif value == 'Health Control':
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age value to continuous numeric"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0=female, 1=male)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip()
+    if value == 'F':
+        return 0
+    elif value == 'M':
+        return 1
+    return None
+
+# 3. Save initial filtering 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. 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 extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Checking identifiers - these are Affymetrix probe IDs (ending in "_PM_at" etc)
+# rather than standard human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Get gene mapping dataframe using ID and Gene Symbol columns
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview data
+print("Data shape after mapping:", gene_data.shape)
+print("\nPreview of mapped gene data:")
+print(preview_df(gene_data))
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Psoriatic_Arthritis/code/GSE57386.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Psoriatic_Arthritis"
+cohort = "GSE57386"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
+in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE57386"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE57386.csv"
+out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE57386.csv"
+out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE57386.csv"
+json_path = "./output/preprocess/3/Psoriatic_Arthritis/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# The series title mentions "Gene expression" and includes biopsies and cell samples,
+# suggesting this dataset contains gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability & 2.2 Data Type Conversion
+# Trait (PsA) can be determined from "disease" field in row 5
+trait_row = 5
+
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    # Extract value after colon and strip whitespace
+    value = value.split(':')[-1].strip()
+    # Convert to binary: 1 for PsA, 0 for others
+    if value == 'Psoriatic Arthritis':
+        return 1
+    elif value in ['Rheumatoid Arthritis', 'Psoriasis', 'normal', 'diseased', 'Healthy Control']:
+        return 0
+    return None
+
+# Age is available in row 1
+age_row = 1
+
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    try:
+        # Extract number after "age: "
+        age = int(value.split(':')[1].strip())
+        return age
+    except:
+        return None
+
+# Gender (Sex) is available in row 0
+gender_row = 0
+
+def convert_gender(value):
+    if pd.isna(value):
+        return None
+    # Extract value after colon
+    value = value.split(':')[1].strip()
+    # Convert to binary: 0 for F, 1 for M
+    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_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 clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The identifiers like "1007_PM_s_at" are probe IDs from Affymetrix GeneChip Human Genome arrays
+# These need to be mapped to standard gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Get gene mapping from annotation data
+# 'ID' column matches the probe IDs in expression data, 'Gene Symbol' contains target gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Print shape and preview gene data 
+print("\nGene expression data shape:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head())
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Psoriatic_Arthritis/code/GSE57405.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Psoriatic_Arthritis"
+cohort = "GSE57405"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
+in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE57405"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE57405.csv"
+out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE57405.csv"
+out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE57405.csv"
+json_path = "./output/preprocess/3/Psoriatic_Arthritis/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on background info mentioning "gene expression" data
+
+# 2.1 Data Availability
+trait_row = 5  # 'disease' field contains trait info
+age_row = 1    # 'age' field available
+gender_row = 0 # 'Sex' field available 
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> Optional[int]:
+    if not isinstance(x, str):
+        return None
+    # Extract value after colon and strip whitespace
+    value = x.split(':')[1].strip() if ':' in x else x.strip()
+    # Convert to binary: 1 for PsA, 0 for others
+    if value == 'Psoriatic Arthritis':
+        return 1
+    elif value in ['Healthy Control', 'Rheumatoid Arthritis', 'Psoriasis']:
+        return 0
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    if not isinstance(x, str):
+        return None
+    try:
+        # Extract numeric value after colon
+        value = x.split(':')[1].strip()
+        return float(value)
+    except:
+        return None
+
+def convert_gender(x: str) -> Optional[int]:
+    if not isinstance(x, str):
+        return None
+    # Extract value after colon and strip whitespace
+    value = x.split(':')[1].strip() if ':' in x else x.strip()
+    if value.upper() == 'F':
+        return 0
+    elif value.upper() == 'M':
+        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 extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Looking at the probe IDs like "1007_PM_s_at", these are Affymetrix probe IDs, not gene symbols
+# The "_PM" suffix indicates Perfect Match probes from an Affymetrix microarray
+# These identifiers need to be mapped to standard gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Get the mapping between probes and genes
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Observe the result
+print("\nShape of gene expression data:", gene_data.shape)
+print("\nFirst few mapped genes and their expression values:")
+print(gene_data.head())
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Psoriatic_Arthritis/code/GSE61281.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Psoriatic_Arthritis"
+cohort = "GSE61281"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
+in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE61281"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE61281.csv"
+out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE61281.csv"
+out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE61281.csv"
+json_path = "./output/preprocess/3/Psoriatic_Arthritis/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data
+is_gene_available = True  # Study title indicates whole blood transcriptional profiling
+
+# 2.1 Data Availability
+trait_row = 1  # 'condition' field contains case/control status
+gender_row = 2  # 'gender' field available
+age_row = None  # Age data not directly available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert trait value to binary: 1 for PsA cases, 0 for controls and PsC"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'psoriatic arthritis' in value:
+        return 1
+    elif 'unaffected control' in value or 'cutaneous psoriasis without arthritis' 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
+
+# 3. Save Initial 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. Extract Clinical Features
+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,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the selected features
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # Save clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Looking at the gene identifiers like 'A_23_P100001', these are Agilent array probe IDs
+# They need to be mapped to standard human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Create mapping dataframe using ID and GENE_SYMBOL columns
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
+
+# Apply gene mapping to get gene expression data 
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview the mapped gene expression data
+print("Gene expression data after mapping:")
+print(gene_data.head())
+print("\nShape:", gene_data.shape)
+print("\nFirst 20 gene symbols:")
+print(list(gene_data.index)[:20])
+
+# Save the gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Psoriatic_Arthritis/code/TCGA.py

