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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a7767e9f",
"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 = \"Rectal_Cancer\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Rectal_Cancer/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Rectal_Cancer/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Rectal_Cancer/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Rectal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "22a9728b",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5d713370",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found matching directories: ['TCGA_Rectal_Cancer_(READ)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)']\n",
"Selected directory: TCGA_Rectal_Cancer_(READ)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data columns:\n",
"['AWG_MLH1_silencing', 'AWG_cancer_type_Oct62011', 'CDE_ID_3226963', 'CIMP', 'MSI_updated_Oct62011', '_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mirna_READ', '_PANCAN_mutation_PANCAN', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'anatomic_neoplasm_subdivision', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'braf_gene_analysis_performed', 'braf_gene_analysis_result', 'circumferential_resection_margin', 'colon_polyps_present', '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', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'gender', 'height', 'histological_type', 'history_of_colon_polyps', 'history_of_neoadjuvant_treatment', 'hypermutation', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'kras_gene_analysis_performed', 'kras_mutation_codon', 'kras_mutation_found', 'longest_dimension', 'loss_expression_of_mismatch_repair_proteins_by_ihc', 'loss_expression_of_mismatch_repair_proteins_by_ihc_result', 'lost_follow_up', 'lymph_node_examined_count', 'lymphatic_invasion', 'microsatellite_instability', 'new_neoplasm_event_type', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'non_nodal_tumor_deposits', 'non_silent_mutation', 'non_silent_rate_per_Mb', 'number_of_abnormal_loci', 'number_of_first_degree_relatives_with_cancer_diagnosis', 'number_of_loci_tested', 'number_of_lymphnodes_positive_by_he', 'number_of_lymphnodes_positive_by_ihc', 'oct_embedded', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'perineural_invasion_present', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'preoperative_pretreatment_cea_level', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'radiation_therapy', 'residual_disease_post_new_tumor_event_margin_status', 'residual_tumor', 'sample_type', 'sample_type_id', 'shortest_dimension', 'silent_mutation', 'silent_rate_per_Mb', 'site_of_additional_surgery_new_tumor_event_mets', 'synchronous_colon_cancer_present', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'total_mutation', 'tumor_tissue_site', 'venous_invasion', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_READ_RPPA', '_GENOMIC_ID_TCGA_READ_G4502A_07_3', '_GENOMIC_ID_TCGA_READ_exp_HiSeqV2', '_GENOMIC_ID_TCGA_READ_PDMarrayCNV', '_GENOMIC_ID_TCGA_READ_PDMRNAseq', '_GENOMIC_ID_TCGA_READ_miRNA_GA', '_GENOMIC_ID_TCGA_READ_exp_GAV2', '_GENOMIC_ID_TCGA_READ_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_READ_hMethyl27', '_GENOMIC_ID_TCGA_READ_miRNA_HiSeq', '_GENOMIC_ID_TCGA_READ_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_READ_mutation_bcm_gene', '_GENOMIC_ID_TCGA_READ_hMethyl450', '_GENOMIC_ID_TCGA_READ_gistic2', '_GENOMIC_ID_TCGA_READ_RPPA_RBN', '_GENOMIC_ID_data/public/TCGA/READ/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_READ_PDMarray', '_GENOMIC_ID_data/public/TCGA/READ/miRNA_GA_gene', '_GENOMIC_ID_TCGA_READ_gistic2thd', '_GENOMIC_ID_TCGA_READ_exp_GAV2_exon', '_GENOMIC_ID_TCGA_READ_mutation_bcm_solid_gene', '_GENOMIC_ID_TCGA_READ_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_READ_exp_HiSeqV2_exon']\n"
]
}
],
"source": [
"# Step 1: Search for directories related to Rectal Cancer\n",
"import os\n",
"\n",
"# List all directories in TCGA root directory\n",
"tcga_dirs = os.listdir(tcga_root_dir)\n",
"\n",
"# Look for directories related to Rectal Cancer\n",
"matching_dirs = [dir_name for dir_name in tcga_dirs \n",
" if any(term in dir_name.lower() for term in \n",
" [\"rectal\", \"read\", \"coadread\"])]\n",
"\n",
"if not matching_dirs:\n",
" print(f\"No matching directory found for trait: {trait}\")\n",
" \n",
" # Record that this trait is not available and exit\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",
" print(f\"Task marked as completed. {trait} is not directly represented in the TCGA dataset.\")\n",
"else:\n",
" # If we found matching directories\n",
" print(f\"Found matching directories: {matching_dirs}\")\n",
" \n",
" # Select the most specific directory for rectal cancer\n",
" if \"TCGA_Rectal_Cancer_(READ)\" in matching_dirs:\n",
" selected_dir = \"TCGA_Rectal_Cancer_(READ)\" # Choose the most specific match\n",
" else:\n",
" selected_dir = matching_dirs[0] # Default to first match if specific one not found\n",
" \n",
" print(f\"Selected directory: {selected_dir}\")\n",
" cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n",
" \n",
" # Step 2: Get file paths for clinical and genetic data\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" # Step 3: Load the 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",
" # Step 4: Print column names of clinical data\n",
" print(\"Clinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n"
]
},
{
"cell_type": "markdown",
"id": "e032b442",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4de6f594",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:55.238055Z",
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"shell.execute_reply": "2025-03-25T03:47:55.245713Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [57.0, 41.0, 41.0, 76.0, 48.0], 'days_to_birth': [-21098.0, -15082.0, -15082.0, -28119.0, -17759.0]}\n",
"\n",
"Gender columns preview:\n",
"{'gender': ['MALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']}\n"
]
}
],
"source": [
"# 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",
