{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0c84fc18", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:25:19.460722Z", "iopub.status.busy": "2025-03-25T07:25:19.460492Z", "iopub.status.idle": "2025-03-25T07:25:19.623917Z", "shell.execute_reply": "2025-03-25T07:25:19.623535Z" } }, "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 = \"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" ] }, { "cell_type": "code", "execution_count": 2, "id": "6d189439", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:25:19.625469Z", "iopub.status.busy": "2025-03-25T07:25:19.625317Z", "iopub.status.idle": "2025-03-25T07:25:20.100393Z", "shell.execute_reply": "2025-03-25T07:25:20.099930Z" } }, "outputs": [ { "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", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:25:20.101834Z", "iopub.status.busy": "2025-03-25T07:25:20.101705Z", "iopub.status.idle": "2025-03-25T07:25:20.113707Z", "shell.execute_reply": "2025-03-25T07:25:20.113332Z" } }, "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": { "iopub.execute_input": "2025-03-25T07:25:20.114951Z", "iopub.status.busy": "2025-03-25T07:25:20.114836Z", "iopub.status.idle": "2025-03-25T07:25:20.118345Z", "shell.execute_reply": "2025-03-25T07:25:20.118015Z" } }, "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", "execution_count": 5, "id": "263d3aba", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:25:20.119448Z", "iopub.status.busy": "2025-03-25T07:25:20.119339Z", "iopub.status.idle": "2025-03-25T07:25:28.601586Z", "shell.execute_reply": "2025-03-25T07:25:28.601220Z" } }, "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.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }