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p1/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE42133.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE42133"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE42133"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Autism_spectrum_disorder_(ASD)/GSE42133.csv"
+out_gene_data_file = "./output/preprocess/1/Autism_spectrum_disorder_(ASD)/gene_data/GSE42133.csv"
+out_clinical_data_file = "./output/preprocess/1/Autism_spectrum_disorder_(ASD)/clinical_data/GSE42133.csv"
+json_path = "./output/preprocess/1/Autism_spectrum_disorder_(ASD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info describing leukocyte gene expression
+
+# 2. Variable Availability
+# From the sample characteristics dictionary:
+#   0 -> ['dx (diagnosis): ASD', 'dx (diagnosis): Control']
+#   1 -> ['gender: male']
+#   2 -> ['cell type: leukocyte']
+
+# Trait information is in key=0 with two unique values (ASD vs Control), so it's available.
+trait_row = 0
+
+# There is no mention of an age variable, so it's unavailable.
+age_row = None
+
+# Gender has only one unique value "male", so it is a constant feature and thus considered not available.
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str):
+    """
+    Convert the trait (diagnosis) to binary.
+    ASD -> 1
+    Control -> 0
+    Else -> None
+    """
+    # Extract substring after the colon
+    parts = value.split(':', 1)
+    val = parts[-1].strip().lower()
+    if val == 'asd':
+        return 1
+    elif val == 'control':
+        return 0
+    else:
+        return None
+
+def convert_age(value: str):
+    """
+    Age data is not available, so return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Convert gender to binary.
+    female -> 0
+    male -> 1
+    Else -> None
+    (Not used here because gender is unavailable.)
+    """
+    parts = value.split(':', 1)
+    val = parts[-1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    else:
+        return None
+
+# 3. Initial Filtering and Save 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. Clinical Feature Extraction if trait is available
+if is_trait_available:
+    selected_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
+    )
+
+    # Observe the extracted clinical DataFrame
+    preview_result = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview_result)
+
+    # Save the clinical features
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These "ILMN_xxxxxxx" are Illumina probe IDs, not standard human gene symbols.
+# Hence, they require mapping to gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the relevant columns in the annotation dataframe:
+#    - The gene expression dataset has identifiers like "ILMN_xxxxxx" in its index, which matches the "ID" column.
+#    - The gene symbol information appears to be in the "Symbol" column.
+
+# 2. Extract the mapping between "ID" and "Symbol" from 'gene_annotation'.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3. Apply the mapping to convert probe-level data to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) A quick check to see the dimension and a preview of the newly mapped gene_data.
+print("Gene data shape after mapping:", gene_data.shape)
+print("Gene data head after mapping:")
+print(gene_data.head())
+# STEP7
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+# Replace the undefined 'clinical_features' with the 'selected_clinical_df' from previous steps
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

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p1/preprocess/Autoinflammatory_Disorders/code/GSE43553.py

