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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 = \"Lactose_Intolerance\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Lactose_Intolerance/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Lactose_Intolerance/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Lactose_Intolerance/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Lactose_Intolerance/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "b6a27c09",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"id": "6d189439",
"metadata": {
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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",
"Found potential match: TCGA_Pancreatic_Cancer_(PAAD) (matched keyword: pancrea)\n",
"Selected directory: TCGA_Pancreatic_Cancer_(PAAD)\n",
"Clinical file: TCGA.PAAD.sampleMap_PAAD_clinicalMatrix\n",
"Genetic file: TCGA.PAAD.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', 'adenocarcinoma_invasion', 'age_at_initial_pathologic_diagnosis', 'alcohol_history_documented', 'alcoholic_exposure_category', 'amount_of_alcohol_consumption_per_day', 'anatomic_neoplasm_subdivision', 'anatomic_neoplasm_subdivision_other', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_diabetes_onset', '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', 'days_to_pancreatitis_onset', 'family_history_of_cancer', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'frequency_of_alcohol_consumption', 'gender', 'histologic_grading_tier_category', 'histological_type', 'histological_type_other', 'history_of_chronic_pancreatitis', 'history_of_diabetes', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'init_pathology_dx_method_other', 'initial_pathologic_diagnosis_method', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'longest_dimension', 'lost_follow_up', 'lymph_node_examined_count', 'maximum_tumor_dimension', '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', 'number_of_lymphnodes_positive_by_he', 'number_of_lymphnodes_positive_by_ihc', 'number_pack_years_smoked', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_death_reason', 'patient_id', 'person_neoplasm_cancer_status', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'progression_determined_by', 'radiation_therapy', 'relative_cancer_type', 'residual_disease_post_new_tumor_event_margin_status', 'residual_tumor', 'sample_type', 'sample_type_id', 'shortest_dimension', 'source_of_patient_death_reason', 'stopped_smoking_year', 'surgery_performed_type', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'tumor_tissue_site', 'tumor_type', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', 'year_of_tobacco_smoking_onset', '_GENOMIC_ID_TCGA_PAAD_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_PAAD_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_PAAD_gistic2', '_GENOMIC_ID_TCGA_PAAD_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_PAAD_exp_HiSeqV2', '_GENOMIC_ID_TCGA_PAAD_mutation_curated_broad_gene', '_GENOMIC_ID_TCGA_PAAD_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_PAAD_mutation_bcm_gene', '_GENOMIC_ID_TCGA_PAAD_RPPA', '_GENOMIC_ID_TCGA_PAAD_hMethyl450', '_GENOMIC_ID_TCGA_PAAD_mutation', '_GENOMIC_ID_TCGA_PAAD_PDMRNAseq', '_GENOMIC_ID_TCGA_PAAD_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_PAAD_mutation_broad_gene', '_GENOMIC_ID_TCGA_PAAD_gistic2thd', '_GENOMIC_ID_data/public/TCGA/PAAD/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_PAAD_miRNA_HiSeq', '_GENOMIC_ID_TCGA_PAAD_exp_HiSeqV2_percentile']\n",
"\n",
"Clinical data shape: (196, 114)\n",
"Genetic data shape: (20530, 183)\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"# 1. List all subdirectories in the TCGA root directory\n",
"subdirectories = os.listdir(tcga_root_dir)\n",
"print(f\"Available TCGA subdirectories: {subdirectories}\")\n",
"\n",
"# The target trait is Lactose_Intolerance\n",
"target_trait = trait.lower() # \"lactose_intolerance\"\n",
"\n",
"# Search for a directory matching our trait (digestive system related)\n",
"best_match = None\n",
"relevant_keywords = [\"digest\", \"colon\", \"intestin\", \"gut\", \"stomach\", \"gastro\", \"coad\", \"read\", \"rect\", \"pancrea\"]\n",
"\n",
"for subdir in subdirectories:\n",
" if not os.path.isdir(os.path.join(tcga_root_dir, subdir)) or subdir.startswith('.'):\n",
" continue\n",
" \n",
" subdir_lower = subdir.lower()\n",
" \n",
" # Check if the directory name contains any of our relevant keywords\n",
" for keyword in relevant_keywords:\n",
" if keyword in subdir_lower:\n",
" best_match = subdir\n",
" print(f\"Found potential match: {subdir} (matched keyword: {keyword})\")\n",
" break\n",
" \n",
" if best_match:\n",
" break\n",
"\n",
"# Handle the case where a match is 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 in TCGA.\")\n"
]
},
{
"cell_type": "markdown",
"id": "faff4bf9",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ecf85358",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age-related columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [70.0, 51.0, 51.0, 62.0, 52.0], 'days_to_birth': [nan, nan, -18698.0, -22792.0, -19014.0]}\n",
"\n",
"Gender-related columns preview:\n",
"{'gender': ['MALE', 'MALE', 'FEMALE', 'MALE', 'MALE']}\n"
]
}
],
"source": [
"# 1. Identify columns that likely contain information about patients' 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 candidate columns from clinical data\n",
"# First, let's get the clinical data file path\n",
"cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Stomach_Cancer_(STAD)')\n",
"clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"# Load the clinical data\n",
"clinical_data = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Extract age-related columns\n",
"age_columns = clinical_data[candidate_age_cols]\n",
"print(\"Age-related columns preview:\")\n",
"age_preview = preview_df(age_columns, n=5)\n",
"print(age_preview)\n",
"\n",
"# Extract gender-related columns\n",
"gender_columns = clinical_data[candidate_gender_cols]\n",
"print(\"\\nGender-related columns preview:\")\n",
"gender_preview = preview_df(gender_columns, n=5)\n",
"print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "9f660172",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "da4c2eed",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chosen age column: age_at_initial_pathologic_diagnosis\n",
"Chosen gender column: gender\n"
]
}
],
"source": [
"# Examine the age-related columns\n",
"age_col = None\n",
"gender_col = None\n",
"\n",
"# Age dictionary preview\n",
"age_dict = {'age_at_initial_pathologic_diagnosis': [70.0, 51.0, 51.0, 62.0, 52.0], \n",
" 'days_to_birth': [float('nan'), float('nan'), -18698.0, -22792.0, -19014.0]}\n",
"\n",
"# Gender dictionary preview\n",
"gender_dict = {'gender': ['MALE', 'MALE', 'FEMALE', 'MALE', 'MALE']}\n",
"\n",
"# For age column selection\n",
"for col, values in age_dict.items():\n",
" # Check if the values are reasonable and not mostly missing\n",
" if not all(pd.isna(v) for v in values):\n",
" age_col = col\n",
" break\n",
"\n",
"# For gender column selection\n",
"for col, values in gender_dict.items():\n",
" # Check if the values make sense for gender\n",
" valid_genders = ['MALE', 'FEMALE', 'male', 'female']\n",
" if any(str(v).upper() in ['MALE', 'FEMALE'] for v in values if v is not None):\n",
" gender_col = col\n",
" break\n",
"\n",
"# Print the chosen columns\n",
"print(f\"Chosen age column: {age_col}\")\n",
"print(f\"Chosen gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "2c269c6f",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Lactose_Intolerance/gene_data/TCGA.csv\n",
"Gene expression data shape after normalization: (19848, 183)\n",
"Clinical data saved to ../../output/preprocess/Lactose_Intolerance/clinical_data/TCGA.csv\n",
"Clinical data shape: (196, 3)\n",
"Number of samples in clinical data: 196\n",
"Number of samples in genetic data: 183\n",
"Number of common samples: 183\n",
"Linked data shape: (183, 19851)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (183, 19851)\n",
"For the feature 'Lactose_Intolerance', the least common label is '0' with 4 occurrences. This represents 2.19% of the dataset.\n",
"The distribution of the feature 'Lactose_Intolerance' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 57.0\n",
" 50% (Median): 65.0\n",
" 75%: 73.0\n",
"Min: 35\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 82 occurrences. This represents 44.81% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"Dataset deemed not usable based on validation criteria. Data not saved.\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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