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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1c558516",
"metadata": {
"execution": {
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"outputs": [],
"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 = \"Colon_and_Rectal_Cancer\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Colon_and_Rectal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "3d3e1f61",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e1bc4a32",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found potential match: TCGA_Liver_Cancer_(LIHC) (score: 1)\n",
"Found potential match: TCGA_Rectal_Cancer_(READ) (score: 2)\n",
"Found potential match: TCGA_Colon_and_Rectal_Cancer_(COADREAD) (score: 4)\n",
"Selected directory: TCGA_Colon_and_Rectal_Cancer_(COADREAD)\n",
"Clinical file: TCGA.COADREAD.sampleMap_COADREAD_clinicalMatrix\n",
"Genetic file: TCGA.COADREAD.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"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_COADREAD', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_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', 'disease_code', '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', 'project_code', '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_COADREAD_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_COADREAD_PDMRNAseq', '_GENOMIC_ID_TCGA_COADREAD_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_COADREAD_hMethyl450', '_GENOMIC_ID_TCGA_COADREAD_gistic2thd', '_GENOMIC_ID_TCGA_COADREAD_hMethyl27', '_GENOMIC_ID_TCGA_COADREAD_G4502A_07_3', '_GENOMIC_ID_TCGA_COADREAD_PDMarrayCNV', '_GENOMIC_ID_TCGA_COADREAD_exp_HiSeqV2', '_GENOMIC_ID_TCGA_COADREAD_PDMarray', '_GENOMIC_ID_TCGA_COADREAD_gistic2', '_GENOMIC_ID_TCGA_COADREAD_mutation', '_GENOMIC_ID_TCGA_COADREAD_RPPA_RBN', '_GENOMIC_ID_TCGA_COADREAD_PDMRNAseqCNV']\n",
"\n",
"Clinical data shape: (736, 123)\n",
"Genetic data shape: (20530, 434)\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"# 1. Find the most relevant directory for Colon and Rectal Cancer\n",
"subdirectories = os.listdir(tcga_root_dir)\n",
"target_trait = trait.lower().replace(\"_\", \" \") # Convert to lowercase for case-insensitive matching\n",
"\n",
"# Start with no match, then find the best match based on similarity to target trait\n",
"best_match = None\n",
"best_match_score = 0\n",
"\n",
"for subdir in subdirectories:\n",
" subdir_lower = subdir.lower()\n",
" \n",
" # Calculate a simple similarity score - more matching words = better match\n",
" # This prioritizes exact matches over partial matches\n",
" score = 0\n",
" for word in target_trait.split():\n",
" if word in subdir_lower:\n",
" score += 1\n",
" \n",
" # Track the best match\n",
" if score > best_match_score:\n",
" best_match_score = score\n",
" best_match = subdir\n",
" print(f\"Found potential match: {subdir} (score: {score})\")\n",
"\n",
"# Use the best match if found\n",
"if best_match:\n",
" print(f\"Selected directory: {best_match}\")\n",
" \n",
" # 2. Get the clinical and genetic data file paths\n",
" cohort_dir = os.path.join(tcga_root_dir, best_match)\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",
" \n",
" # Check if we have both gene and trait data\n",
" is_gene_available = genetic_df.shape[0] > 0\n",
" is_trait_available = clinical_df.shape[0] > 0\n",
" \n",
"else:\n",
" print(f\"No suitable directory found for {trait}.\")\n",
" is_gene_available = False\n",
" is_trait_available = False\n",
"\n",
"# Record the data availability\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",
"\n",
"# Exit if no suitable directory was found\n",
"if not best_match:\n",
" print(\"Skipping this trait as no suitable data was found.\")\n"
]
},
{
"cell_type": "markdown",
"id": "709cd3d3",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5933e2a7",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:24:35.185376Z"
}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [61.0, 67.0, 42.0, 74.0, nan], 'days_to_birth': [-22379.0, -24523.0, -15494.0, -27095.0, nan]}\n",
"\n",
"Gender columns preview:\n",
"{'gender': ['FEMALE', 'MALE', 'FEMALE', 'MALE', nan]}\n"
]
}
],
"source": [
"# Identify columns that might contain age information\n",
"candidate_age_cols = [\n",
" 'age_at_initial_pathologic_diagnosis',\n",
