Datasets:

Liu-Hy
/

GenoTEX

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2	+ 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p3/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE131027.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM3759992,GSM3759993,GSM3759994,GSM3759995,GSM3759996,GSM3759997,GSM3759998,GSM3759999,GSM3760000,GSM3760001,GSM3760002,GSM3760003,GSM3760004,GSM3760005,GSM3760006,GSM3760007,GSM3760008,GSM3760009,GSM3760010,GSM3760011,GSM3760012,GSM3760013,GSM3760014,GSM3760015,GSM3760016,GSM3760017,GSM3760018,GSM3760019,GSM3760020,GSM3760021,GSM3760022,GSM3760023,GSM3760024,GSM3760025,GSM3760026,GSM3760027,GSM3760028,GSM3760029,GSM3760030,GSM3760031,GSM3760032,GSM3760033,GSM3760034,GSM3760035,GSM3760036,GSM3760037,GSM3760038,GSM3760039,GSM3760040,GSM3760041,GSM3760042,GSM3760043,GSM3760044,GSM3760045,GSM3760046,GSM3760047,GSM3760048,GSM3760049,GSM3760050,GSM3760051,GSM3760052,GSM3760053,GSM3760054,GSM3760055,GSM3760056,GSM3760057,GSM3760058,GSM3760059,GSM3760060,GSM3760061,GSM3760062,GSM3760063,GSM3760064,GSM3760065,GSM3760066,GSM3760067,GSM3760068,GSM3760069,GSM3760070,GSM3760071,GSM3760072,GSM3760073,GSM3760074,GSM3760075,GSM3760076,GSM3760077,GSM3760078,GSM3760079,GSM3760080,GSM3760081,GSM3760082,GSM3760083
2	+ Kidney_Clear_Cell_Carcinoma,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE150404.csv ADDED Viewed

	@@ -0,0 +1,4 @@

+,GSM4548605,GSM4548606,GSM4548607,GSM4548608,GSM4548609,GSM4548610,GSM4548611,GSM4548612,GSM4548613,GSM4548614,GSM4548615,GSM4548616,GSM4548617,GSM4548618,GSM4548619,GSM4548620,GSM4548621,GSM4548622,GSM4548623,GSM4548624,GSM4548625,GSM4548626,GSM4548627,GSM4548628,GSM4548629,GSM4548630,GSM4548631,GSM4548632,GSM4548633,GSM4548634,GSM4548635,GSM4548636,GSM4548637,GSM4548638,GSM4548639,GSM4548640,GSM4548641,GSM4548642,GSM4548643,GSM4548644,GSM4548645,GSM4548646,GSM4548647,GSM4548648,GSM4548649,GSM4548650,GSM4548651,GSM4548652,GSM4548653,GSM4548654,GSM4548655,GSM4548656,GSM4548657,GSM4548658,GSM4548659,GSM4548660,GSM4548661,GSM4548662,GSM4548663,GSM4548664
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p3/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE245862.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM7850012,GSM7850013,GSM7850014,GSM7850015,GSM7850016,GSM7850017,GSM7850018,GSM7850019,GSM7850020,GSM7850021,GSM7850022,GSM7850023,GSM7850024,GSM7850025,GSM7850026,GSM7850027,GSM7850028,GSM7850029,GSM7850030,GSM7850031,GSM7850032,GSM7850033,GSM7850034,GSM7850035,GSM7850036,GSM7850038,GSM7850039,GSM7850040,GSM7850041,GSM7850042,GSM7850043,GSM7850044,GSM7850045,GSM7850046,GSM7850047,GSM7850048,GSM7850049,GSM7850050,GSM7850051,GSM7850052,GSM7850053,GSM7850054,GSM7850055,GSM7850056,GSM7850057
2	+ Kidney_Clear_Cell_Carcinoma,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE94321.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM2473069,GSM2473070,GSM2473071,GSM2473072,GSM2473073,GSM2473074,GSM2473075,GSM2473076,GSM2473077,GSM2473078,GSM2473079,GSM2473080,GSM2473081,GSM2473082,GSM2473083,GSM2473084,GSM2473085,GSM2473086,GSM2473087,GSM2473088,GSM2473089,GSM2473090,GSM2473091,GSM2473092,GSM2473093,GSM2473094,GSM2473095,GSM2473096,GSM2473097,GSM2473098,GSM2473099,GSM2473100,GSM2473101,GSM2473102,GSM2473103,GSM2473104,GSM2473105,GSM2473106,GSM2473107,GSM2473108,GSM2473109,GSM2473110,GSM2473111,GSM2473112,GSM2473113,GSM2473114,GSM2473115,GSM2473116,GSM2473117,GSM2473118,GSM2473119,GSM2473120,GSM2473121,GSM2473122,GSM2473123,GSM2473124,GSM2473125,GSM2473126,GSM2473127,GSM2473128,GSM2473129,GSM2473130,GSM2473131,GSM2473132,GSM2473133,GSM2473134,GSM2473135,GSM2473136,GSM2473137
2	+ Kidney_Clear_Cell_Carcinoma,,,0.0,0.0,1.0,,,,,,0.0,0.0,,0.0,0.0,0.0,,,,,,,,,,,1.0,0.0,1.0,0.0,,0.0,,,,,0.0,0.0,,,0.0,0.0,0.0,,0.0,,,,0.0,,0.0,,0.0,0.0,,,,0.0,1.0,,0.0,0.0,0.0,1.0,0.0,0.0,0.0,,