@@ -0,0 +1,30 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Psoriatic_Arthritis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Psoriatic_Arthritis/cohort_info.json"
+
+# 1. Check TCGA directories for PTSD-related data
+all_dirs = ['CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)', 'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)', 'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)', 'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)', 'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)']
+
+# No directories contain PTSD-related data since TCGA is a cancer database
+is_gene_available = False
+is_trait_available = False
+
+# Record that no suitable data is available
+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/Psoriatic_Arthritis/cohort_info.json

@@ -0,0 +1 @@
+{"GSE61281": {"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": 52, "note": "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."}, "GSE57405": {"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": 111, "note": "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."}, "GSE57386": {"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": 255, "note": "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."}, "GSE57383": {"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": 55, "note": "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."}, "GSE57376": {"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": 32, "note": "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."}, "GSE142049": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 114, "note": "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."}, "GSE141934": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 100, "note": "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."}, "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/Rectal_Cancer/clinical_data/GSE109057.csv

@@ -0,0 +1,4 @@
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+Rectal_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
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p3/preprocess/Rectal_Cancer/clinical_data/GSE119409.csv

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p3/preprocess/Rectal_Cancer/clinical_data/GSE123390.csv

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p3/preprocess/Rectal_Cancer/clinical_data/GSE133057.csv

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p3/preprocess/Rectal_Cancer/clinical_data/GSE139255.csv

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p3/preprocess/Rectal_Cancer/clinical_data/GSE145037.csv

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p3/preprocess/Rectal_Cancer/clinical_data/GSE150082.csv

@@ -0,0 +1,4 @@
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p3/preprocess/Rectal_Cancer/clinical_data/GSE170999.csv

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p3/preprocess/Rectal_Cancer/clinical_data/GSE40492.csv

