{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4bcb1c5b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:48:24.322981Z", "iopub.status.busy": "2025-03-25T03:48:24.322755Z", "iopub.status.idle": "2025-03-25T03:48:24.508611Z", "shell.execute_reply": "2025-03-25T03:48:24.508134Z" } }, "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 = \"Red_Hair\"\n", "\n", "# Input paths\n", "tcga_root_dir = \"../../input/TCGA\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Red_Hair/TCGA.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Red_Hair/gene_data/TCGA.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Red_Hair/clinical_data/TCGA.csv\"\n", "json_path = \"../../output/preprocess/Red_Hair/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "980a80c4", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "5ee2fc6c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:48:24.510137Z", "iopub.status.busy": "2025-03-25T03:48:24.509982Z", "iopub.status.idle": "2025-03-25T03:48:25.684368Z", "shell.execute_reply": "2025-03-25T03:48:25.684015Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found matching directories: ['TCGA_Melanoma_(SKCM)', 'TCGA_Ocular_melanomas_(UVM)']\n", "Selected directory: TCGA_Melanoma_(SKCM)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Clinical data columns:\n", "['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'breslow_depth_value', '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', 'days_to_submitted_specimen_dx', 'distant_metastasis_anatomic_site', 'followup_case_report_form_submission_reason', 'form_completion_date', 'gender', 'height', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'interferon_90_day_prior_excision_admin_indicator', 'is_ffpe', 'lactate_dehydrogenase_result', 'lost_follow_up', 'malignant_neoplasm_mitotic_count_rate', 'melanoma_clark_level_value', 'melanoma_origin_skin_anatomic_site', 'melanoma_ulceration_indicator', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_non_melanoma_event_histologic_type_text', 'new_primary_melanoma_anatomic_site', 'new_tumor_dx_prior_submitted_specimen_dx', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'new_tumor_metastasis_anatomic_site', 'new_tumor_metastasis_anatomic_site_other_text', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'primary_anatomic_site_count', 'primary_melanoma_at_diagnosis_count', 'primary_neoplasm_melanoma_dx', 'primary_tumor_multiple_present_ind', 'prior_systemic_therapy_type', 'radiation_therapy', 'sample_type', 'sample_type_id', 'subsequent_primary_melanoma_during_followup', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tissue_type', 'tumor_descriptor', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_SKCM_exp_HiSeqV2', '_GENOMIC_ID_TCGA_SKCM_hMethyl450', '_GENOMIC_ID_TCGA_SKCM_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_SKCM_miRNA_HiSeq', '_GENOMIC_ID_TCGA_SKCM_gistic2thd', '_GENOMIC_ID_data/public/TCGA/SKCM/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_SKCM_RPPA', '_GENOMIC_ID_TCGA_SKCM_mutation_bcm_gene', '_GENOMIC_ID_TCGA_SKCM_mutation_broad_gene', '_GENOMIC_ID_TCGA_SKCM_gistic2', '_GENOMIC_ID_TCGA_SKCM_mutation', '_GENOMIC_ID_TCGA_SKCM_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_SKCM_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_SKCM_PDMRNAseq', '_GENOMIC_ID_TCGA_SKCM_exp_HiSeqV2_percentile']\n" ] } ], "source": [ "# Step 1: Search for directories related to Red Hair\n", "import os\n", "\n", "# List all directories in TCGA root directory\n", "tcga_dirs = os.listdir(tcga_root_dir)\n", "\n", "# Red hair is associated with melanoma risk, so look for melanoma or skin cancer datasets\n", "matching_dirs = [dir_name for dir_name in tcga_dirs \n", " if any(term in dir_name.lower() for term in \n", " [\"melanoma\", \"skin cancer\", \"skin\", \"skcm\"])]\n", "\n", "if not matching_dirs:\n", " print(f\"No matching directory found for trait: {trait}\")\n", " \n", " # Record that this trait is not available and exit\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", "else:\n", " # If we found matching directories\n", " print(f\"Found matching directories: {matching_dirs}\")\n", " \n", " # Select the most specific directory for melanoma (which may have red hair data)\n", " if \"TCGA_Melanoma_(SKCM)\" in matching_dirs:\n", " selected_dir = \"TCGA_Melanoma_(SKCM)\" # Choose the most specific match\n", " else:\n", " selected_dir = matching_dirs[0] # Default to first match if specific one not found\n", " \n", " print(f\"Selected directory: {selected_dir}\")\n", " cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n", " \n", " # Step 2: Get file paths for clinical and genetic data\n", " clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", " \n", " # Step 3: Load the 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", " # Step 4: Print column names of clinical data\n", " print(\"Clinical data columns:\")\n", " print(clinical_df.columns.tolist())\n" ] }, { "cell_type": "markdown", "id": "1b7156cd", "metadata": {}, "source": [ "### Step 2: Find Candidate Demographic Features" ] }, { "cell_type": "code", "execution_count": 3, "id": "4ce443f7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:48:25.685713Z", "iopub.status.busy": "2025-03-25T03:48:25.685600Z", "iopub.status.idle": "2025-03-25T03:48:25.695704Z", "shell.execute_reply": "2025-03-25T03:48:25.695377Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age columns preview:\n", "{'age_at_initial_pathologic_diagnosis': [71.0, 82.0, 82.0, 46.0, 74.0], 'days_to_birth': [-26176.0, -30286.0, -30163.0, -17025.0, -27124.0], 'year_of_initial_pathologic_diagnosis': [2012.0, 2009.0, 2013.0, 2010.0, 2010.0]}\n", "\n", "Gender columns preview:\n", "{'gender': ['MALE', 'MALE', 'MALE', 'FEMALE', 'FEMALE']}\n" ] } ], "source": [ "# 1. Identify candidate demographic columns\n", "candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth', 'year_of_initial_pathologic_diagnosis']\n", "candidate_gender_cols = ['gender']\n", "\n", "# 2. Load the clinical data to preview\n", "cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Melanoma_(SKCM)')\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 the candidate columns for preview\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", "\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", "\n", "print(\"Age columns preview:\")\n", "print(age_preview)\n", "print(\"\\nGender columns preview:\")\n", "print(gender_preview)\n" ] }, { "cell_type": "markdown", "id": "d6b9284b", "metadata": {}, "source": [ "### Step 3: Select Demographic Features" ] }, { "cell_type": "code", "execution_count": 4, "id": "cf4c8b5b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:48:25.696925Z", "iopub.status.busy": "2025-03-25T03:48:25.696817Z", "iopub.status.idle": "2025-03-25T03:48:25.699402Z", "shell.execute_reply": "2025-03-25T03:48:25.699102Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected age column: age_at_initial_pathologic_diagnosis\n", "Selected gender column: gender\n" ] } ], "source": [ "# Analyze the available demographic information\n", "\n", "# Check age columns\n", "# We have 3 potential age columns:\n", "# 1. age_at_initial_pathologic_diagnosis - direct age values\n", "# 2. days_to_birth - negative days (can be converted to years by dividing by -365)\n", "# 3. year_of_initial_pathologic_diagnosis - not useful for age calculation without additional info\n", "\n", "# Check gender columns\n", "# We have only one gender column: 'gender'\n", "\n", "# Select the most appropriate columns\n", "age_col = 'age_at_initial_pathologic_diagnosis' # Direct age values are most useful\n", "gender_col = 'gender' # Only option available\n", "\n", "# Print chosen columns\n", "print(f\"Selected age column: {age_col}\")\n", "print(f\"Selected gender column: {gender_col}\")\n" ] }, { "cell_type": "markdown", "id": "80b05aff", "metadata": {}, "source": [ "### Step 4: Feature Engineering and Validation" ] }, { "cell_type": "code", "execution_count": 5, "id": "2554f72e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:48:25.700432Z", "iopub.status.busy": "2025-03-25T03:48:25.700327Z", "iopub.status.idle": "2025-03-25T03:48:39.476972Z", "shell.execute_reply": "2025-03-25T03:48:39.476439Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved clinical data with 481 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: (474, 19851) (samples x features)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "After handling missing values, data shape: (474, 19851)\n", "For the feature 'Red_Hair', the least common label is '0' with 1 occurrences. This represents 0.21% of the dataset.\n", "The distribution of the feature 'Red_Hair' in this dataset is severely biased.\n", "\n", "Quartiles for 'Age':\n", " 25%: 48.0\n", " 50% (Median): 58.0\n", " 75%: 70.75\n", "Min: 15.0\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 180 occurrences. This represents 37.97% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "Dataset was determined to be unusable and was not saved.\n" ] } ], "source": [ "# Step 1: Extract and standardize clinical features\n", "# Use the Melanoma directory identified in Step 1\n", "selected_dir = \"TCGA_Melanoma_(SKCM)\"\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 melanoma patients from TCGA, which is relevant for studying Red_Hair trait due to its association with melanoma risk.\"\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.\")" ] } ], "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 }