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{
"cells": [
{
"cell_type": "code",
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"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 = \"Uterine_Carcinosarcoma\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Uterine_Carcinosarcoma/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Uterine_Carcinosarcoma/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Uterine_Carcinosarcoma/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Uterine_Carcinosarcoma/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "0fb134e2",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f5d1b3bd",
"metadata": {
"execution": {
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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 relevant directories for Uterine_Carcinosarcoma: ['TCGA_Uterine_Carcinosarcoma_(UCS)']\n",
"Selected directory for Uterine_Carcinosarcoma: TCGA_Uterine_Carcinosarcoma_(UCS)\n",
"Clinical data file: ../../input/TCGA/TCGA_Uterine_Carcinosarcoma_(UCS)/TCGA.UCS.sampleMap_UCS_clinicalMatrix\n",
"Genetic data file: ../../input/TCGA/TCGA_Uterine_Carcinosarcoma_(UCS)/TCGA.UCS.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Clinical data columns:\n",
"['CDE_ID_3226963', '_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'aln_pos_ihc', 'aln_pos_light_micro', 'anatomic_neoplasm_subdivision', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'birth_control_pill_history_usage_category', 'clinical_stage', 'colorectal_cancer', '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', 'diabetes', 'form_completion_date', 'gender', 'height', 'histological_type', 'history_of_neoadjuvant_treatment', 'horm_ther', 'hypertension', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_pathologic_diagnosis_method', 'initial_weight', 'is_ffpe', 'lost_follow_up', 'menopause_status', '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', 'oct_embedded', 'other_dx', 'pathology_report_file_name', 'patient_id', 'pct_tumor_invasion', 'peritoneal_wash', 'person_neoplasm_cancer_status', 'pln_pos_ihc', 'pln_pos_light_micro', 'postoperative_rx_tx', 'pregnancies', 'primary_therapy_outcome_success', 'prior_tamoxifen_administered_usage_category', 'radiation_therapy', 'recurrence_second_surgery_neoplasm_surgical_procedure_name', 'residual_tumor', 'sample_type', 'sample_type_id', 'surgical_approach', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'total_aor_lnp', 'total_aor_lnr', 'total_pelv_lnp', 'total_pelv_lnr', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_UCS_miRNA_HiSeq', '_GENOMIC_ID_TCGA_UCS_RPPA', '_GENOMIC_ID_data/public/TCGA/UCS/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_UCS_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_UCS_exp_HiSeqV2', '_GENOMIC_ID_TCGA_UCS_mutation_bcm_gene', '_GENOMIC_ID_TCGA_UCS_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_UCS_mutation_curated_broad_gene', '_GENOMIC_ID_TCGA_UCS_gistic2thd', '_GENOMIC_ID_TCGA_UCS_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_UCS_mutation_broad_gene', '_GENOMIC_ID_TCGA_UCS_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_UCS_gistic2', '_GENOMIC_ID_TCGA_UCS_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_UCS_hMethyl450', '_GENOMIC_ID_TCGA_UCS_PDMRNAseq']\n"
]
}
],
"source": [
"# Step 1: Review subdirectories to find one related to Uterine Carcinosarcoma\n",
"import os\n",
"\n",
"# List all directories in TCGA root directory\n",
"tcga_dirs = os.listdir(tcga_root_dir)\n",
"print(f\"Available TCGA directories: {tcga_dirs}\")\n",
"\n",
"# Look for directories related to Uterine Carcinosarcoma\n",
"relevant_dirs = []\n",
"for dir_name in tcga_dirs:\n",
" dir_lower = dir_name.lower()\n",
" if \"uterine\" in dir_lower and \"carcinosarcoma\" in dir_lower:\n",
" relevant_dirs.append(dir_name)\n",
"\n",
"print(f\"Potential relevant directories for {trait}: {relevant_dirs}\")\n",
"\n",
"# Select the appropriate directory for the trait\n",
"selected_dir = None\n",
"if relevant_dirs:\n",
" selected_dir = relevant_dirs[0] # Take the first match since it's specific\n",
"\n",
"if selected_dir:\n",
" print(f\"Selected directory for {trait}: {selected_dir}\")\n",
" \n",
" # Get the full path to the directory\n",
" cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n",
" \n",
" # Step 2: Find clinical and genetic data files\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" print(f\"Clinical data file: {clinical_file_path}\")\n",
" print(f\"Genetic data file: {genetic_file_path}\")\n",
" \n",
" # Step 3: Load the data files\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",