p3/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE95425.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM2510512,GSM2510513,GSM2510514,GSM2510515,GSM2510516,GSM2510517,GSM2510518,GSM2510519,GSM2510520,GSM2510521,GSM2510522,GSM2510523,GSM2510524,GSM2510525,GSM2510526,GSM2510527,GSM2510528,GSM2510529,GSM2510530,GSM2510531,GSM2510532,GSM2510533,GSM2510534,GSM2510535,GSM2510536,GSM2510537,GSM2510538,GSM2510539,GSM2510540,GSM2510541,GSM2510542,GSM2510543,GSM2510544,GSM2510545,GSM2510546,GSM2510547,GSM2510548,GSM2510549,GSM2510550,GSM2510551,GSM2510552,GSM2510553,GSM2510554,GSM2510555,GSM2510556,GSM2510557,GSM2510558,GSM2510559,GSM2510560,GSM2510561,GSM2510562,GSM2510563,GSM2510564
2	+ Kidney_Clear_Cell_Carcinoma,0.0,,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,,1.0,1.0,0.0,,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,,1.0,,0.0,0.0,1.0

p3/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/TCGA.csv ADDED Viewed

	@@ -0,0 +1,946 @@

+sampleID,Kidney_Clear_Cell_Carcinoma,Age,Gender
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p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE102807.py ADDED Viewed

	@@ -0,0 +1,56 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE102807"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE102807"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE102807.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE102807.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE102807.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/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 from SOFT file instead of matrix file
+background_info, clinical_data = get_background_and_clinical_data(soft_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# Gene expression data availability
+is_gene_available = False  # Based on metadata, this appears to be ChIP-seq data, not gene expression
+# Define variable rows and conversion functions
+trait_row = None # No clinical trait info available
+age_row = None # No age info available
+gender_row = None # No gender info available
+def convert_trait(x):
+    return None
+def convert_age(x):
+    return None
+def convert_gender(x):
+    return None
+# 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)
+)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE106757.py ADDED Viewed

