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{
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{
"cell_type": "code",
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"id": "b1e2e671",
"metadata": {
"execution": {
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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 = \"Bladder_Cancer\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Bladder_Cancer/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Bladder_Cancer/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Bladder_Cancer/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Bladder_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "78da5baf",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "be5637fa",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected directory: TCGA_Bladder_Cancer_(BLCA)\n",
"Clinical file: TCGA.BLCA.sampleMap_BLCA_clinicalMatrix\n",
"Genetic file: TCGA.BLCA.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Clinical data columns:\n",
"['_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_BLCA', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mirna_BLCA', '_PANCAN_mutation_PANCAN', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'additional_surgery_locoregional_procedure', 'additional_treatment_completion_success_outcome', 'age_at_initial_pathologic_diagnosis', 'age_began_smoking_in_years', 'anatomic_neoplasm_subdivision', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'bladder_carcinoma_extracapsular_extension_status', 'cancer_diagnosis_cancer_type_icd9_text_name', 'chemical_exposure_text', 'clinical_T', 'complete_response_observed', 'days_to_additional_surgery_metastatic_procedure', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'diagnosis_subtype', 'disease_code', 'disease_extracapsular_extension_ind_3', 'eastern_cancer_oncology_group', 'family_member_relationship_type', 'followup_case_report_form_submission_reason', 'form_completion_date', 'gender', 'height', 'hist_of_non_mibc', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'induction_course_complete', 'informed_consent_verified', 'init_pathology_dx_method_other', 'initial_pathologic_diagnosis_method', 'initial_weight', 'is_ffpe', 'karnofsky_performance_score', 'lost_follow_up', 'lymph_node_examined_count', 'lymphovascular_invasion_present', 'maint_therapy_course_complete', 'metastatic_site', 'mibc_90day_post_resection_bcg', 'neoplasm_histologic_grade', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'non_mibc_tx', 'number_of_lymphnodes_positive_by_he', 'number_pack_years_smoked', 'occupation_primary_job', 'oct_embedded', 'other_dx', 'other_metastatic_site', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'person_concomitant_prostate_carcinoma_occurrence_indicator', 'person_concomitant_prostate_carcinoma_pathologic_t_stage', 'person_neoplasm_cancer_status', 'person_occupation_description_text', 'person_occupation_years_number', 'person_primary_industry_text', 'postoperative_rx_tx', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'project_code', 'radiation_therapy', 'resp_maint_from_bcg_admin_month_dur', 'sample_type', 'sample_type_id', 'stopped_smoking_year', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_BLCA_RPPA', '_GENOMIC_ID_TCGA_BLCA_mutation_curated_broad_gene', '_GENOMIC_ID_TCGA_BLCA_mutation', '_GENOMIC_ID_TCGA_BLCA_gistic2thd', '_GENOMIC_ID_TCGA_BLCA_RPPA_RBN', '_GENOMIC_ID_data/public/TCGA/BLCA/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_BLCA_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_BLCA_PDMRNAseq', '_GENOMIC_ID_TCGA_BLCA_mutation_broad_gene', '_GENOMIC_ID_data/public/TCGA/BLCA/miRNA_GA_gene', '_GENOMIC_ID_TCGA_BLCA_exp_HiSeqV2', '_GENOMIC_ID_TCGA_BLCA_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_BLCA_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_BLCA_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_BLCA_miRNA_HiSeq', '_GENOMIC_ID_TCGA_BLCA_miRNA_GA', '_GENOMIC_ID_TCGA_BLCA_hMethyl450', '_GENOMIC_ID_TCGA_BLCA_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_BLCA_gistic2']\n",
"\n",
"Clinical data shape: (436, 129)\n",
"Genetic data shape: (20530, 426)\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"# 1. Find the most relevant directory for Bladder Cancer\n",
"subdirectories = os.listdir(tcga_root_dir)\n",
"target_trait = trait.lower().replace(\"_\", \" \") # Convert to lowercase for case-insensitive matching\n",
"\n",
"# Search for exact matches or synonyms\n",
"matched_dir = None\n",
"for subdir in subdirectories:\n",
" if \"bladder\" in subdir.lower() and \"cancer\" in subdir.lower():\n",
" matched_dir = subdir\n",
" break\n",
"\n",
"if not matched_dir:\n",
" print(f\"No suitable directory found for {trait}.\")\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",
" exit()\n",
"\n",
"print(f\"Selected directory: {matched_dir}\")\n",
"\n",
"# 2. Get the clinical and genetic data file paths\n",
"cohort_dir = os.path.join(tcga_root_dir, matched_dir)\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"print(f\"Clinical file: {os.path.basename(clinical_file_path)}\")\n",
"print(f\"Genetic file: {os.path.basename(genetic_file_path)}\")\n",
"\n",
"# 3. Load the data files\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
"\n",
"# 4. Print clinical data columns for inspection\n",
"print(\"\\nClinical data columns:\")\n",
"print(clinical_df.columns.tolist())\n",
"\n",
"# Print basic information about the datasets\n",
"print(f\"\\nClinical data shape: {clinical_df.shape}\")\n",
"print(f\"Genetic data shape: {genetic_df.shape}\")\n"
]
},
{
"cell_type": "markdown",
"id": "1760f433",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7abdc56c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:58:57.588849Z",
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"shell.execute_reply": "2025-03-25T06:58:57.599330Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [63, 66, 69, 59, 83], 'age_began_smoking_in_years': [20.0, 15.0, nan, nan, 30.0], 'days_to_birth': [-23323.0, -24428.0, -25259.0, -21848.0, -30520.0]}\n",
