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{
"cells": [
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"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Autoinflammatory_Disorders\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Autoinflammatory_Disorders/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "56dd88d4",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"id": "0525c11d",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Available TCGA directories: ['TCGA_Liver_Cancer_(LIHC)', 'TCGA_Lower_Grade_Glioma_(LGG)', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Mesothelioma_(MESO)', 'TCGA_Ocular_melanomas_(UVM)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Rectal_Cancer_(READ)', 'TCGA_Sarcoma_(SARC)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Thymoma_(THYM)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Uterine_Carcinosarcoma_(UCS)', '.DS_Store', 'CrawlData.ipynb', 'TCGA_Acute_Myeloid_Leukemia_(LAML)', 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Glioblastoma_(GBM)', 'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)']\n",
"Potential matches for Autoinflammatory_Disorders: ['TCGA_Large_Bcell_Lymphoma_(DLBC)']\n",
"\n",
"Selected directory: TCGA_Large_Bcell_Lymphoma_(DLBC)\n",
"Clinical data file: TCGA.DLBC.sampleMap_DLBC_clinicalMatrix\n",
"Genetic data file: TCGA.DLBC.sampleMap_HiSeqV2_PANCAN.gz\n",
"\n",
"Clinical data columns:\n",
"['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'age_at_initial_pathologic_diagnosis', 'b_lymphocyte_genotyping_method', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'bone_marrow_biopsy_done', 'bone_marrow_involvement', 'bone_marrow_sample_histology', 'clinical_stage', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_after_initial_treatment', 'eastern_cancer_oncology_group', 'ebv_positive_malignant_cells_percent', 'ebv_status_malignant_cells_method', 'epstein_barr_viral_status', 'extranodal_involvement', 'extranodal_involvment_site_other', 'extranodal_sites_involvement_number', 'first_progression_histology_type', 'first_progression_histology_type_other', 'first_recurrence_biopsy_confirmed', 'follicular_percent', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'gender', 'genetic_abnormality_method_other', 'genetic_abnormality_results', 'genetic_abnormality_results_other', 'genetic_abnormality_tested', 'genetic_abnormality_tested_other', 'height', 'histological_type', 'history_immunological_disease', 'history_immunological_disease_other', 'history_immunosuppresive_rx', 'history_immunosuppressive_rx_other', 'history_of_neoadjuvant_treatment', 'history_relevant_infectious_dx', 'hiv_status', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'igh_genotype_results', 'immunophenotypic_analysis_method', 'immunophenotypic_analysis_results', 'immunophenotypic_analysis_tested', 'informed_consent_verified', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'ldh_level', 'ldh_norm_range_upper', 'longest_dimension', 'lost_follow_up', 'lymph_node_involvement_site', 'maximum_tumor_bulk_anatomic_site', 'maximum_tumor_dimension', 'mib1_positive_percentage_range', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_after_initial_treatment', 'oct_embedded', 'other_dx', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'pet_scan_results', 'primary_therapy_outcome_success', 'radiation_therapy', 'sample_type', 'sample_type_id', 'shortest_dimension', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_DLBC_exp_HiSeqV2', '_GENOMIC_ID_TCGA_DLBC_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_DLBC_PDMRNAseq', '_GENOMIC_ID_TCGA_DLBC_hMethyl450', '_GENOMIC_ID_TCGA_DLBC_miRNA_HiSeq', '_GENOMIC_ID_TCGA_DLBC_gistic2thd', '_GENOMIC_ID_TCGA_DLBC_PDMRNAseqCNV', '_GENOMIC_ID_data/public/TCGA/DLBC/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_DLBC_gistic2', '_GENOMIC_ID_TCGA_DLBC_mutation_bcm_gene', '_GENOMIC_ID_TCGA_DLBC_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_DLBC_RPPA', '_GENOMIC_ID_TCGA_DLBC_exp_HiSeqV2_PANCAN']\n"
]
}
],
"source": [
"import os\n",
"\n",
"# List all subdirectories in tcga_root_dir\n",
"subdirs = os.listdir(tcga_root_dir)\n",
"print(f\"Available TCGA directories: {subdirs}\")\n",
"\n",
"# Check if there's any appropriate TCGA dataset for Autoinflammatory Disorders\n",
"# Autoinflammatory disorders involve chronic inflammation which could be relevant to:\n",
"# - Lymphomas (immune system cancers)\n",
"# - Cancers with inflammatory components\n",
"\n",
"# Look for potential matches\n",
"potential_matches = []\n",