"# Define cohort directory path\n",
"cohort_dir = os.path.join(tcga_root_dir, \"TCGA_Rectal_Cancer_(READ)\")\n",
"\n",
"# Get clinical file path\n",
"clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"# Read the clinical data\n",
"clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\\t')\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",
"# 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(\"Age columns preview:\")\n",
"print(age_preview)\n",
"print(\"\\nGender columns preview:\")\n",
"print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "c112f776",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1744003a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:55.247131Z",
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"shell.execute_reply": "2025-03-25T03:47:55.249407Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chosen age column: age_at_initial_pathologic_diagnosis\n",
"First 5 values of age column: [57.0, 41.0, 41.0, 76.0, 48.0]\n",
"Chosen gender column: gender\n",
"First 5 values of gender column: ['MALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']\n"
]
}
],
"source": [
"# Evaluate age columns\n",
"age_col_candidates = {'age_at_initial_pathologic_diagnosis': [57.0, 41.0, 41.0, 76.0, 48.0], \n",
" 'days_to_birth': [-21098.0, -15082.0, -15082.0, -28119.0, -17759.0]}\n",
"\n",
"# Evaluate gender columns\n",
"gender_col_candidates = {'gender': ['MALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']}\n",
"\n",
"# Select age column\n",
"# 'age_at_initial_pathologic_diagnosis' provides direct age values, while 'days_to_birth' requires conversion\n",
"# Both columns have no missing values in the preview, but 'age_at_initial_pathologic_diagnosis' is more straightforward\n",
"age_col = 'age_at_initial_pathologic_diagnosis'\n",
"\n",
"# Select gender column\n",
"# Only one gender column available which contains valid values\n",
"gender_col = 'gender'\n",
"\n",
"# Print the chosen columns\n",
"print(f\"Chosen age column: {age_col}\")\n",
"print(f\"First 5 values of age column: {age_col_candidates[age_col]}\")\n",
"print(f\"Chosen gender column: {gender_col}\")\n",
"print(f\"First 5 values of gender column: {gender_col_candidates[gender_col]}\")\n"
]
},
{
"cell_type": "markdown",
"id": "f5d34360",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "994c32a8",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:55.250781Z",
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"shell.execute_reply": "2025-03-25T03:48:09.566721Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved clinical data with 186 samples\n",
"After normalization: 19848 genes remaining\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved normalized gene expression data\n",
"Linked data shape: (105, 19851) (samples x features)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After handling missing values, data shape: (105, 19851)\n",
"For the feature 'Rectal_Cancer', the least common label is '0' with 10 occurrences. This represents 9.52% of the dataset.\n",
"The distribution of the feature 'Rectal_Cancer' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 54.0\n",
" 50% (Median): 63.0\n",
" 75%: 73.0\n",
"Min: 31.0\n",
"Max: 90.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '0.0' with 49 occurrences. This represents 46.67% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved usable linked data to ../../output/preprocess/Rectal_Cancer/TCGA.csv\n"
]
}
],
"source": [
"# Step 1: Extract and standardize clinical features\n",
"# Use the Rectal Cancer directory identified in Step 1\n",
"selected_dir = \"TCGA_Rectal_Cancer_(READ)\"\n",
"cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n",
"\n",
"# Get the file paths for clinical and genetic data\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"# Load the data\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",
"# Extract standardized clinical features using the provided trait variable\n",
"clinical_features = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=trait, # Using the provided trait variable\n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\n",
"\n",
"# Save the clinical data to out_clinical_data_file\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\"Saved clinical data with {len(clinical_features)} samples\")\n",
"\n",
"# Step 2: Normalize gene symbols in gene expression data\n",
"# Transpose to get genes as rows\n",
"gene_df = genetic_df\n",
"\n",
"# Normalize gene symbols using NCBI Gene database synonyms\n",
"normalized_gene_df = normalize_gene_symbols_in_index(gene_df)\n",
"print(f\"After normalization: {len(normalized_gene_df)} genes remaining\")\n",
"\n",
"# Save the normalized gene expression 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\"Saved normalized gene expression data\")\n",
"\n",
"# Step 3: Link clinical and genetic data\n",
"# Merge clinical features with genetic expression data\n",
"linked_data = clinical_features.join(normalized_gene_df.T, how='inner')\n",
"print(f\"Linked data shape: {linked_data.shape} (samples x features)\")\n",
"\n",
"# Step 4: Handle missing values\n",
"cleaned_data = handle_missing_values(linked_data, trait_col=trait)\n",
"print(f\"After handling missing values, data shape: {cleaned_data.shape}\")\n",
"\n",
"# Step 5: Determine if trait or demographics are severely biased\n",
"trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait=trait)\n",
"\n",
"# Step 6: Validate data quality and save cohort information\n",
"note = \"The dataset contains gene expression data along with clinical information for rectal cancer patients from TCGA.\"\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=cleaned_data,\n",
" note=note\n",
")\n",
"\n",
"# Step 7: Save the linked data if usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" cleaned_data.to_csv(out_data_file)\n",
" print(f\"Saved usable linked data to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset was determined to be unusable and was not saved.\")"
]
}
],
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|