	@@ -0,0 +1,147 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE106757"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE106757"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE106757.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE106757.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE106757.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# Gene Expression Data Availability
+is_gene_available = True  # Yes, the study involves transcriptional analysis of monocytes
+# Variable Availability and Data Type Conversion
+trait_row = 0  # The trait (disease state) is in row 0
+age_row = None  # Age not available
+gender_row = None  # Gender not available
+def convert_trait(value: str) -> Optional[int]:
+    """Convert disease state to binary: 0 for healthy, 1 for renal cell carcinoma"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'healthy' in value:
+        return 0
+    elif 'renal cell carcinoma' in value or 'rcc' in value:
+        return 1
+    return None
+convert_age = None  # Age data not available
+convert_gender = None  # Gender data not available
+# Initial validation and saving metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+# Clinical feature extraction since 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
+)
+# Preview and save clinical features
+print("Clinical Features Preview:")
+print(preview_df(clinical_features))
+# Save clinical data
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These appear to be standard HGNC gene symbols mixed with some older identifiers
+# Most identifiers like A1BG, A2M, AAAS are valid HGNC symbols
+# However, some like 7A5, AAA1 are likely older or alternative identifiers
+# Therefore mapping to current HGNC symbols would be beneficial for standardization
+requires_gene_mapping = True
+# First inspect if there's a platform section in the SOFT file
+with gzip.open(soft_file_path, 'rt') as f:
+    # Search for lines containing platform annotation
+    for line in f:
+        if line.startswith('^PLATFORM'):
+            print("Found platform section:")
+            # Print next 20 lines to understand the structure
+            print('\n'.join([next(f).strip() for _ in range(20)]))
+            break
+# Then extract gene annotation using the library function
+gene_metadata = get_gene_annotation(soft_file_path)
+print("\nGene annotation data shape:", gene_metadata.shape)
+print("\nColumns:", gene_metadata.columns.tolist())
+print("\nPreview:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# Since annotation mapping failed, directly normalize the gene symbols
+gene_data = normalize_gene_symbols_in_index(genetic_data)
+# Print preview of gene data
+print("Gene data shape:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10].tolist())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Final validation and save metadata
+note = "This dataset contains gene expression data from blood monocyte subsets comparing renal cell carcinoma patients with healthy donors."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE117230.py ADDED Viewed

	@@ -0,0 +1,170 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE117230"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE117230"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE117230.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE117230.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE117230.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains transcriptional profiling data per background info
+is_gene_available = True
+# 2. Variable Analysis
+# 2.1 Data Availability
+# Trait: disease state from row 0 distinguishes ccRCC patients vs healthy controls
+trait_row = 0
+# Age is not available in sample characteristics
+age_row = None
+# Gender is not available in sample characteristics
+gender_row = None
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert disease state to binary: 0 for healthy control, 1 for ccRCC"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].lower()
+    if 'ccrcc patient' in value:
+        return 1
+    elif 'healthy control' in value:
+        return 0
+    return None
+def convert_age(value: str) -> float:
+    """Convert age to float"""
+    return None  # Not used since age not available
+def convert_gender(value: str) -> int:
+    """Convert gender to binary"""
+    return None  # Not used since gender not available
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+# 4. Extract Clinical Features
+# Since trait_row is not None, we need to extract clinical features
+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 clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# The identifiers appear to be probeset IDs (ending in '_st')
+# rather than standard human gene symbols like 'BRCA1', 'TP53', etc.
+# These will need to be mapped to official gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# Find probe IDs and gene symbols in annotation data
+# The gene expression data uses probeset_id format, which matches the 'ID' column in annotations
+# Gene symbols are in gene_assignment column with format "RefSeq // Gene Symbol // Description"
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
+# Convert the gene assignment strings to gene symbols
+def extract_gene(assignment):
+    if pd.isna(assignment):
+        return []
+    # Split by gene name separator '//' and look for entries that appear to be gene symbols
+    genes = []
+    parts = assignment.split('//')
+    for part in parts:
+        genes.extend(extract_human_gene_symbols(part))
+    return genes
+# Get the gene mapping and apply it to convert probe expression to gene expression
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+# Print preview of the gene data
+print("Preview of mapped gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Final validation and save metadata
+note = "Dataset from gene expression microarray profiling of proximal tubule cells from African American individuals, comparing samples with different APOL1 genotypes."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE119958.py ADDED Viewed

