{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8ec6e2b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:05:24.370947Z",
     "iopub.status.busy": "2025-03-25T07:05:24.370716Z",
     "iopub.status.idle": "2025-03-25T07:05:24.535141Z",
     "shell.execute_reply": "2025-03-25T07:05:24.534790Z"
    }
   },
   "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 = \"Canavan_Disease\"\n",
    "\n",
    "# Input paths\n",
    "tcga_root_dir = \"../../input/TCGA\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Canavan_Disease/TCGA.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Canavan_Disease/gene_data/TCGA.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Canavan_Disease/clinical_data/TCGA.csv\"\n",
    "json_path = \"../../output/preprocess/Canavan_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ba3914",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e900b224",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:05:24.536532Z",
     "iopub.status.busy": "2025-03-25T07:05:24.536395Z",
     "iopub.status.idle": "2025-03-25T07:05:25.830861Z",
     "shell.execute_reply": "2025-03-25T07:05:25.830473Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found potential match: TCGA_Lower_Grade_Glioma_(LGG)\n",
      "Selected as best match: TCGA_Lower_Grade_Glioma_(LGG)\n",
      "Selected directory: TCGA_Lower_Grade_Glioma_(LGG)\n",
      "Clinical file: TCGA.LGG.sampleMap_LGG_clinicalMatrix\n",
      "Genetic file: TCGA.LGG.sampleMap_HiSeqV2_PANCAN.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clinical data columns:\n",
      "['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'additional_surgery_locoregional_procedure', 'additional_surgery_metastatic_procedure', 'age_at_initial_pathologic_diagnosis', 'animal_insect_allergy_history', 'animal_insect_allergy_types', 'asthma_history', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'days_to_additional_surgery_locoregional_procedure', 'days_to_additional_surgery_metastatic_procedure', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_after_initial_treatment', 'days_to_performance_status_assessment', 'eastern_cancer_oncology_group', 'eczema_history', 'family_history_of_cancer', 'family_history_of_primary_brain_tumor', 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust', 'first_diagnosis_age_of_animal_insect_allergy', 'first_diagnosis_age_of_food_allergy', 'first_presenting_symptom', 'first_presenting_symptom_longest_duration', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'food_allergy_history', 'food_allergy_types', 'form_completion_date', 'gender', 'hay_fever_history', 'headache_history', 'histological_type', 'history_ionizing_rt_to_head', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'inherited_genetic_syndrome_found', 'inherited_genetic_syndrome_result', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'karnofsky_performance_score', 'laterality', 'ldh1_mutation_found', 'ldh1_mutation_test_method', 'ldh1_mutation_tested', 'longest_dimension', 'lost_follow_up', 'mental_status_changes', 'mold_or_dust_allergy_history', 'motor_movement_changes', 'neoplasm_histologic_grade', 'new_tumor_event_after_initial_treatment', 'oct_embedded', 'other_dx', 'pathology_report_file_name', 'patient_id', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'preoperative_antiseizure_meds', 'preoperative_corticosteroids', 'primary_therapy_outcome_success', 'radiation_therapy', 'sample_type', 'sample_type_id', 'seizure_history', 'sensory_changes', 'shortest_dimension', 'supratentorial_localization', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_location', 'tumor_tissue_site', 'vial_number', 'visual_changes', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_LGG_mutation', '_GENOMIC_ID_TCGA_LGG_PDMRNAseq', '_GENOMIC_ID_TCGA_LGG_RPPA', '_GENOMIC_ID_TCGA_LGG_mutation_broad_gene', '_GENOMIC_ID_TCGA_LGG_gistic2', '_GENOMIC_ID_TCGA_LGG_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_LGG_miRNA_HiSeq', '_GENOMIC_ID_TCGA_LGG_PDMarrayCNV', '_GENOMIC_ID_data/public/TCGA/LGG/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_LGG_mutation_curated_broad_gene', '_GENOMIC_ID_TCGA_LGG_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_LGG_hMethyl450_MethylMix', '_GENOMIC_ID_TCGA_LGG_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_LGG_mutation_bcm_gene', '_GENOMIC_ID_TCGA_LGG_hMethyl450', '_GENOMIC_ID_TCGA_LGG_PDMarray', '_GENOMIC_ID_TCGA_LGG_exp_HiSeqV2', '_GENOMIC_ID_TCGA_LGG_G4502A_07_3', '_GENOMIC_ID_TCGA_LGG_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_LGG_gistic2thd', '_GENOMIC_ID_TCGA_LGG_mutation_ucsc_maf_gene']\n",
      "\n",
      "Clinical data shape: (530, 113)\n",
      "Genetic data shape: (20530, 530)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# 1. Find the most relevant directory for Canavan Disease\n",
    "subdirectories = os.listdir(tcga_root_dir)\n",
    "target_trait = trait.lower().replace(\"_\", \" \")  # Convert to lowercase for case-insensitive matching\n",
    "\n",
    "# Search for related terms to Canavan Disease\n",
    "related_terms = [\"canavan\", \"leukodystrophy\", \"aspa\", \"neurological\", \"brain\", \"cns\", \"central nervous system\", \"glioma\"]\n",
    "matched_dir = None\n",
    "\n",
    "for subdir in subdirectories:\n",
    "    subdir_lower = subdir.lower()\n",
    "    # Check if any related term is in the directory name\n",
    "    if any(term in subdir_lower for term in related_terms):\n",
    "        matched_dir = subdir\n",
    "        print(f\"Found potential match: {subdir}\")\n",
    "        # Prioritize more specific matches\n",
    "        if \"glioma\" in subdir_lower or \"gbm\" in subdir_lower:\n",
    "            print(f\"Selected as best match: {subdir}\")\n",
    "            matched_dir = subdir\n",
    "            break\n",
    "\n",
    "# If we found a potential match, use it\n",
    "if matched_dir:\n",
    "    print(f\"Selected directory: {matched_dir}\")\n",
    "    \n",