@@ -0,0 +1,4 @@
+,GSM994976,GSM994977,GSM994978,GSM994979,GSM994980,GSM994981,GSM994982,GSM994983,GSM994984,GSM994985,GSM994986,GSM994987,GSM994988,GSM994989,GSM994990,GSM994991,GSM994992,GSM994993,GSM994994,GSM994995,GSM994996,GSM994997,GSM994998,GSM994999,GSM995000,GSM995001,GSM995002,GSM995003,GSM995004,GSM995005,GSM995006,GSM995007,GSM995008,GSM995009,GSM995010,GSM995011,GSM995012,GSM995013,GSM995014,GSM995015,GSM995016,GSM995017,GSM995018,GSM995019,GSM995020,GSM995021,GSM995022,GSM995023,GSM995024,GSM995025,GSM995026,GSM995027,GSM995028,GSM995029,GSM995030,GSM995031,GSM995032,GSM995033,GSM995034,GSM995035,GSM995036,GSM995037,GSM995038,GSM995039,GSM995040,GSM995041,GSM995042,GSM995043,GSM995044,GSM995045,GSM995046,GSM995047,GSM995048,GSM995049,GSM995050,GSM995051,GSM995052,GSM995053,GSM995054,GSM995055,GSM995056,GSM995057,GSM995058,GSM995059,GSM995060,GSM995061,GSM995062,GSM995063,GSM995064,GSM995065,GSM995066,GSM995067,GSM995068,GSM995069,GSM995070,GSM995071,GSM995072,GSM995073,GSM995074,GSM995075,GSM995076,GSM995077,GSM995078,GSM995079,GSM995080,GSM995081,GSM995082,GSM995083,GSM995084,GSM995085,GSM995086,GSM995087,GSM995088,GSM995089,GSM995090,GSM995091,GSM995092,GSM995093,GSM995094,GSM995095,GSM995096,GSM995097,GSM995098,GSM995099,GSM995100,GSM995101,GSM995102,GSM995103,GSM995104,GSM995105,GSM995106,GSM995107,GSM995108,GSM995109,GSM995110,GSM995111,GSM995112,GSM995113,GSM995114,GSM995115,GSM995116,GSM995117,GSM995118,GSM995119,GSM995120,GSM995121,GSM995122,GSM995123,GSM995124,GSM995125,GSM995126,GSM995127,GSM995128,GSM995129,GSM995130,GSM995131,GSM995132,GSM995133,GSM995134,GSM995135,GSM995136,GSM995137,GSM995138,GSM995139,GSM995140,GSM995141,GSM995142,GSM995143,GSM995144,GSM995145,GSM995146,GSM995147,GSM995148,GSM995149,GSM995150,GSM995151,GSM995152,GSM995153,GSM995154,GSM995155,GSM995156,GSM995157,GSM995158,GSM995159,GSM995160,GSM995161,GSM995162,GSM995163,GSM995164,GSM995165,GSM995166,GSM995167,GSM995168,GSM995169,GSM995170,GSM995171,GSM995172,GSM995173,GSM995174,GSM995175,GSM995176,GSM995177,GSM995178,GSM995179,GSM995180,GSM995181,GSM995182,GSM995183,GSM995184,GSM995185,GSM995186,GSM995187,GSM995188,GSM995189,GSM995190,GSM995191,GSM995192,GSM995193,GSM995194,GSM995195,GSM995196,GSM995197,GSM995198,GSM995199,GSM995200,GSM995201,GSM995202,GSM995203,GSM995204,GSM995205,GSM995206,GSM995207,GSM995208,GSM995209,GSM995210,GSM995211,GSM995212,GSM995213,GSM995214,GSM995215,GSM995216,GSM995217,GSM995218,GSM995219,GSM995220
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+Age,55.5,65.6,62.6,61.8,52.1,59.1,70.6,60.6,55.0,53.1,58.5,68.4,58.8,70.0,77.5,68.4,75.2,76.3,38.2,61.1,69.4,54.2,77.7,57.4,61.2,56.5,47.0,62.7,51.2,73.2,47.2,72.7,38.8,75.4,79.9,60.9,76.2,49.4,49.9,58.1,77.2,72.3,59.0,76.2,69.0,61.3,66.3,65.5,68.3,78.8,65.8,57.0,65.6,74.1,48.1,57.2,61.8,65.4,49.1,63.7,58.5,66.3,62.0,57.3,48.3,71.1,71.2,52.6,70.3,54.0,53.7,60.0,68.2,65.7,79.7,81.3,66.3,59.2,57.3,81.5,62.3,63.6,66.8,71.5,76.7,76.5,58.3,76.6,62.5,51.2,67.8,63.3,76.3,41.8,58.0,65.7,59.5,53.6,56.8,60.7,64.0,55.5,50.0,35.6,77.7,65.1,74.6,63.0,73.2,73.3,54.8,61.2,63.2,63.9,75.4,68.2,51.1,46.3,59.4,70.9,59.8,81.1,72.9,70.8,63.8,63.6,72.3,51.9,69.5,65.1,54.8,56.2,66.2,79.5,67.5,54.7,73.7,61.9,73.9,50.5,76.9,41.7,73.0,41.6,67.0,61.9,42.0,68.7,59.3,68.0,78.7,57.2,63.6,60.7,72.0,62.5,51.7,56.5,74.9,61.5,69.8,63.5,58.9,53.1,65.3,71.9,46.2,63.8,47.5,52.4,64.3,65.1,47.6,60.3,70.9,56.3,53.6,55.8,61.2,59.3,62.4,40.8,68.4,62.6,60.2,72.0,70.9,67.5,70.4,61.4,53.7,36.2,58.6,53.8,61.2,67.0,66.8,69.3,60.6,75.0,55.7,48.5,76.2,70.8,76.7,64.3,77.2,70.1,62.5,52.7,60.4,53.3,66.4,56.2,60.7,61.9,69.6,63.2,81.3,58.3,62.8,75.9,74.3,71.4,74.2,76.5,77.5,59.8,46.0,80.7,65.6,75.5,59.9,72.8,57.5,70.7,74.8,75.7,57.5,70.9,66.9,74.3,43.3,83.1,84.3
+Gender,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0