	@@ -0,0 +1,161 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE119958"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE119958"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE119958.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE119958.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE119958.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix HTA 2.0 arrays for mRNA expression
+# 2. Variable Availability and Row IDs
+trait_row = 1  # APOL1 genotype information in row 1
+# Age and gender info not available in sample characteristics
+age_row = None
+gender_row = None
+# 2.2 Conversion Functions
+def convert_trait(value):
+    """
+    Convert APOL1 genotype to binary:
+    0: Low risk (G0G0)
+    1: High risk (G1G0, G2G0, G1G1, G1G2)
+    """
+    if not value or ':' not in value:
+        return None
+    genotype = value.split(': ')[1].strip()
+    if 'G0G0' in genotype:
+        return 0
+    elif any(g in genotype for g in ['G1G0', 'G2G0', 'G1G1', 'G1G2']):
+        return 1
+    return None
+def convert_age(value):
+    return None  # Not available
+def convert_gender(value):
+    return None  # Not available
+# 3. Save metadata for initial filtering
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+# 4. Extract clinical features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", preview)
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# Looking at gene identifiers like 'TC01000001.hg.1', these are probe IDs from Affymetrix array
+# and not standard human gene symbols. They need to be mapped.
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# Identify ID and gene columns for mapping
+# ID column contains same identifiers (e.g. TC01000001.hg.1) as gene expression data
+# gene_assignment column contains gene symbols and details
+# Process mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
+# Apply mapping to convert probe measurements to gene expression
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# Save genetic data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Final validation and save metadata
+note = "Dataset from gene expression microarray profiling of proximal tubule cells from African American individuals, comparing samples with different APOL1 genotypes."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE127136.py ADDED Viewed

	@@ -0,0 +1,97 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE127136"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE127136"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE127136.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE127136.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE127136.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# This is single-cell RNA-seq data, not miRNA or methylation data
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# For trait (cancer vs normal), we can use disease state field (row 1)
+trait_row = 1
+# Age and gender not available in sample characteristics
+age_row = None
+gender_row = None
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if ':' in str(x):
+        value = str(x).split(':')[1].strip().lower()
+        if 'kidney cancer' in value:
+            return 1  # cancer samples
+        elif 'normal' in value:
+            return 0  # normal samples (paracancerous tissues)
+    return None
+def convert_age(x):
+    return None
+def convert_gender(x):
+    return None
+# 3. Save Metadata
+# Validate and save cohort info (initial filtering)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the processed clinical data
+    print("Preview of processed clinical data:")
+    print(preview_df(selected_clinical))
+    # Save clinical data
+    selected_clinical.to_csv(out_clinical_data_file)
+# Since we've discovered the gene expression data is in a different database,
+# we need to abort the gene data extraction attempt
+print("Gene expression data for this series is stored in the Genome Sequence Archive for Human database")
+print("(https://bigd.big.ac.cn/gsa-human/) under accession PRJCA003506")
+print("The data is not available in GEO matrix format required by our pipeline")
+# Set genetic data to empty dataframe to indicate extraction failed
+genetic_data = pd.DataFrame()