@@ -0,0 +1,151 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autoinflammatory_Disorders"
+cohort = "GSE43553"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders"
+in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE43553"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/GSE43553.csv"
+out_gene_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/gene_data/GSE43553.csv"
+out_clinical_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/clinical_data/GSE43553.csv"
+json_path = "./output/preprocess/1/Autoinflammatory_Disorders/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+import numpy as np
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on microarray-based gene expression profiling in the background info
+
+# 2. Variable Availability and Data Type Conversion
+#    Examining the sample characteristics dictionary, we see "disease state: CAPS" and 
+#    "disease state: other autoinflammatory disease" in key=3, which vary across samples 
+#    (not a constant feature). Hence, we'll use key=3 for our trait. 
+trait_row = 3
+age_row = None    # No age information found
+gender_row = None # No gender information found
+
+# Define the conversion functions
+def convert_trait(value: str) -> int:
+    if not isinstance(value, str) or pd.isna(value):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if 'caps' in val or 'other autoinflammatory disease' in val:
+        return 1
+    # Otherwise, assume 0 (e.g., healthy or not the target condition)
+    return 0
+
+convert_age = None
+convert_gender = None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Proceed only if trait_row is not None.
+if is_trait_available:
+    selected_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 resulting DataFrame
+    print(preview_df(selected_clinical_df, n=5))
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Identify the columns in the gene annotation that match the probe IDs in the gene expression data ("ID")
+#    and the column that stores the gene symbols ("Gene Symbol").
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Extract a gene mapping dataframe with the probe column and the gene symbol column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert probe-level measurements to gene expression data by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Preview a few rows of the mapped gene expression data
+print("Preview of gene_data after mapping:")
+print(gene_data.head(5))
+# STEP7
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the actual trait name
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Autoinflammatory_Disorders/code/GSE80060.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autoinflammatory_Disorders"
+cohort = "GSE80060"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders"
+in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE80060"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/GSE80060.csv"
+out_gene_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/gene_data/GSE80060.csv"
+out_clinical_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/clinical_data/GSE80060.csv"
+json_path = "./output/preprocess/1/Autoinflammatory_Disorders/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine whether this dataset likely contains gene expression data
+is_gene_available = True  # Based on the title "Gene expression data of whole blood..."
+
+# Step 2.1: Identify data availability for trait, age, and gender
+trait_row = 1   # "disease status: SJIA" vs "disease status: Healthy"
+age_row = None  # No age info found
+gender_row = None  # No gender info found
+
+# Step 2.2: Define data type conversions
+def convert_trait(value: str):
+    # Extract the substring after the colon
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Map SJIA -> 1, Healthy -> 0
+    if val == 'sjia':
+        return 1
+    elif val == 'healthy':
+        return 0
+    else:
+        return None
+
+def convert_age(value: str):
+    # No age data; return None
+    return None
+
+def convert_gender(value: str):
+    # No gender data; return None
+    return None
+
+# Step 3: Conduct initial filtering and save 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
+)
+
+# Step 4: Clinical feature extraction if trait_row is not None
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait='Disease 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
+    )
+    # Preview the selected clinical data
+    preview = preview_df(selected_clinical_df)
+    print("Selected Clinical Data Preview:", preview)
+    
+    # Save extracted clinical features
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers appear to be Affymetrix probe IDs rather than standard human gene symbols.
+# Therefore, they require mapping to gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in gene_annotation that correspond to the probe IDs and the gene symbols.
+#    From the preview, the "ID" column matches the probe identifiers in the gene_data index,
+#    and the "Gene Symbol" column contains the gene symbols.
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Get a gene mapping dataframe from the annotation.
+mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)
+
+# 3. Convert probe-level data to gene-level data using the mapping, dividing probe expression among multiple genes.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, print a small preview of the resulting gene expression dataframe.
+print("Preview of Gene Expression Data (first few genes):")
+print(preview_df(gene_data, n=5))
+# STEP7
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically (note the trait column name matches the clinical data's "Disease Status")
+linked_data_processed = handle_missing_values(linked_data, trait_col="Disease Status")
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, "Disease Status")
+
+# 5. Final quality validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Autoinflammatory_Disorders/code/TCGA.py

@@ -0,0 +1,51 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autoinflammatory_Disorders"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Autoinflammatory_Disorders/cohort_info.json"
+
+import os
+import pandas as pd
+
+# Step 1: Check directories in tcga_root_dir for anything relevant to "Autoinflammatory_Disorders"
+search_terms = ["autoinflammatory", "inflam"]
+dir_list = os.listdir(tcga_root_dir)
+matching_dir = None
+
+for d in dir_list:
+    d_lower = d.lower()
+    if any(term in d_lower for term in search_terms):
+        # Found a match, select this directory
+        matching_dir = d
+        break
+
+if matching_dir is None:
+    # No matching directory found. Mark trait as skipped.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # 2. Identify the clinicalMatrix and PANCAN files
+    cohort_dir = os.path.join(tcga_root_dir, matching_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load both 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 the column names of the clinical data
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())

p1/preprocess/Autoinflammatory_Disorders/cohort_info.json

@@ -0,0 +1 @@
+{"GSE80060": {"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": 206, "note": "Dataset processed with GEO pipeline. Checked for missing values and bias."}, "GSE43553": {"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": 66, "note": "Dataset processed with GEO pipeline. Checked for missing values and bias."}, "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}}

p1/preprocess/Autoinflammatory_Disorders/gene_data/GSE43553.csv

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p1/preprocess/Autoinflammatory_Disorders/gene_data/GSE80060.csv

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p1/preprocess/Bile_Duct_Cancer/GSE131027.csv

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p1/preprocess/Bile_Duct_Cancer/TCGA.csv

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p1/preprocess/Bile_Duct_Cancer/clinical_data/GSE107754.csv

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p1/preprocess/Bile_Duct_Cancer/clinical_data/GSE131027.csv

@@ -0,0 +1,2 @@
+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
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p1/preprocess/Bile_Duct_Cancer/clinical_data/TCGA.csv