p3/preprocess/Rectal_Cancer/clinical_data/GSE94104.csv

@@ -0,0 +1,2 @@
+,GSM2469019,GSM2469020,GSM2469021,GSM2469022,GSM2469023,GSM2469024,GSM2469025,GSM2469026,GSM2469027,GSM2469028,GSM2469029,GSM2469030,GSM2469031,GSM2469032,GSM2469033,GSM2469034,GSM2469035,GSM2469036,GSM2469037,GSM2469038,GSM2469039,GSM2469040,GSM2469041,GSM2469042,GSM2469043,GSM2469044,GSM2469045,GSM2469046,GSM2469047,GSM2469048,GSM2469049,GSM2469050,GSM2469051,GSM2469052,GSM2469053,GSM2469054,GSM2469055,GSM2469056,GSM2469057,GSM2469058,GSM2469059,GSM2469060,GSM2469061,GSM2469062,GSM2469063,GSM2469064,GSM2469065,GSM2469066,GSM2469067,GSM2469068,GSM2469069,GSM2469070,GSM2469071,GSM2469072,GSM2469073,GSM2469074,GSM2469075,GSM2469076,GSM2469077,GSM2469078,GSM2469079,GSM2469080,GSM2469081,GSM2469082,GSM2469083,GSM2469084,GSM2469085,GSM2469086,GSM2469087,GSM2469088,GSM2469089,GSM2469090,GSM2469091,GSM2469092,GSM2469093,GSM2469094,GSM2469095,GSM2469096,GSM2469097,GSM2469098
+Rectal_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0