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE131027.py ADDED Viewed

	@@ -0,0 +1,162 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE131027"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE131027"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE131027.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE131027.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Since we have gene mutation data but no explicit gene expression matrix shown,
+# this dataset likely contains pure mutation data rather than expression data
+is_gene_available = False
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Cancer type is recorded in row 1, we can use it to identify kidney cancer cases
+trait_row = 1
+# Age and gender are not recorded in the sample characteristics
+age_row = None
+gender_row = None
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Extract value after colon
+    if ':' in str(x):
+        value = str(x).split(':')[1].strip().lower()
+        # Check if it's kidney cancer
+        if 'renal cell carcinoma' in value:
+            return 1
+        else:
+            return 0
+    return None
+def convert_age(x):
+    return None
+def convert_gender(x):
+    return None
+# 3. Initial Filtering and Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    # Extract clinical features
+    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 processed data
+    preview = preview_df(clinical_df)
+    print("Preview of clinical data:")
+    print(preview)
+    # Save to CSV
+    clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These are probe IDs from the Affymetrix human microarray platform
+# They need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# 1. Looking at gene annotations, 'ID' matches probe IDs in expression data, and 'Gene Symbol' has corresponding gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+# 2. Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
+# 3. Apply gene mapping to convert probe expression to gene expression
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased = judge_binary_variable_biased(linked_data, trait)
+    if "Age" in linked_data.columns:
+        if judge_continuous_variable_biased(linked_data, "Age"):
+            linked_data = linked_data.drop(columns="Age")
+    if "Gender" in linked_data.columns:
+        if judge_binary_variable_biased(linked_data, "Gender"):
+            linked_data = linked_data.drop(columns="Gender")
+# 5. Final validation and save metadata
+note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE150404.py ADDED Viewed

	@@ -0,0 +1,168 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE150404"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE150404"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE150404.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE150404.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE150404.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info mentioning "Expression data" and "microarrays", this dataset contains gene expression data
+is_gene_available = True
+# 2.1 Data Availability
+# trait (cancer stage) is in row 1
+# gender is in row 2
+# age is in row 3
+trait_row = 1
+gender_row = 2
+age_row = 3
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> int:
+    """Convert cancer stage to binary: early (0) vs late (1) stage"""
+    if not isinstance(x, str):
+        return None
+    x = x.split(': ')[-1].lower()
+    if x in ['first', 'second']:
+        return 0  # Early stage
+    elif x in ['third', 'fourth']:
+        return 1  # Late stage
+    return None
+def convert_age(x: str) -> float:
+    """Convert age string to float"""
+    if not isinstance(x, str):
+        return None
+    x = x.split(': ')[-1]
+    try:
+        return float(x)
+    except:
+        return None
+def convert_gender(x: str) -> int:
+    """Convert gender to binary: female (0) vs male (1)"""
+    if not isinstance(x, str):
+        return None
+    x = x.split(': ')[-1].lower()
+    if x == 'female':
+        return 0
+    elif x == 'male':
+        return 1
+    return None
+# 3. Save metadata through initial filtering
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# 4. Extract clinical features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+# Preview and save clinical data
+print("Preview of extracted clinical features:")
+print(preview_df(selected_clinical))
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# The gene identifiers are numeric codes (16650001, etc) rather than standard human gene symbols
+# These appear to be probe IDs that need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# 'ID' column in gene_metadata matches the gene identifiers in genetic_data
+# 'gene_assignment' column contains gene symbols
+# Get mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
+# Apply the mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Final validation and save metadata
+note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE245862.py ADDED Viewed