" 'days_to_birth' # Negative days to birth can represent age\n",
"]\n",
"\n",
"# Identify columns that might contain gender information\n",
"candidate_gender_cols = [\n",
" 'gender'\n",
"]\n",
"\n",
"# Load the clinical data to examine these columns\n",
"cohort_dir = os.path.join(tcga_root_dir, \"TCGA_Colon_and_Rectal_Cancer_(COADREAD)\")\n",
"clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Extract and preview age-related 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-related columns\n",
"if candidate_gender_cols:\n",
" gender_df = clinical_df[candidate_gender_cols]\n",
" print(\"\\nGender columns preview:\")\n",
" print(preview_df(gender_df))\n"
]
},
{
"cell_type": "markdown",
"id": "37d99797",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "88b72458",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:24:35.186985Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age column candidates:\n",
"Column: age_at_initial_pathologic_diagnosis, Values: [61.0, 67.0, 42.0, 74.0, None], Missing: 20.0%\n",
"Column: days_to_birth, Values: [-22379.0, -24523.0, -15494.0, -27095.0, None], Missing: 20.0%\n",
"\n",
"Gender column candidates:\n",
"Column: gender, Values: ['FEMALE', 'MALE', 'FEMALE', 'MALE', None], Missing: 20.0%\n",
"\n",
"Selected columns:\n",
"Age column: age_at_initial_pathologic_diagnosis\n",
"Gender column: gender\n"
]
}
],
"source": [
"# Check the age columns\n",
"print(\"Age column candidates:\")\n",
"for col, values in {'age_at_initial_pathologic_diagnosis': [61.0, 67.0, 42.0, 74.0, None], \n",
" 'days_to_birth': [-22379.0, -24523.0, -15494.0, -27095.0, None]}.items():\n",
" missing_count = sum(1 for v in values if v is None or pd.isna(v))\n",
" missing_percentage = missing_count / len(values) * 100\n",
" print(f\"Column: {col}, Values: {values}, Missing: {missing_percentage:.1f}%\")\n",
"\n",
"# Check the gender columns\n",
"print(\"\\nGender column candidates:\")\n",
"for col, values in {'gender': ['FEMALE', 'MALE', 'FEMALE', 'MALE', None]}.items():\n",
" missing_count = sum(1 for v in values if v is None or pd.isna(v))\n",
" missing_percentage = missing_count / len(values) * 100\n",
" print(f\"Column: {col}, Values: {values}, Missing: {missing_percentage:.1f}%\")\n",
"\n",
"# Select the columns\n",
"age_col = 'age_at_initial_pathologic_diagnosis' # Clear age values in years\n",
"gender_col = 'gender' # Standard gender labels\n",
"\n",
"print(\"\\nSelected columns:\")\n",
"print(f\"Age column: {age_col}\")\n",
"print(f\"Gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "a3d7c8f6",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "342d196e",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Colon_and_Rectal_Cancer/gene_data/TCGA.csv\n",
"Gene expression data shape after normalization: (19848, 434)\n",
"Clinical data saved to ../../output/preprocess/Colon_and_Rectal_Cancer/clinical_data/TCGA.csv\n",
"Clinical data shape: (736, 3)\n",
"Number of samples in clinical data: 736\n",
"Number of samples in genetic data: 434\n",
"Number of common samples: 434\n",
"Linked data shape: (434, 19851)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (434, 19851)\n",
"For the feature 'Colon_and_Rectal_Cancer', the least common label is '0' with 51 occurrences. This represents 11.75% of the dataset.\n",
"The distribution of the feature 'Colon_and_Rectal_Cancer' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 56.0\n",
" 50% (Median): 66.0\n",
" 75%: 75.0\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 199 occurrences. This represents 45.85% 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/Colon_and_Rectal_Cancer/TCGA.csv\n",
"Preprocessing completed.\n"
]
}
],
"source": [
"# Step 1: Extract and standardize clinical features\n",
"# Create clinical features dataframe with trait (Canavan Disease) using patient IDs\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",
"# 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=trait)\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=trait)\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=f\"Dataset contains TCGA glioma and brain tumor samples with gene expression and clinical information for {trait}.\"\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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|