p3/preprocess/Rectal_Cancer/code/GSE109057.py

@@ -0,0 +1,164 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE109057"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE109057"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE109057.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE109057.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE109057.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# This is gene expression microarray data, as stated in background info
+is_gene_available = True
+
+# 2. Feature Row and Type Conversion Functions
+# trait is available from row 0
+trait_row = 0
+def convert_trait(x: str) -> int:
+    # All samples are rectal cancer, binary encoding 1
+    return 1 
+
+# gender is available from row 1
+gender_row = 1
+def convert_gender(x: str) -> int:
+    val = x.split(': ')[1]
+    if val == 'F':
+        return 0
+    elif val == 'M':
+        return 1
+    return None
+
+# age is available from row 2
+age_row = 2
+def convert_age(x: str) -> float:
+    val = x.split(': ')[1]
+    # Extract lower bound of age range as representative value
+    try:
+        lower = float(val.split(' <=')[0])
+        return lower
+    except:
+        return None
+
+# 3. Save initial filtering results
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True
+)
+
+# 4. Extract clinical features and save
+clinical_df = 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
+print(preview_df(clinical_df))
+
+# Save to CSV
+clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Based on the row IDs like '11715100_at', '11715101_s_at', '11715102_x_at'
+# These appear to be Affymetrix probe IDs rather than gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Create mapping dataframe using ID and Gene Symbol columns
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
+
+# Apply gene mapping to convert probe level data to gene level
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Normalize gene symbols using standard dictionary
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save gene expression data 
+gene_data.to_csv(out_gene_data_file)
+
+# Print preview of gene data 
+print("Preview of gene expression data:")
+print(preview_df(gene_data))
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE119409.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE119409"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE119409"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE119409.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE119409.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE119409.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# From series title and summary, this dataset contains gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (sensitivity to therapy) is in row 2
+trait_row = 2
+
+# Age is in row 3
+age_row = 3 
+
+# Gender is not available
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    x = x.split(': ')[1].lower()
+    if x == 'sensitive':
+        return 1
+    elif x == 'resistant':
+        return 0
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        age = int(x.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    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
+# Extract features since trait data is available
+clinical_df = 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
+print("Preview of clinical features:")
+print(preview_df(clinical_df))
+
+# Save to CSV
+clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The gene identifiers in the data appear to be Affymetrix probe IDs (e.g. "1007_s_at", "1053_at")
+# These are not standard gene symbols and need to be mapped to HGNC gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# From the preview, we can see 'ID' contains probe IDs matching gene expression data
+# and 'Gene Symbol' contains corresponding gene symbols
+
+# Get gene mapping between probe IDs and gene symbols
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe data to gene expression data 
+gene_data = apply_gene_mapping(genetic_data, gene_mapping)
+
+# Normalize the gene symbols in the data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Print shape of final gene data
+print("Gene expression data shape:", gene_data.shape)
+print("\nPreview of gene data:")
+print(preview_df(gene_data))
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE123390.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE123390"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE123390"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE123390.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE123390.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE123390.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Availability
+# Yes - Using Affymetrix Human Transcriptome Array 2.0 for global gene expression
+is_gene_available = True
+
+# 2. Variable Availability and Conversion
+
+# Trait (Response to treatment)
+# Available in row 2 as "response" - binary outcome (pCR vs pIR)
+trait_row = 2
+
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.split(": ")[-1].strip()
+    if value == "pCR":  # Complete response
+        return 1
+    elif value == "pIR":  # Incomplete response  
+        return 0
+    return None
+
+# Age - Not available 
+age_row = None
+convert_age = None
+
+# Gender - Not available
+gender_row = None 
+convert_gender = 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. Extract clinical features
+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
+    )
+    
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Based on observation of the row IDs like "2824546_st", these are Affymetrix probe IDs
+# rather than standard human gene symbols. They will need to be mapped to gene symbols.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# The 'ID' column in gene annotation appears to contain probe IDs that match the gene expression data index
+# The 'gene_assignment' column contains gene symbols, but needs extraction
+
+# Get gene mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply gene mapping to convert probe-level measurements to gene expression
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Print shape and preview gene expression data
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nPreview of gene data:")
+print(preview_df(gene_data))
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE133057.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE133057"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE133057"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE133057.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE133057.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE133057.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, from background info this is transcriptomic analysis of rectal cancer biopsies
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# AJCC score is a measure of tumor regression (phenotypic trait)
+trait_row = 1  
+gender_row = 2
+age_row = 5
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert AJCC score to binary where 0,1 are good response (0) and 2,3 are poor response (1)"""
+    if not value or ':' not in value:
+        return None
+    score = int(value.split(': ')[1])
+    if score in [0, 1]:
+        return 0  # good response
+    elif score in [2, 3]:
+        return 1  # poor response
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age string to float"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0=female, 1=male)"""
+    if not value or ':' not in value:
+        return None
+    gender = value.split(': ')[1].lower()
+    if gender == 'female':
+        return 0
+    elif gender == 'male':
+        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)
+    
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Based on "ILMN_" prefix in gene identifiers, these appear to be Illumina probe IDs, not gene symbols
+# They will need to be mapped to standard gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. From observation:
+# - Gene expression data uses 'ILMN_' prefixed IDs
+# - In gene annotation, 'ID' column stores these identifiers, and 'Symbol' column stores gene symbols
+
+# 2. Get gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Symbol')
+
+# 3. Apply mapping to convert probe level data to gene level data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview and save the gene expression data
+print("\nGene expression data shape:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(preview_df(gene_data))
+
+gene_data.to_csv(out_gene_data_file)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE139255.py