	@@ -0,0 +1,173 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE245862"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE245862"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE245862.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE245862.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE245862.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene expression data availability
+# Yes, this is a microarray study of gene expression data
+is_gene_available = True
+# 2.1 Data availability
+# Looking at sample characteristics - key 0 contains phenotype data that can be used for trait classification
+trait_row = 0
+# Age and gender information not available in sample characteristics
+age_row = None
+gender_row = None
+# 2.2 Data type conversion functions
+def convert_trait(value):
+    """Convert phenotype to binary - normal (0) vs modified STAT3 (1)"""
+    if not isinstance(value, str):
+        return None
+    if ':' in value:
+        value = value.split(':', 1)[1].strip()
+    if "Normal" in value:
+        return 0
+    elif value:  # Any modified STAT3 phenotype
+        return 1
+    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 since trait data is available
+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 extracted features
+print(preview_df(clinical_df))
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# The IDs appear to be probe IDs or accession numbers, not gene symbols
+# They are numerical identifiers, whereas gene symbols are typically alphanumeric like 'BRCA1', 'TP53' etc.
+# Therefore mapping to gene symbols will be required
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# Extract gene symbols from annotations and create mapping
+def extract_hgnc_symbols(text):
+    """Extract HGNC gene symbols from annotation text"""
+    if not isinstance(text, str):
+        return []
+    pattern = r"HGNC Symbol;Acc:HGNC:\d+\] // ([A-Z][A-Z0-9-]+)"
+    matches = re.findall(pattern, text)
+    return list(set(matches))
+mapping_df = gene_metadata[['ID', 'SPOT_ID.1']].copy()
+mapping_df['Gene'] = mapping_df['SPOT_ID.1'].apply(extract_hgnc_symbols)
+mapping_df = mapping_df[['ID', 'Gene']]
+# Convert probe measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# Normalize gene symbols to their latest official symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+# Preview processed gene data
+print("Preview of gene data after mapping:")
+print(preview_df(gene_data))
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Select clinical features
+clinical_features = geo_select_clinical_features(
+    clinical_data,  # Use clinical_data from previous steps
+    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
+)
+# 1. Gene data already normalized in previous step
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# 4. Judge whether features are biased and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Final validation and save metadata
+note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE94321.py ADDED Viewed

	@@ -0,0 +1,162 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE94321"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE94321"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE94321.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE94321.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE94321.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info "[human mRNA]", this dataset contains gene expression data
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# From sample characteristics, row 0 contains tissue info which can indicate trait
+trait_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(value: str) -> int:
+    """Convert trait values to binary (0: control, 1: case)"""
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(': ')[-1]
+    # RMC (Renal Medullary Carcinoma) is a type of kidney cancer
+    if value == 'rmc':
+        return 1
+    # Other tissue types are not kidney cancer
+    elif value in ['rt', 'es', 'uc']:
+        return 0
+    return None
+def convert_age(value: str) -> Optional[float]:
+    return None
+def convert_gender(value: str) -> Optional[int]:
+    return 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:
+    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 clinical data
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These identifiers appear to be probe/sequence IDs from a microarray platform
+# The '_at' suffix is characteristic of Affymetrix probe IDs
+# They need to be mapped to official human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# Extract mapping information from annotation data
+# The 'ID' column matches the gene identifiers in expression data (microarray probe IDs)
+# The Description column contains gene names that can be mapped to gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Description')
+# Convert probe-level data to gene expression data by mapping probe IDs to gene symbols
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# Preview the resulting gene expression data
+print("Gene expression data shape:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(list(gene_data.index[:5]))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Final validation and save metadata
+note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE95425.py ADDED Viewed

	@@ -0,0 +1,160 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+cohort = "GSE95425"
+# Input paths
+in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
+in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE95425"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE95425.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE95425.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE95425.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Get unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - the study looks at transcriptomes which means gene expression data
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# For trait: Can infer from sampling depth (row 2)
+trait_row = 2
+# No age or gender data available
+age_row = None
+gender_row = None
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert sampling depth to binary trait (cortex vs medulla).
+    0 = cortex (normal), 1 = medulla"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[1].lower()
+    if value == "cortex":
+        return 0
+    elif value == "medulla":
+        return 1
+    # Mixed category treated as missing
+    return None
+convert_age = None
+convert_gender = 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:")
+    print(preview)
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# The IDs are Illumina probe IDs (e.g., ILMN_1343291) rather than human gene symbols
+# These need to be mapped to standard gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+# Preview the first few rows
+print("\nPreview of the annotation data:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# 1. From observation:
+# - Gene expression data uses ILMN_ IDs like 'ILMN_1343291'
+# - In gene annotation, 'ID' column has the same ILMN_ identifiers
+# - 'Symbol' column contains the gene symbols we want to map to
+# 2. Extract gene mapping data
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Symbol')
+# 3. Convert probe level data to gene expression data using the mapping
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+# Preview first few genes and their expression values
+print("\nPreview of mapped gene expression data:")
+print(json.dumps(preview_df(gene_data), indent=2))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# Early exit if trait values are all NaN
+if linked_data[trait].isna().all():
+    is_biased = True
+    linked_data = None
+else:
+    # 4. Judge whether features are biased and remove biased demographic features
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Final validation and save metadata
+note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/TCGA.py ADDED Viewed

