{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ebd87b48",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T06:11:45.820804Z"
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   },
   "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 = \"Pheochromocytoma_and_Paraganglioma\"\n",
    "cohort = \"GSE39716\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Pheochromocytoma_and_Paraganglioma\"\n",
    "in_cohort_dir = \"../../input/GEO/Pheochromocytoma_and_Paraganglioma/GSE39716\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Pheochromocytoma_and_Paraganglioma/GSE39716.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Pheochromocytoma_and_Paraganglioma/gene_data/GSE39716.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Pheochromocytoma_and_Paraganglioma/clinical_data/GSE39716.csv\"\n",
    "json_path = \"../../output/preprocess/Pheochromocytoma_and_Paraganglioma/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3050fb69",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e454d43",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:11:45.822602Z",
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     "iopub.status.idle": "2025-03-25T06:11:45.939636Z",
     "shell.execute_reply": "2025-03-25T06:11:45.939223Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Expression data from pheochromocytoma (PHEO) and paraganglioma (PGL) tumor samples\"\n",
      "!Series_summary\t\"Genotype specific differences in expression profiles have been evaluated using human HuGene1.0-ST Gene Chips. In this dataset we include expression data obtained from 8 normal adrenal medulla and 45 PHEOs/PGLs patient samples.\"\n",
      "!Series_summary\t\"Viable appearing tissue from the center of the lesions was collected and snap frozen for RNA extraction. Each of the 45 PHEO/PGL samples was examined by pathologist upon resection. Patients PKh_27 and PKh_28 with SDHB mutation were from the same patient with samples taken from two different locations at different times. Diagnosis of PHEO/PGL has been confired in all cases histopathologically. The tissues were grouped according to genetic/syndromic background and tumor location into SDHB (n = 18), SDHD-A/T (n = 6), SDHD-HN (n= 8), and VHL (n = 13).\"\n",
      "!Series_overall_design\t\"Microarray analysis was performed on normal and tumor samples. We used PAM model to identify minimum subset of genes selective for each mutation class. Heirarchical cluster analysis was used to identify samples with similar expression patterns. Data was validated using qRT-PCR analysis.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['age(yrs): 61', 'age(yrs): --', 'age(yrs): 53', 'age(yrs): 72', 'age(yrs): 65', 'age(yrs): 56', 'age(yrs): 62', 'age(yrs): 28.5', 'age(yrs): 24', 'age(yrs): 46', 'age(yrs): 9', 'age(yrs): 22.8', 'age(yrs): 26.6', 'age(yrs): 38.2', 'age(yrs): 45.6', 'age(yrs): 36', 'age(yrs): 36.2', 'age(yrs): 52', 'age(yrs): 12', 'age(yrs): 31', 'age(yrs): 55', 'age(yrs): 35', 'age(yrs): 34.8', 'age(yrs): 17', 'age(yrs): 30', 'age(yrs): 16', 'age(yrs): 61.4', 'age(yrs): 31.8', 'age(yrs): 32.6', 'age(yrs): 49.3'], 1: ['gender: male', 'gender: --', 'gender: female'], 2: ['tissue type: Normal', 'tissue type: paraganglioma (PGL) tumor', 'tissue type: pheochromocytoma (PHEO) tumor'], 3: ['mutation: none', 'mutation: SDHB', 'mutation: SDHD', 'mutation: VHL'], 4: ['tissue type: adrenal_medulla', 'tissue type: malignant_metastases', 'tissue type: extraadrenal_primary_malignant', 'tissue type: adrenal_primary_benign', 'tissue type: extraadrenal_multiple_benign', 'tissue type: extraadrenal_primary_benign', 'tissue type: adrenal_multiple_benign', 'tissue type: adrenal', 'tissue type: extraadrenal', 'tissue type: Head and neck', 'tissue type: bilateral_adrenal_benign']}\n"
     ]
    }
   ],
   "source": [
    "from tools.preprocess import *\n",
    "# 1. Identify the paths to the SOFT file and the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Read the matrix file to obtain background information and sample characteristics data\n",
    "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
    "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "\n",
    "# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "\n",
    "# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
    "print(\"Background Information:\")\n",
    "print(background_info)\n",
    "print(\"Sample Characteristics Dictionary:\")\n",
    "print(sample_characteristics_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546f854e",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e302cf01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:11:45.941034Z",
     "iopub.status.busy": "2025-03-25T06:11:45.940923Z",
     "iopub.status.idle": "2025-03-25T06:11:45.955301Z",