@@ -0,0 +1,131 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE139255"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE139255"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE139255.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE139255.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE139255.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes - The background info mentions gene expression analysis with nCounter PanCancer Pathway Panel analyzing 770 genes
+is_gene_available = True
+
+# 2. Variable Availability and Type Conversion
+# 2.1 Row identification 
+trait_row = 0 # Histological response data is in row 0
+age_row = None # Age data not available 
+gender_row = None # Gender data not available
+
+# 2.2 Conversion functions
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].strip().lower()
+    if 'good-response' in value:
+        return 1
+    elif 'non-response' in value:
+        return 0
+    return None
+
+convert_age = None
+convert_gender = 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. Extract clinical features
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview and save clinical data
+print("Clinical data preview:")
+print(preview_df(clinical_df))
+clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Looking at the gene IDs like ABL1, ACAD9, ACVR1B - these are standard human gene symbols
+# No mapping required as they are already in the correct format
+requires_gene_mapping = False
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols 
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE145037.py

@@ -0,0 +1,137 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE145037"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE145037"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE145037.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE145037.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE145037.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# From background info, this dataset contains gene expression data from rectal cancer patients
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Row identifiers
+trait_row = 3  # response to CRT in row 3
+age_row = 2    # age in row 2  
+gender_row = 1 # gender in row 1
+
+# 2.2 Conversion functions
+def convert_trait(x):
+    if not x:
+        return None
+    value = x.split(': ')[1].lower()
+    return 0 if 'non-response' in value else 1
+
+def convert_age(x):
+    if not x:
+        return None
+    try:
+        return int(x.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(x):
+    if not x:
+        return None
+    value = x.split(': ')[1].lower()
+    return 1 if 'male' in value else 0
+
+# 3. Save metadata for initial filtering
+is_trait_avail = 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_avail)
+
+# 4. Extract clinical features
+selected_clinical = geo_select_clinical_features(clinical_data,
+                                               trait="Response",
+                                               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 data
+preview_dict = preview_df(selected_clinical)
+print("Preview of extracted clinical data:")
+print(preview_dict)
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The IDs are human gene symbols (e.g. A1BG, A1CF, A2M) and do not need mapping
+requires_gene_mapping = False
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, "Response")
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, "Response")
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE150082.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE150082"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE150082"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE150082.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE150082.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE150082.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability 
+# From Series_title and Series_summary, we can see this is a microarray gene expression dataset
+is_gene_available = True 
+
+# 2. Variable Availability and Data Type Conversion
+# Trait (Response to treatment)
+trait_row = 4 # 'response' field has Good/Poor values
+def convert_trait(x):
+    if pd.isna(x): return None
+    val = x.split(': ')[1]
+    if val == 'Poor': return 0
+    if val == 'Good': return 1
+    return None
+
+# Age
+age_row = 2 
+def convert_age(x):
+    if pd.isna(x): return None
+    try:
+        return int(x.split(': ')[1])
+    except:
+        return None
+
+# Gender/Sex
+gender_row = 0
+def convert_gender(x): 
+    if pd.isna(x): return None
+    val = x.split(': ')[1]
+    if val == 'F': return 0
+    if val == 'M': 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
+clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
+                                               age_row, convert_age,
+                                               gender_row, convert_gender)
+
+# Preview the extracted features
+preview_dict = preview_df(clinical_features)
+print("Preview of clinical features:")
+print(preview_dict)
+
+# Save clinical data
+clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Based on inspecting the gene identifiers (e.g. 'A_23_P100001'), these appear to be probe IDs 
+# from an Agilent microarray platform, not standard human gene symbols.
+# They will need to be mapped to proper gene symbols.
+
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. Determine mapping columns:
+# 'ID' column in annotation contains same identifiers as gene expression data
+# 'GENE_SYMBOL' contains the gene symbols we want to map to
+
+# 2. Get mapping dataframe with ID and gene symbol columns
+gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
+
+# 3. Apply gene mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(genetic_data, gene_mapping)
+
+# Preview transformed data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of first few rows:")
+print(gene_data.head())
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients with focus on KRAS mutation status."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE170999.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE170999"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE170999"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE170999.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE170999.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE170999.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains gene expression data (Affymetrix U133 platform mentioned)
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Find row indices for clinical variables
+trait_row = 0  # KRAS mutation status is at row 0
+age_row = None  # Age data not available 
+gender_row = None  # Gender data not available
+
+# 2.2 Data type conversion functions
+def convert_trait(val):
+    if not isinstance(val, str):
+        return None
+    val = val.lower().split(': ')[-1]
+    if 'mutant' in val:
+        return 1
+    elif 'wild-type' in val:
+        return 0
+    return None
+    
+def convert_age(val):
+    return None  # Not used since age data not available
+
+def convert_gender(val):
+    return None  # Not used since gender data not available
+
+# 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. 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 clinical data
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# These appear to be probe IDs from Affymetrix U133 Plus 2.0 microarray
+# Not gene symbols - need to map to HGNC gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. The 'ID' column in gene annotation matches probe IDs in gene expression data
+# and 'Gene Symbol' contains the corresponding gene symbols
+
+# 2. Extract mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, "ID", "Gene Symbol")
+
+# 3. Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview gene data
+print("Preview of gene expression data after mapping:")
+print(gene_data.head())
+print("\nShape:", gene_data.shape)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients with focus on KRAS mutation status."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE40492.py