	@@ -0,0 +1,127 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Kidney_Clear_Cell_Carcinoma"
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+# Output paths
+out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/cohort_info.json"
+# Select the relevant directory
+cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)')
+# Get paths to clinical and genetic data files
+clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+# Load the data files
+clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
+# Print clinical data columns for review
+print("Clinical data columns:", clinical_df.columns.tolist())
+# Check data availability
+is_gene_available = len(genetic_df) > 0
+is_trait_available = len(clinical_df) > 0
+# Record data availability
+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)
+# Identify candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+# Get clinical data file path
+clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, trait))
+# Read clinical data
+clinical_df = pd.read_csv(clinical_file_path, index_col=0)
+# Preview age columns
+age_preview = {}
+for col in candidate_age_cols:
+    age_preview[col] = clinical_df[col].head(5).tolist()
+print("Age columns preview:")
+print(age_preview)
+# Preview gender columns
+gender_preview = {}
+for col in candidate_gender_cols:
+    gender_preview[col] = clinical_df[col].head(5).tolist()
+print("\nGender columns preview:")
+print(gender_preview)
+# Select the relevant directory
+cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)')
+# Get paths to clinical and genetic data files
+clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+# Load the data files
+clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
+# Print clinical data columns for review
+print("Clinical data columns:", clinical_df.columns.tolist())
+# Check data availability
+is_gene_available = len(genetic_df) > 0
+is_trait_available = len(clinical_df) > 0
+# Record data availability
+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)
+# From the clinical columns, identify suitable columns for age and gender
+age_col = "age_at_initial_pathologic_diagnosis"  # Most relevant and direct age column
+gender_col = "gender"  # Direct gender information column
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# Select demographic columns
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+# Extract and standardize clinical features
+selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Normalize gene symbols and save
+normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_genetic_df.to_csv(out_gene_data_file)
+# Link clinical and genetic data
+linked_data = pd.concat([selected_clinical_df, normalized_genetic_df.T], axis=1)
+# Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# Judge whether features are biased and remove biased demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# Final validation and save cohort info
+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=trait_biased,
+    df=linked_data,
+    note="TCGA kidney clear cell carcinoma data comparing tumor vs normal tissue samples"
+)
+# Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Kidney_Clear_Cell_Carcinoma/cohort_info.json ADDED Viewed

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+ {"GSE95425": {"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": 47, "note": "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."}, "GSE94321": {"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": 32, "note": "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."}, "GSE245862": {"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": "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."}, "GSE150404": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 60, "note": "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."}, "GSE131027": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 92, "note": "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."}, "GSE119958": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 100, "note": "Dataset from gene expression microarray profiling of proximal tubule cells from African American individuals, comparing samples with different APOL1 genotypes."}, "GSE117230": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 40, "note": "Dataset from gene expression microarray profiling of proximal tubule cells from African American individuals, comparing samples with different APOL1 genotypes."}, "GSE106757": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 36, "note": "This dataset contains gene expression data from blood monocyte subsets comparing renal cell carcinoma patients with healthy donors."}, "GSE102807": {"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}, "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": "TCGA kidney clear cell carcinoma data comparing tumor vs normal tissue samples"}}

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+size 11964637

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+size 31117591

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+size 24379939

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+version https://git-lfs.github.com/spec/v1
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+size 16759412

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+size 182067513

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