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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
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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 = \"Acute_Myeloid_Leukemia\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Acute_Myeloid_Leukemia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "96c766f9",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"id": "74c6204b",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Available TCGA subdirectories: ['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",
"Selected directory: TCGA_Acute_Myeloid_Leukemia_(LAML)\n",
"Clinical data file: ../../input/TCGA/TCGA_Acute_Myeloid_Leukemia_(LAML)/TCGA.LAML.sampleMap_LAML_clinicalMatrix\n",
"Genetic data file: ../../input/TCGA/TCGA_Acute_Myeloid_Leukemia_(LAML)/TCGA.LAML.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Clinical data columns:\n",
"['FISH_test_component', 'FISH_test_component_percentage_value', '_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_LAML', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mirna_LAML', '_PANCAN_mutation_PANCAN', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'age_at_initial_pathologic_diagnosis', 'atra_exposure', 'cumulative_agent_total_dose', 'cytogenetic_abnormality', 'cytogenetic_abnormality_other', 'cytogenetic_analysis_performed_ind', 'days_to_birth', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'disease_detection_molecular_analysis_method_type', 'fish_evaluation_performed_ind', 'fluorescence_in_situ_hybrid_cytogenetics_metaphase_ncls_rslt_cnt', 'fluorescence_in_situ_hybridization_abnormal_result_indicator', 'form_completion_date', 'gender', 'history_of_neoadjuvant_treatment', 'hydroxyurea_administration_prior_registration_clinicl_stdy_ndctr', 'hydroxyurea_agent_administered_day_count', 'immunophenotype_cytochemistry_testing_result', 'informed_consent_verified', 'is_ffpe', 'lab_procedure_abnormal_lymphocyte_result_percent_value', 'lab_procedure_blast_cell_outcome_percentage_value', 'lab_procedure_bone_marrow_band_cell_result_percent_value', 'lab_procedure_bone_marrow_basophil_result_percent_value', 'lab_procedure_bone_marrow_blast_cell_outcome_percent_value', 'lab_procedure_bone_marrow_cellularity_outcome_percent_value', 'lab_procedure_bone_marrow_lymphocyte_outcome_percent_value', 'lab_procedure_bone_marrow_metamyelocyte_result_value', 'lab_procedure_bone_marrow_myelocyte_result_percent_value', 'lab_procedure_bone_marrow_neutrophil_result_percent_value', 'lab_procedure_bone_marrow_prolymphocyte_result_percent_value', 'lab_procedure_bone_marrow_promonocyte_count_result_percent_value', 'lab_procedure_bone_marrow_promyelocyte_result_percent_value', 'lab_procedure_hematocrit_outcome_percent_value', 'lab_procedure_hemoglobin_result_specified_value', 'lab_procedure_leukocyte_result_unspecified_value', 'lab_procedure_monocyte_result_percent_value', 'lab_procedure_platelet_result_specified_value', 'leukemia_french_american_british_morphology_code', 'leukemia_specimen_cell_source_type', 'molecular_analysis_abnormal_result_indicator', 'molecular_analysis_abnormality_testing_result', 'molecular_analysis_performed_indicator', 'patient_id', 'person_history_nonmedical_leukemia_causing_agent_type', 'prior_dx', 'prior_hematologic_disorder_diagnosis_indicator', 'sample_type', 'sample_type_id', 'steroid_therapy_administered', 'tissue_source_site', 'total_dose_units', 'tumor_tissue_site', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_LAML_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_LAML_hMethyl27', '_GENOMIC_ID_TCGA_LAML_exp_HiSeqV2', '_GENOMIC_ID_TCGA_LAML_miRNA_GA', '_GENOMIC_ID_data/public/TCGA/LAML/miRNA_GA_gene', '_GENOMIC_ID_TCGA_LAML_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_LAML_mutation_wustl_hiseq_gene', '_GENOMIC_ID_TCGA_LAML_exp_GA_exon', '_GENOMIC_ID_TCGA_LAML_gistic2', '_GENOMIC_ID_TCGA_LAML_exp_GA', '_GENOMIC_ID_TCGA_LAML_hMethyl450', '_GENOMIC_ID_TCGA_LAML_mutation', '_GENOMIC_ID_TCGA_LAML_PDMRNAseq', '_GENOMIC_ID_TCGA_LAML_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_LAML_gistic2thd', '_GENOMIC_ID_TCGA_LAML_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_LAML_mutation_wustl_gene']\n",
"\n",
"Clinical data shape: (200, 91)\n",
"Genetic data shape: (20530, 173)\n"
]
}
],
"source": [
"import os\n",
"\n",
"# Step 1: Identify the most relevant directory for Acute Myeloid Leukemia\n",
"tcga_subdirs = os.listdir(tcga_root_dir)\n",
"print(f\"Available TCGA subdirectories: {tcga_subdirs}\")\n",
"\n",
"# Look for directories related to Acute Myeloid Leukemia\n",
"target_dir = None\n",
"for subdir in tcga_subdirs:\n",
" if \"Leukemia\" in subdir and \"Acute\" in subdir and \"Myeloid\" in subdir:\n",
" target_dir = subdir\n",
" break\n",
"\n",
"if target_dir is None:\n",
" print(f\"No suitable directory found for {trait}.\")\n",
" # Mark the task as completed by creating a JSON record indicating data is not available\n",
" validate_and_save_cohort_info(is_final=False, cohort=\"TCGA\", info_path=json_path, \n",
" is_gene_available=False, is_trait_available=False)\n",
" exit() # Exit the program\n",
"\n",
"# Step 2: Get file paths for the selected directory\n",
"cohort_dir = os.path.join(tcga_root_dir, target_dir)\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"print(f\"Selected directory: {target_dir}\")\n",
"print(f\"Clinical data file: {clinical_file_path}\")\n",
"print(f\"Genetic data file: {genetic_file_path}\")\n",
"\n",
"# Step 3: 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",
"# Step 4: Print column names of clinical data\n",
"print(\"\\nClinical data columns:\")\n",
"print(clinical_df.columns.tolist())\n",
"\n",
"# Additional basic information\n",
"print(f\"\\nClinical data shape: {clinical_df.shape}\")\n",
"print(f\"Genetic data shape: {genetic_df.shape}\")\n"
]
},
{
"cell_type": "markdown",
"id": "9a53de26",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7881f14f",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [50, 61, 30, 77, 46], 'days_to_birth': [-18385, -22584, -11203, -28124, -16892]}\n",
"\n",
"Gender columns preview:\n",
"{'gender': ['MALE', 'FEMALE', 'MALE', 'MALE', 'MALE']}\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. Extract and preview the candidate columns\n",
"clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'TCGA_Acute_Myeloid_Leukemia_(LAML)'))\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Extract and 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",
"print(\"Age columns preview:\")\n",
"print(age_preview)\n",
"\n",
"# Extract and 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",
"print(\"\\nGender columns preview:\")\n",
"print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "9ef79128",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "651fd9e5",
"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": [
"# Select age column - both columns seem to have data but age_at_initial_pathologic_diagnosis is more intuitive\n",
"age_col = \"age_at_initial_pathologic_diagnosis\"\n",
"\n",
"# Select gender column - only one option available and it appears to have consistent data\n",
"gender_col = \"gender\"\n",
"\n",
"# 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": "54884be7",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7bd1d64c",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/TCGA.csv\n",
"Clinical data shape: (200, 3)\n",
" AML Age Gender\n",
"sampleID \n",
"TCGA-AB-2802-03 1 50 1\n",
"TCGA-AB-2803-03 1 61 0\n",
"TCGA-AB-2804-03 1 30 1\n",
"TCGA-AB-2805-03 1 77 1\n",
"TCGA-AB-2806-03 1 46 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/TCGA.csv\n",
"Normalized gene data shape: (19848, 173)\n",
"Linked data shape: (173, 19851)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After handling missing values - linked data shape: (173, 19851)\n",
"Quartiles for 'AML':\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 'AML' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 44.0\n",
" 50% (Median): 58.0\n",
" 75%: 67.0\n",
"Min: 18\n",
"Max: 88\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 80 occurrences. This represents 46.24% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"After removing biased features - linked data shape: (173, 19851)\n",
"Linked data not saved due to quality concerns\n"
]
}
],
"source": [
"# Step 1: Extract and standardize the clinical features\n",
"# Get file paths\n",
"cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Acute_Myeloid_Leukemia_(LAML)')\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"# Load 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",
"# Create standardized clinical features dataframe with trait, age, and gender\n",
"clinical_features = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=\"AML\", # Using \"AML\" as the trait name\n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\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",
"print(f\"Clinical data shape: {clinical_features.shape}\")\n",
"print(clinical_features.head())\n",
"\n",
"# Step 2: Normalize gene symbols in gene expression data\n",
"# Transpose the genetic data to have genes as rows\n",
"genetic_data = genetic_df.copy()\n",
"\n",
"# Normalize gene symbols using the NCBI Gene database synonyms\n",
"normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)\n",
"\n",
"# Save normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"\n",
"# Step 3: Link clinical and genetic data\n",
"# Transpose genetic data to get samples as rows, genes as columns\n",
"genetic_data_transposed = normalized_gene_data.T\n",
"\n",
"# Ensure clinical and genetic data have the same samples (index values)\n",
"common_samples = clinical_features.index.intersection(genetic_data_transposed.index)\n",
"clinical_subset = clinical_features.loc[common_samples]\n",
"genetic_subset = genetic_data_transposed.loc[common_samples]\n",
"\n",
"# Combine clinical and genetic data\n",
"linked_data = pd.concat([clinical_subset, genetic_subset], axis=1)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 4: Handle missing values\n",
"linked_data = handle_missing_values(linked_data, trait_col=\"AML\")\n",
"print(f\"After handling missing values - linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 5: Determine biased features\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=\"AML\")\n",
"print(f\"After removing biased features - linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 6: Validate data quality and save cohort info\n",
"# First check if we have both gene and trait data\n",
"is_gene_available = linked_data.shape[1] > 3 # More than just AML, Age, Gender\n",
"is_trait_available = \"AML\" in linked_data.columns\n",
"\n",
"# Take notes of special findings\n",
"notes = \"TCGA AML dataset successfully processed. Contains tumor samples (AML=1) and normal samples (AML=0).\"\n",
"\n",
"# Validate the 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=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=notes\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(\"Linked data not saved due to quality concerns\")"
]
}
],
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|