@@ -0,0 +1,176 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE40492"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE40492"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE40492.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE40492.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE40492.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene expression data availability check
+# From background info, it's a gene expression microarray study for molecular markers
+is_gene_available = True
+
+# 2. Variable availability and data type conversion
+# 2.1 Row identification 
+# Trait: Use pathological tumor status after treatment (lymph node status) from row 9
+trait_row = 9
+
+# Age: Available in row 1
+age_row = 1
+
+# Gender: Available in row 2 as 'Sex'
+gender_row = 2
+
+# 2.2 Conversion functions
+def convert_trait(x):
+    # Convert lymph node status to binary:
+    # 0 = negative nodes, 1 = positive nodes (1 or 2)
+    if x is None or 'NA' in str(x):
+        return None
+    val = x.split(': ')[1]
+    if val == '0':
+        return 0
+    elif val in ['1', '2']:
+        return 1
+    return None
+
+def convert_age(x):
+    if x is None or 'NA' in str(x):
+        return None
+    try:
+        return float(x.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(x):
+    if x is None or 'NA' in str(x):
+        return None
+    val = x.split(': ')[1].lower()
+    if 'female' in val:
+        return 0
+    elif 'male' in val:
+        return 1
+    return None
+
+# 3. Initial filtering and metadata saving
+is_usable = 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_df = geo_select_clinical_features(
+        clinical_data,
+        trait="Lymph_Node_Status",
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_df))
+    
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The IDs are just ordinal numbers and require mapping to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Extract probe ID and gene symbol columns from annotation
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
+
+# Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the processed data
+print("Preview of gene expression data:")
+print(gene_data.head())
+print("\nShape:", gene_data.shape)
+print("\nFirst 10 gene symbols:")
+print(list(gene_data.index)[:10])
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, "Lymph_Node_Status")
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, "Lymph_Node_Status")
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from rectal cancer patients with focus on lymph node status after treatment."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/GSE94104.py