"immune_related = ['TCGA_Large_Bcell_Lymphoma_(DLBC)'] # Immune system cancers\n",
"\n",
"# Add immune-related cancers to potential matches\n",
"for dir_name in subdirs:\n",
" if dir_name in immune_related:\n",
" potential_matches.append(dir_name)\n",
"\n",
"if potential_matches:\n",
" print(f\"Potential matches for {trait}: {potential_matches}\")\n",
" # Select the most specific match\n",
" selected_dir = potential_matches[0] # First match as default\n",
" cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n",
" \n",
" # Get paths to clinical and genetic files\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" # Load clinical and genetic data\n",
" clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\\t')\n",
" genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\\t')\n",
" \n",
" print(f\"\\nSelected directory: {selected_dir}\")\n",
" print(f\"Clinical data file: {os.path.basename(clinical_file_path)}\")\n",
" print(f\"Genetic data file: {os.path.basename(genetic_file_path)}\")\n",
" \n",
" # Print column names of clinical data\n",
" print(\"\\nClinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n",
" \n",
" # Check if gene expression and trait data are available\n",
" is_gene_available = not genetic_df.empty\n",
" is_trait_available = not clinical_df.empty\n",
" \n",
" # Only validate, don't finalize\n",
" validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
" )\n",
"else:\n",
" print(f\"No suitable directory found for {trait}. While autoinflammatory disorders involve inflammation, TCGA datasets don't directly address these conditions.\")\n",
" \n",
" # Mark this cohort as not usable for Autoinflammatory Disorders research\n",
" validate_and_save_cohort_info(\n",
" is_final=False, \n",
" cohort=\"TCGA\", \n",
" info_path=json_path, \n",
" is_gene_available=False, \n",
" is_trait_available=False,\n",
" )\n"
]
},
{
"cell_type": "markdown",
"id": "e9ac5294",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f0b27d6a",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'days_to_birth': [-27468, -24590, -14723, -27025, -21330]}\n",
"\n",
"Gender columns preview:\n",
"{'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}\n"
]
}
],
"source": [
"# 1. Identify candidate columns for age and gender\n",
"candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n",
"candidate_gender_cols = ['gender']\n",
"\n",
"# 2. Load the clinical data to preview the candidate columns\n",
"clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'TCGA_Large_Bcell_Lymphoma_(DLBC)'))\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Preview age columns\n",
"age_preview = {}\n",
"for col in candidate_age_cols:\n",
" if col in clinical_df.columns:\n",
" age_preview[col] = clinical_df[col].head(5).tolist()\n",
"\n",
"print(\"Age columns preview:\")\n",
"print(age_preview)\n",
"\n",
"# Preview gender columns\n",
"gender_preview = {}\n",
"for col in candidate_gender_cols:\n",
" if col in clinical_df.columns:\n",
" gender_preview[col] = clinical_df[col].head(5).tolist()\n",
"\n",
"print(\"\\nGender columns preview:\")\n",
"print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "09839d68",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5f6f8c33",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:54:15.169917Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected age column: age_at_initial_pathologic_diagnosis\n",
"Selected gender column: gender\n"
]
}
],
"source": [
"# Check the age columns\n",
"age_col = None\n",
"if 'age_at_initial_pathologic_diagnosis' in {'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'days_to_birth': [-27468, -24590, -14723, -27025, -21330]}:\n",
" # This column directly contains age values in years\n",
" age_col = 'age_at_initial_pathologic_diagnosis'\n",
"elif 'days_to_birth' in {'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'days_to_birth': [-27468, -24590, -14723, -27025, -21330]}:\n",
" # This column contains negative days from birth, which can be converted to age\n",
" age_col = 'days_to_birth'\n",
"\n",
"# Check the gender columns\n",
"gender_col = None\n",
"if 'gender' in {'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}:\n",
" gender_col = 'gender'\n",
"\n",
"# Print the selected columns\n",
"print(f\"Selected age column: {age_col}\")\n",
"print(f\"Selected gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "e5d4eae3",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
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"id": "2fbce4da",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Extracting clinical features...\n",