"\n",
"Gender columns preview:\n",
"{'gender': ['MALE', 'MALE', 'MALE', 'FEMALE', 'MALE']}\n"
]
}
],
"source": [
"# Find candidate age columns\n",
"candidate_age_cols = [\n",
" 'age_at_initial_pathologic_diagnosis',\n",
" 'age_began_smoking_in_years',\n",
" 'days_to_birth' # This is often used to calculate age\n",
"]\n",
"\n",
"# Find candidate gender columns\n",
"candidate_gender_cols = [\n",
" 'gender'\n",
"]\n",
"\n",
"# Extract the candidate columns from clinical data\n",
"# First, we need to load the clinical data\n",
"cohort_dir = os.path.join(tcga_root_dir, \"TCGA_Bladder_Cancer_(BLCA)\")\n",
"clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)\n",
"clinical_df = pd.read_table(clinical_file_path, index_col=0)\n",
"\n",
"# Extract and preview age columns\n",
"age_data = clinical_df[candidate_age_cols]\n",
"age_preview = preview_df(age_data, n=5)\n",
"print(\"Age columns preview:\")\n",
"print(age_preview)\n",
"\n",
"# Extract and preview gender columns\n",
"gender_data = clinical_df[candidate_gender_cols]\n",
"gender_preview = preview_df(gender_data, n=5)\n",
"print(\"\\nGender columns preview:\")\n",
"print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "7b4b5039",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8fc76097",
"metadata": {
"execution": {
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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": [
"# Step 1: Select appropriate columns for age and gender\n",
"\n",
"# For age, we have three candidate columns:\n",
"# - 'age_at_initial_pathologic_diagnosis': Contains direct age values\n",
"# - 'age_began_smoking_in_years': Contains smoking initiation age (many NaN values)\n",
"# - 'days_to_birth': Contains negative values representing days before birth (essentially age in days)\n",
"\n",
"# Choose the most appropriate column for age\n",
"age_col = 'age_at_initial_pathologic_diagnosis' # This column has clear, direct age values without NaNs\n",
"\n",
"# For gender, we only have one candidate column:\n",
"# - 'gender': Contains 'MALE' and 'FEMALE' values\n",
"gender_col = 'gender' # This is the only column and contains valid gender information\n",
"\n",
"# Step 2: Print the chosen columns\n",
"print(f\"Selected age column: {age_col}\")\n",
"print(f\"Selected gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ddc496fa",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ed48391f",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Bladder_Cancer/gene_data/TCGA.csv\n",
"Gene expression data shape after normalization: (19848, 426)\n",
"Clinical data saved to ../../output/preprocess/Bladder_Cancer/clinical_data/TCGA.csv\n",
"Clinical data shape: (436, 3)\n",
"Number of samples in clinical data: 436\n",
"Number of samples in genetic data: 426\n",
"Number of common samples: 426\n",
"Linked data shape: (426, 19851)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (426, 19851)\n",
"For the feature 'Bladder_Cancer', the least common label is '0' with 19 occurrences. This represents 4.46% of the dataset.\n",
"The distribution of the feature 'Bladder_Cancer' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 60.0\n",
" 50% (Median): 69.0\n",
" 75%: 76.0\n",
"Min: 34\n",
"Max: 90\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '0' with 115 occurrences. This represents 27.00% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Bladder_Cancer/TCGA.csv\n",
"Preprocessing completed.\n"
]
}
],
"source": [
"# Step 1: Extract and standardize clinical features\n",
"# Create clinical features dataframe with trait (bladder cancer) using patient IDs\n",
"clinical_features = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=\"Bladder_Cancer\", \n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\n",
"\n",
"# Step 2: Normalize gene symbols in the gene expression data\n",
"# The gene symbols in TCGA genetic data are already standardized, but we'll normalize them for consistency\n",
"normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_df.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
"print(f\"Gene expression data shape after normalization: {normalized_gene_df.shape}\")\n",
"\n",
"# Step 3: Link clinical and genetic data\n",
"# Transpose genetic data to have samples as rows and genes as columns\n",
"genetic_df_t = normalized_gene_df.T\n",
"# Save the clinical data for reference\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",
"print(f\"Clinical data shape: {clinical_features.shape}\")\n",
"\n",
"# Verify common indices between clinical and genetic data\n",
"clinical_indices = set(clinical_features.index)\n",
"genetic_indices = set(genetic_df_t.index)\n",
"common_indices = clinical_indices.intersection(genetic_indices)\n",
"print(f\"Number of samples in clinical data: {len(clinical_indices)}\")\n",
"print(f\"Number of samples in genetic data: {len(genetic_indices)}\")\n",
"print(f\"Number of common samples: {len(common_indices)}\")\n",
"\n",
"# Link the data by using the common indices\n",
"linked_data = pd.concat([clinical_features.loc[list(common_indices)], genetic_df_t.loc[list(common_indices)]], axis=1)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 4: Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait_col=\"Bladder_Cancer\")\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# Step 5: Determine whether the trait and demographic features are severely biased\n",
"trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=\"Bladder_Cancer\")\n",
"\n",
"# Step 6: Conduct final quality validation and save information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=trait_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains TCGA bladder cancer samples with gene expression and clinical information.\"\n",
")\n",
"\n",
"# Step 7: Save linked data if usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset deemed not usable based on validation criteria. Data not saved.\")\n",
"\n",
"print(\"Preprocessing completed.\")"
]
}
],
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