@@ -0,0 +1,156 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+cohort = "GSE94104"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rectal_Cancer"
+in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE94104"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE94104.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE94104.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE94104.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/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)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# The background info mentions RNA and expression beadchip, so gene expression data is likely available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 2  # Tumour regression grade is available and varies
+age_row = None  # Age not provided
+gender_row = None  # Gender not provided
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    # Extract numeric grade after colon
+    try:
+        grade = int(value.split(': ')[1])
+        # Convert to binary: grade 1-2 (good response) vs grade 3 (poor response)
+        return 0 if grade <= 2 else 1
+    except:
+        return None
+
+# No age or gender conversion functions needed since data not available
+convert_age = None
+convert_gender = None
+
+# 3. Save Metadata
+is_usable = 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:
+    # Extract available 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 = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The gene identifiers start with "ILMN_" which indicates these are Illumina probe IDs
+# They need to be mapped to standard human gene symbols for downstream analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. Identify relevant columns
+# The gene expression data uses "ILMN_" IDs which match the 'ID' column in annotation
+# The 'Symbol' column contains gene symbols
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."
+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=trait_biased,
+    df=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)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rectal_Cancer/code/TCGA.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rectal_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rectal_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Rectal_Cancer/cohort_info.json"
+
+# 1. Find directory for rectal cancer data
+selected_dir = 'TCGA_Rectal_Cancer_(READ)'
+cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+# 2. Get file paths for clinical and genetic data
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data files
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t') 
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+
+# Record data availability 
+is_gene_available = len(genetic_df.columns) > 0
+is_trait_available = len(clinical_df.columns) > 0
+
+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
+)
+# Define candidate columns for age and gender
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+# Load clinical data directly from the root directory
+clinical_file_path = os.path.join(tcga_root_dir, "READ.clinical.txt")
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep="\t") 
+
+# Extract and preview age columns
+age_preview = {}
+for col in candidate_age_cols:
+    if col in clinical_df.columns:
+        age_preview[col] = clinical_df[col].head(5).tolist()
+print("Age columns preview:", preview_df(clinical_df[candidate_age_cols], n=5))
+
+# Extract and preview gender columns  
+gender_preview = {}
+for col in candidate_gender_cols:
+    if col in clinical_df.columns:
+        gender_preview[col] = clinical_df[col].head(5).tolist()
+print("\nGender columns preview:", preview_df(clinical_df[candidate_gender_cols], n=5))
+# For Rectal Cancer cohort from TCGA dataset
+candidate_age_cols = ["age_at_diagnosis", "age_at_index", "age_began_smoking", "age_at_initial_pathologic_diagnosis"]
+candidate_gender_cols = ["gender", "sex"]
+
+# Get clinical file path
+clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, trait))
+
+# Load clinical data
+clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
+
+# Create preview dictionaries
+age_preview = {}
+for col in candidate_age_cols:
+    if col in clinical_df.columns:
+        age_preview[col] = clinical_df[col].head().to_list()
+
+gender_preview = {}
+for col in candidate_gender_cols:
+    if col in clinical_df.columns:
+        gender_preview[col] = clinical_df[col].head().to_list()
+
+print("Age columns preview:", age_preview)
+print("Gender columns preview:", gender_preview)
+# 1. Find directory for rectal cancer data
+selected_dir = 'TCGA_Rectal_Cancer_(READ)'
+cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+# 2. Get file paths for clinical and genetic data
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data files
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t') 
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+
+# Record data availability 
+is_gene_available = len(genetic_df.columns) > 0
+is_trait_available = len(clinical_df.columns) > 0
+
+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
+)
+# Get age column name
+age_candidates = {
+    'age_at_initial_pathologic_diagnosis': ['56', '45', '72', '71', '65'],
+    'days_to_birth': ['-20454', '-27549', '-28914', '-24705', '-19724']
+}
+
+# 'age_at_initial_pathologic_diagnosis' is more direct and interpretable than 'days_to_birth'
+age_col = 'age_at_initial_pathologic_diagnosis' 
+
+# Get gender column name
+gender_candidates = {
+    'gender': ['MALE', 'FEMALE', 'MALE', 'MALE', 'MALE']
+}
+
+# 'gender' is the only and valid column for gender information 
+gender_col = 'gender'
+
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# 1. Extract and standardize clinical features
+selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
+
+# 2. Normalize gene symbols in genetic data
+normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_genetic_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data
+linked_data = pd.merge(selected_clinical_df, normalized_genetic_df.T, left_index=True, right_index=True)
+
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for bias in trait and demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Validate and save cohort info
+note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}"
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+    print(f"Linked data saved to {out_data_file}")
+    print("Shape of final linked data:", linked_data.shape)
+else:
+    print("Dataset was found to be unusable and was not saved")

p3/preprocess/Rectal_Cancer/cohort_info.json

@@ -0,0 +1 @@
+{"GSE94104": {"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": 80, "note": "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."}, "GSE40492": {"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": 245, "note": "Dataset contains gene expression data from rectal cancer patients with focus on lymph node status after treatment."}, "GSE170999": {"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": 76, "note": "Dataset contains gene expression data from rectal cancer patients with focus on KRAS mutation status."}, "GSE150082": {"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": 39, "note": "Dataset contains gene expression data from rectal cancer patients with focus on KRAS mutation status."}, "GSE145037": {"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": 31, "note": "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."}, "GSE139255": {"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": 156, "note": "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."}, "GSE133057": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 33, "note": "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."}, "GSE123390": {"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 rectal cancer patients examining chemoradiotherapy response."}, "GSE119409": {"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": 56, "note": "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."}, "GSE109057": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 91, "note": "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."}, "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": 105, "note": "Sample size after preprocessing: 105. Number of genes: 19848"}}