"Clinical features shape: (48, 3)\n",
"Preview of clinical features:\n",
" Autoinflammatory_Disorders Age Gender\n",
"sampleID \n",
"TCGA-FA-8693-01 1 75 1\n",
"TCGA-FA-A4BB-01 1 67 1\n",
"TCGA-FA-A4XK-01 1 40 1\n",
"TCGA-FA-A6HN-01 1 73 1\n",
"TCGA-FA-A6HO-01 1 58 0\n",
"Clinical data saved to ../../output/preprocess/Autoinflammatory_Disorders/clinical_data/TCGA.csv\n",
"\n",
"Normalizing gene expression data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original gene expression data shape: (20530, 48)\n",
"Normalized gene expression data shape: (19848, 48)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Autoinflammatory_Disorders/gene_data/TCGA.csv\n",
"\n",
"Linking clinical and genetic data...\n",
"Number of common samples: 48\n",
"Linked data shape: (48, 19851)\n",
"\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (48, 19851)\n",
"\n",
"Checking for biased features...\n",
"Quartiles for 'Autoinflammatory_Disorders':\n",
" 25%: 1.0\n",
" 50% (Median): 1.0\n",
" 75%: 1.0\n",
"Min: 1\n",
"Max: 1\n",
"The distribution of the feature 'Autoinflammatory_Disorders' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 46.0\n",
" 50% (Median): 57.5\n",
" 75%: 67.0\n",
"Min: 23\n",
"Max: 82\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '1' with 22 occurrences. This represents 45.83% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"\n",
"Validating final data quality...\n",
"\n",
"Data is not usable. Linked data will not be saved.\n"
]
}
],
"source": [
"# Step: Feature Engineering and Validation\n",
"\n",
"# 1. Extract and standardize clinical features\n",
"print(\"\\nExtracting clinical features...\")\n",
"clinical_features = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=trait, \n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\n",
"\n",
"print(f\"Clinical features shape: {clinical_features.shape}\")\n",
"print(f\"Preview of clinical features:\\n{clinical_features.head()}\")\n",
"\n",
"# Save clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 2. Normalize gene symbols in the gene expression data\n",
"print(\"\\nNormalizing gene expression data...\")\n",
"genetic_df_normalized = normalize_gene_symbols_in_index(genetic_df)\n",
"print(f\"Original gene expression data shape: {genetic_df.shape}\")\n",
"print(f\"Normalized gene expression data shape: {genetic_df_normalized.shape}\")\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"genetic_df_normalized.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
"\n",
"# 3. Link the clinical and genetic data on sample IDs\n",
"print(\"\\nLinking clinical and genetic data...\")\n",
"# Transpose genetic data to have samples as rows and genes as columns\n",
"genetic_df_for_linking = genetic_df_normalized.T\n",
"\n",
"# Ensure sample IDs in clinical features match those in genetic data\n",
"common_samples = clinical_features.index.intersection(genetic_df_for_linking.index)\n",
"print(f\"Number of common samples: {len(common_samples)}\")\n",
"\n",
"# Filter both dataframes to keep only common samples\n",
"clinical_features_common = clinical_features.loc[common_samples]\n",
"genetic_df_common = genetic_df_for_linking.loc[common_samples]\n",
"\n",
"# Combine clinical and genetic data\n",
"linked_data = pd.concat([clinical_features_common, genetic_df_common], axis=1)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values systematically\n",
"print(\"\\nHandling missing values...\")\n",
"linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n",
"print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n",
"\n",
"# 5. Determine if trait or demographic features are biased\n",
"print(\"\\nChecking for biased features...\")\n",
"is_trait_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
"\n",
"# 6. Validate data quality and save cohort information\n",
"print(\"\\nValidating final data quality...\")\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=genetic_df_normalized.shape[0] > 0,\n",
" is_trait_available=clinical_features.shape[0] > 0,\n",
" is_biased=is_trait_biased,\n",
" df=linked_data_clean,\n",
" note=\"Pancreatic cancer dataset used as proxy for Type 2 Diabetes due to pancreatic involvement in diabetes.\"\n",
")\n",
"\n",
"# 7. Save the linked data if usable\n",
"if is_usable:\n",
" print(f\"\\nData is usable. Saving linked data to {out_data_file}...\")\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_clean.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"\\nData is not usable. Linked data will not be saved.\")"
]
}
],
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