" # Step 4: Print column names of clinical data\n",
" print(\"\\nClinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n",
" \n",
" # Check if both datasets are available\n",
" is_gene_available = not genetic_df.empty\n",
" is_trait_available = not clinical_df.empty\n",
" \n",
" # Initial validation\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 directory specifically relevant to the trait: {trait}\")\n",
" \n",
" # Since the trait is not directly represented, we should record this fact\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"
]
},
{
"cell_type": "markdown",
"id": "11220cf2",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "145aa1dd",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [65, 63, 69, 68, 61], 'days_to_birth': [-24102, -23359, -25413, -25041, -22341]}\n",
"Gender columns preview:\n",
"{'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']}\n"
]
}
],
"source": [
"# Step 1: Identify candidate demographic features\n",
"candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n",
"candidate_gender_cols = ['gender']\n",
"\n",
"# Step 2: Extract and preview candidate columns\n",
"clinical_data_file, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, f\"TCGA_{trait.replace('_', '_')}_(UCS)\"))\n",
"clinical_df = pd.read_csv(clinical_data_file, sep='\\t', index_col=0)\n",
"\n",
"# Extract and preview age columns\n",
"if candidate_age_cols:\n",
" age_df = clinical_df[candidate_age_cols]\n",
" print(\"Age columns preview:\")\n",
" print(preview_df(age_df))\n",
"\n",
"# Extract and preview gender columns\n",
"if candidate_gender_cols:\n",
" gender_df = clinical_df[candidate_gender_cols]\n",
" print(\"Gender columns preview:\")\n",
" print(preview_df(gender_df))\n"
]
},
{
"cell_type": "markdown",
"id": "078a8f53",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f2d526ed",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected age column: age_at_initial_pathologic_diagnosis\n",
"Age column preview: [65, 63, 69, 68, 61]\n",
"Selected gender column: gender\n",
"Gender column preview: ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']\n"
]
}
],
"source": [
"# Examine the age columns\n",
"age_columns = {'age_at_initial_pathologic_diagnosis': [65, 63, 69, 68, 61], \n",
" 'days_to_birth': [-24102, -23359, -25413, -25041, -22341]}\n",
"\n",
"# Examine the gender columns\n",
"gender_columns = {'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']}\n",
"\n",
"# Select appropriate columns for age and gender\n",
"# For age, we have two options:\n",
"# 1. 'age_at_initial_pathologic_diagnosis' - direct age values\n",
"# 2. 'days_to_birth' - negative days since birth (can be converted to age)\n",
"# We'll choose 'age_at_initial_pathologic_diagnosis' as it's already in years and easier to interpret\n",
"age_col = 'age_at_initial_pathologic_diagnosis'\n",
"\n",
"# For gender, there's only one option available\n",
"gender_col = 'gender' if gender_columns else None\n",
"\n",
"# Print the selected columns\n",
"print(f\"Selected age column: {age_col}\")\n",
"print(f\"Age column preview: {age_columns[age_col]}\")\n",
"print(f\"Selected gender column: {gender_col}\")\n",
"print(f\"Gender column preview: {gender_columns[gender_col] if gender_col else None}\")\n"
]
},
{
"cell_type": "markdown",
"id": "2bcf76a5",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved clinical data with 57 samples\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After normalization: 19848 genes remaining\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved normalized gene expression data\n",
"Linked data shape: (57, 19851) (samples x features)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After handling missing values, data shape: (57, 19851)\n",
"Quartiles for 'Uterine_Carcinosarcoma':\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 'Uterine_Carcinosarcoma' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 62.0\n",
" 50% (Median): 68.0\n",
" 75%: 76.0\n",
"Min: 51\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 57 occurrences. This represents 100.00% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is severely biased.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset was determined to be unusable and was not saved.\n"
]
}
],
"source": [
"# Step 1: Extract and standardize clinical features\n",
"# Use the Uterine Carcinosarcoma directory identified in Step 1\n",
"selected_dir = \"TCGA_Uterine_Carcinosarcoma_(UCS)\"\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 uterine carcinosarcoma 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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