    "    # 2. Get the clinical and genetic data file paths\n",
    "    cohort_dir = os.path.join(tcga_root_dir, matched_dir)\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 matched_dir:\n",
    "    print(\"Skipping this trait as no suitable data was found.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb79a41",
   "metadata": {},
   "source": [
    "### Step 2: Find Candidate Demographic Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3503fa77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:05:25.832225Z",
     "iopub.status.busy": "2025-03-25T07:05:25.832099Z",
     "iopub.status.idle": "2025-03-25T07:05:25.847133Z",
     "shell.execute_reply": "2025-03-25T07:05:25.846813Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age columns preview:\n",
      "{'age_at_initial_pathologic_diagnosis': [44.0, 50.0, 59.0, 56.0, 40.0], 'days_to_birth': [-16179.0, -18341.0, -21617.0, -20516.0, -14806.0], 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust': [nan, nan, nan, nan, nan], 'first_diagnosis_age_of_animal_insect_allergy': [nan, nan, nan, nan, nan], 'first_diagnosis_age_of_food_allergy': [nan, nan, nan, nan, nan]}\n",
      "\n",
      "Gender columns preview:\n",
      "{'gender': ['FEMALE', 'MALE', 'MALE', 'FEMALE', 'FEMALE']}\n"
     ]
    }
   ],
   "source": [
    "# Identify candidate columns for age and gender\n",
    "candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth', 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust', \n",
    "                     'first_diagnosis_age_of_animal_insect_allergy', 'first_diagnosis_age_of_food_allergy']\n",
    "candidate_gender_cols = ['gender']\n",
    "\n",
    "# Read the clinical data file\n",
    "cohort_dir = os.path.join(tcga_root_dir, \"TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)\")\n",
    "clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
    "clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
    "\n",
    "# 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",
    "\n",
    "# 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",
    "\n",
    "print(\"Age columns preview:\")\n",
    "print(age_preview)\n",
    "print(\"\\nGender columns preview:\")\n",
    "print(gender_preview)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3310c2",
   "metadata": {},
   "source": [
    "### Step 3: Select Demographic Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e6c02dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:05:25.848338Z",
     "iopub.status.busy": "2025-03-25T07:05:25.848226Z",
     "iopub.status.idle": "2025-03-25T07:05:25.851493Z",
     "shell.execute_reply": "2025-03-25T07:05:25.851209Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected age column: age_at_initial_pathologic_diagnosis\n",
      "Age column preview: [44.0, 50.0, 59.0, 56.0, 40.0]\n",
      "Selected gender column: gender\n",
      "Gender column preview: ['FEMALE', 'MALE', 'MALE', 'FEMALE', 'FEMALE']\n"
     ]
    }
   ],
   "source": [
    "# Inspecting the age columns\n",
    "age_columns = {\n",
    "    'age_at_initial_pathologic_diagnosis': [44.0, 50.0, 59.0, 56.0, 40.0], \n",
    "    'days_to_birth': [-16179.0, -18341.0, -21617.0, -20516.0, -14806.0], \n",
    "    'first_diagnosis_age_asth_ecz_hay_fev_mold_dust': [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')], \n",
    "    'first_diagnosis_age_of_animal_insect_allergy': [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')], \n",
    "    'first_diagnosis_age_of_food_allergy': [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')]\n",
    "}\n",
    "\n",
    "# Inspecting the gender columns\n",
    "gender_columns = {\n",
    "    'gender': ['FEMALE', 'MALE', 'MALE', 'FEMALE', 'FEMALE']\n",
    "}\n",
    "\n",
    "# Select the best age column\n",
    "# 'age_at_initial_pathologic_diagnosis' has actual age values in years (preferred)\n",
    "# 'days_to_birth' has negative values representing days since birth, which would need conversion\n",
    "# The other columns have all NaN values\n",
    "\n",
    "age_col = 'age_at_initial_pathologic_diagnosis'\n",
    "\n",
    "# Select the best gender column\n",
    "# Only one column is available and it has valid values\n",
    "gender_col = 'gender'\n",
    "\n",
    "# Print the selected columns\n",
    "print(f\"Selected age column: {age_col}\")\n",
    "print(f\"Age column preview: {age_columns[age_col]}\")\n",
    "print(f\"Selected gender column: {gender_col}\")\n",
    "print(f\"Gender column preview: {gender_columns[gender_col]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d4a5e2f",
   "metadata": {},
   "source": [
    "### Step 4: Feature Engineering and Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2bf2559",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:05:25.852670Z",
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     "shell.execute_reply": "2025-03-25T07:05:39.076929Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene expression data saved to ../../output/preprocess/Canavan_Disease/gene_data/TCGA.csv\n",
      "Gene expression data shape after normalization: (19848, 530)\n",
      "Clinical data saved to ../../output/preprocess/Canavan_Disease/clinical_data/TCGA.csv\n",
      "Clinical data shape: (1148, 3)\n",
      "Number of samples in clinical data: 1148\n",
      "Number of samples in genetic data: 530\n",
      "Number of common samples: 530\n",
      "Linked data shape: (530, 19851)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (530, 19851)\n",
      "Quartiles for 'Canavan_Disease':\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 'Canavan_Disease' in this dataset is severely biased.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 32.0\n",
      "  50% (Median): 41.0\n",
      "  75%: 53.0\n",
      "Min: 14.0\n",
      "Max: 87.0\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 238 occurrences. This represents 44.91% 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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