@@ -0,0 +1,46 @@
+,Bile_Duct_Cancer,Age,Gender
+TCGA-3X-AAV9-01,1,72,1
+TCGA-3X-AAVA-01,1,50,0
+TCGA-3X-AAVB-01,1,70,0
+TCGA-3X-AAVC-01,1,72,0
+TCGA-3X-AAVE-01,1,60,1
+TCGA-4G-AAZO-01,1,71,0
+TCGA-4G-AAZT-01,1,62,1
+TCGA-W5-AA2G-01,1,62,0
+TCGA-W5-AA2H-01,1,70,0
+TCGA-W5-AA2I-01,1,66,1
+TCGA-W5-AA2I-11,0,66,1
+TCGA-W5-AA2O-01,1,57,1
+TCGA-W5-AA2Q-01,1,68,1
+TCGA-W5-AA2Q-11,0,68,1
+TCGA-W5-AA2R-01,1,77,0
+TCGA-W5-AA2R-11,0,77,0
+TCGA-W5-AA2T-01,1,64,0
+TCGA-W5-AA2U-01,1,78,0
+TCGA-W5-AA2U-11,0,78,0
+TCGA-W5-AA2W-01,1,31,0
+TCGA-W5-AA2X-01,1,67,1
+TCGA-W5-AA2X-11,0,67,1
+TCGA-W5-AA2Z-01,1,29,0
+TCGA-W5-AA30-01,1,82,1
+TCGA-W5-AA30-11,0,82,1
+TCGA-W5-AA31-01,1,71,1
+TCGA-W5-AA31-11,0,71,1
+TCGA-W5-AA33-01,1,60,1
+TCGA-W5-AA34-01,1,75,0
+TCGA-W5-AA34-11,0,75,0
+TCGA-W5-AA36-01,1,51,0
+TCGA-W5-AA38-01,1,55,0
+TCGA-W5-AA39-01,1,81,1
+TCGA-W6-AA0S-01,1,46,0
+TCGA-WD-A7RX-01,1,71,0
+TCGA-YR-A95A-01,1,52,1
+TCGA-ZD-A8I3-01,1,73,0
+TCGA-ZH-A8Y1-01,1,74,0
+TCGA-ZH-A8Y2-01,1,59,0
+TCGA-ZH-A8Y4-01,1,58,1
+TCGA-ZH-A8Y5-01,1,69,1
+TCGA-ZH-A8Y6-01,1,41,0
+TCGA-ZH-A8Y8-01,1,73,1
+TCGA-ZU-A8S4-01,1,52,1
+TCGA-ZU-A8S4-11,0,52,1

p1/preprocess/Bile_Duct_Cancer/code/GSE107754.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bile_Duct_Cancer"
+cohort = "GSE107754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer"
+in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE107754"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Bile_Duct_Cancer/GSE107754.csv"
+out_gene_data_file = "./output/preprocess/1/Bile_Duct_Cancer/gene_data/GSE107754.csv"
+out_clinical_data_file = "./output/preprocess/1/Bile_Duct_Cancer/clinical_data/GSE107754.csv"
+json_path = "./output/preprocess/1/Bile_Duct_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+
+# 1) Determine gene expression data availability
+is_gene_available = True  # The summary indicates "Whole human genome gene expression microarrays"
+
+# 2) Variable Availability and Data Type Conversion
+# After reviewing the sample characteristics:
+#   - trait_row (for Bile_Duct_Cancer) is 2, because "tissue: Bile duct cancer" appears among various tissues.
+#   - age_row is None, no age information found.
+#   - gender_row is 0, as "gender: Male" and "gender: Female" appear there.
+
+trait_row = 2
+age_row = None
+gender_row = 0
+
+# Conversion functions
+def convert_trait(x: str):
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Binary conversion: 1 if it's Bile duct cancer, 0 otherwise
+    return 1 if val == 'bile duct cancer' else 0
+
+def convert_age(x: str):
+    # Age data not available, return None
+    return None
+
+def convert_gender(x: str):
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'male':
+        return 1
+    elif val == 'female':
+        return 0
+    return None
+
+# 3) Save Metadata (initial filtering)
+# trait data is available (trait_row is not None) => is_trait_available = True
+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) Clinical Feature Extraction (only do this step if trait_row is not None)
+if trait_row is not None:
+    # Suppose the clinical_data dataframe is already loaded in the environment
+    clinical_features_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 extracted clinical features
+    preview_result = preview_df(clinical_features_df, n=5)
+    print("Clinical Features Preview:", preview_result)
+
+    # Save clinical data
+    clinical_features_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on review, these identifiers (e.g., A_23_P100001) appear to be microarray probe set IDs,
+# not standard human gene symbols, hence gene mapping is required.
+print("\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Identify the columns in the annotation dataframe that match the IDs in the gene expression data
+#    and which store the human gene symbols. In this case, "ID" matches "A_23_P..." probe IDs,
+#    and "GENE_SYMBOL" stores the actual gene symbols.
+
+# 2) Extract the mapping between these columns into a separate dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3) Convert probe-level measurements to gene-level measurements
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional demonstration) Print shape or a small snippet to verify
+print("Mapped gene_data shape:", gene_data.shape)
+print("First few gene symbols in the mapped gene_data index:")
+print(gene_data.index[:10].tolist())
+# STEP7
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+#    Use the correct variable name from previous steps: "clinical_features_df"
+linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the actual trait name
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Bile_Duct_Cancer/code/GSE131027.py

@@ -0,0 +1,162 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bile_Duct_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer"
+in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Bile_Duct_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/preprocess/1/Bile_Duct_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/preprocess/1/Bile_Duct_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/preprocess/1/Bile_Duct_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the series description mentioning "expression features", we assume gene expression.
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics, "Bile duct cancer" appears under key=1 alongside other cancers.
+# Hence, trait data is available and not constant. So:
+trait_row = 1
+
+# Age data is not present in the dictionary.
+age_row = None
+
+# Gender data is also absent in the dictionary.
+gender_row = None
+
+# Define conversion functions
+def convert_trait(value: str):
+    # Split by colon and take the rightmost part
+    val = value.split(':')[-1].strip().lower()
+    # Convert "bile duct cancer" to 1, everything else to 0, unknown to None
+    if val == "bile duct cancer":
+        return 1
+    elif val:
+        return 0
+    return None
+
+def convert_age(value: str):
+    # No data available, just return None
+    return None
+
+def convert_gender(value: str):
+    # No data available, just return None
+    return None
+
+# 3. Save Metadata (initial filtering)
+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. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the extracted features (just for inspection, not stored)
+    print(preview_df(selected_clinical_df, n=5, max_items=200))
+    # Save clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6 - Gene Identifier Mapping
+
+# 1. Identify columns in 'gene_annotation' which correspond to the probe ID and the gene symbol.
+#    From the preview, the 'ID' column matches our gene_data index (e.g., '1007_s_at'),
+#    and the 'Gene Symbol' column stores the gene symbols.
+
+# 2. Get the gene mapping dataframe
+gene_mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="Gene Symbol"
+)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=gene_mapping_df
+)
+
+# Optional: Print shape or index to verify
+print("Gene expression data after mapping:")
+print("Shape:", gene_data.shape)
+print("First 5 genes:\n", gene_data.index[:5])
+# STEP7
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the actual trait name
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Bile_Duct_Cancer/code/TCGA.py

@@ -0,0 +1,119 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bile_Duct_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Bile_Duct_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Bile_Duct_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Bile_Duct_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Bile_Duct_Cancer/cohort_info.json"
+
+import os
+import pandas as pd
+
+# Step 1: Check directories in tcga_root_dir for anything relevant to "Bile_Duct_Cancer"
+search_terms = ["bile_duct", "bileduct", "chol"]
+dir_list = os.listdir(tcga_root_dir)
+matching_dir = None
+
+for d in dir_list:
+    d_lower = d.lower()
+    if any(term in d_lower for term in search_terms):
+        # Found a match, select this directory
+        matching_dir = d
+        break
+
+if matching_dir is None:
+    # No matching directory found. Mark the dataset as skipped.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # 2. Identify the clinicalMatrix and PANCAN files
+    cohort_dir = os.path.join(tcga_root_dir, matching_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load both 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 the column names of the clinical data
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())
+# Identify candidate demographic columns
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+# Extract the columns and preview them
+age_cols_in_data = [col for col in candidate_age_cols if col in clinical_df.columns]
+gender_cols_in_data = [col for col in candidate_gender_cols if col in clinical_df.columns]
+
+if age_cols_in_data:
+    age_preview_df = clinical_df[age_cols_in_data]
+    print("Age Data Preview:", preview_df(age_preview_df, n=5))
+else:
+    print("Age Data Preview:", {})
+
+if gender_cols_in_data:
+    gender_preview_df = clinical_df[gender_cols_in_data]
+    print("Gender Data Preview:", preview_df(gender_preview_df, n=5))
+else:
+    print("Gender Data Preview:", {})
+# Based on inspection of the supplied previews, we select "age_at_initial_pathologic_diagnosis" for age 
+# (as it directly represents age in years) and "gender" for gender.
+
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+print("Chosen Age Column:", age_col)
+print("Chosen Gender Column:", gender_col)
+# 1) Extract and standardize clinical features (trait, age, gender) from the TCGA data
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols in the gene expression data
+genetic_df_normalized = normalize_gene_symbols_in_index(genetic_df)
+genetic_df_normalized.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on sample IDs
+gene_expr_t = genetic_df_normalized.T
+linked_data = selected_clinical_df.join(gene_expr_t, how='inner')
+
+# 4) Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5) Determine whether the trait and some demographic features are severely biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6) Validate and save cohort information
+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="Prostate Cancer data from TCGA."
+)
+
+# 7) If usable, save the final linked data, including clinical and genetic features
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    # Save clinical subset if present
+    clinical_cols = [col for col in [trait, "Age", "Gender"] if col in linked_data.columns]
+    if clinical_cols:
+        linked_data[clinical_cols].to_csv(out_clinical_data_file)

p1/preprocess/Bile_Duct_Cancer/cohort_info.json

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