     "shell.execute_reply": "2025-03-25T06:11:45.954992Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{'GSM978080': [0.0, 61.0, 1.0], 'GSM978081': [0.0, nan, nan], 'GSM978082': [0.0, 53.0, 0.0], 'GSM978083': [0.0, 72.0, 0.0], 'GSM978084': [0.0, 72.0, 0.0], 'GSM978085': [0.0, 65.0, nan], 'GSM978086': [0.0, 56.0, 1.0], 'GSM978087': [0.0, 62.0, 1.0], 'GSM978088': [1.0, 28.5, 1.0], 'GSM978089': [1.0, 24.0, 1.0], 'GSM978090': [1.0, 46.0, 0.0], 'GSM978091': [1.0, 9.0, 0.0], 'GSM978092': [1.0, 22.8, 1.0], 'GSM978093': [1.0, 26.6, 1.0], 'GSM978094': [1.0, 38.2, 1.0], 'GSM978095': [1.0, 45.6, 0.0], 'GSM978096': [1.0, 36.0, 0.0], 'GSM978097': [1.0, 36.2, 0.0], 'GSM978098': [1.0, 52.0, 1.0], 'GSM978099': [1.0, 12.0, 1.0], 'GSM978100': [1.0, 31.0, 1.0], 'GSM978101': [1.0, 55.0, 1.0], 'GSM978102': [1.0, 35.0, 1.0], 'GSM978103': [1.0, 34.8, 0.0], 'GSM978104': [1.0, 17.0, 1.0], 'GSM978105': [1.0, 30.0, 0.0], 'GSM978106': [1.0, 16.0, 1.0], 'GSM978107': [1.0, 31.0, 0.0], 'GSM978108': [1.0, 26.6, 0.0], 'GSM978109': [1.0, 61.4, 1.0], 'GSM978110': [1.0, 31.8, 1.0], 'GSM978111': [1.0, 32.6, 1.0], 'GSM978112': [1.0, 49.3, 0.0], 'GSM978113': [1.0, 61.1, 0.0], 'GSM978114': [1.0, 26.6, 0.0], 'GSM978115': [1.0, 27.1, 0.0], 'GSM978116': [1.0, 48.0, 1.0], 'GSM978117': [1.0, 29.0, 0.0], 'GSM978118': [1.0, 24.3, 1.0], 'GSM978119': [1.0, 34.0, 0.0], 'GSM978120': [1.0, 42.8, 0.0], 'GSM978121': [1.0, 29.4, 1.0], 'GSM978122': [1.0, 42.6, 0.0], 'GSM978123': [1.0, 39.2, 1.0], 'GSM978124': [1.0, 30.0, 1.0], 'GSM978125': [1.0, 33.1, 1.0], 'GSM978126': [1.0, 18.9, 1.0], 'GSM978127': [1.0, 13.1, 1.0], 'GSM978128': [1.0, 28.6, 1.0], 'GSM978129': [1.0, 16.1, 1.0], 'GSM978130': [1.0, 22.7, 1.0], 'GSM978131': [1.0, 23.1, 1.0], 'GSM978132': [1.0, 24.8, 0.0]}\n",
      "Saved clinical data to ../../output/preprocess/Pheochromocytoma_and_Paraganglioma/clinical_data/GSE39716.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# Review the data to make educated decisions\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# The dataset mentions \"Expression data\" obtained using \"human HuGene1.0-ST Gene Chips\"\n",
    "# This appears to be a gene expression microarray dataset, not miRNA or methylation data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Trait: Pheochromocytoma_and_Paraganglioma\n",
    "# Looking at key 2 in the sample characteristics, we see tissue types:\n",
    "# 'tissue type: Normal', 'tissue type: paraganglioma (PGL) tumor', 'tissue type: pheochromocytoma (PHEO) tumor'\n",
    "# This clearly indicates trait data availability\n",
    "trait_row = 2\n",
    "\n",
    "# Define conversion function for trait\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value) or value == '--':\n",
    "        return None\n",
    "    \n",
    "    # Extract the part after colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on tissue type\n",
    "    if 'normal' in value.lower():\n",
    "        return 0  # Normal tissue\n",
    "    elif 'paraganglioma' in value.lower() or 'pheochromocytoma' in value.lower() or 'tumor' in value.lower():\n",
    "        return 1  # Disease tissue\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 2.2 Age data\n",
    "# Key 0 contains age data like 'age(yrs): 61', 'age(yrs): --', etc.\n",
    "age_row = 0\n",
    "\n",
    "def convert_age(value):\n",
    "    if pd.isna(value) or value == '--':\n",
    "        return None\n",
    "    \n",
    "    # Extract the part after colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to float if possible\n",
    "    try:\n",
    "        if value == '--':\n",
    "            return None\n",
    "        else:\n",
    "            return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "# 2.3 Gender data\n",
    "# Key 1 contains gender information: 'gender: male', 'gender: --', 'gender: female'\n",
    "gender_row = 1\n",
    "\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value) or value == '--':\n",
    "        return None\n",
    "    \n",
    "    # Extract the part after colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if value.lower() == 'female':\n",
    "        return 0\n",
    "    elif value.lower() == 'male':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial filtering and save metadata\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction (if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # Assuming clinical_data was obtained in a previous step\n",
    "    try:\n",
    "        # First, check if the clinical_data variable exists\n",
    "        clinical_data\n",
    "    except NameError:\n",
    "        # If clinical_data is not defined, we need to load it\n",
    "        # For GEO data, clinical information is typically in the Sample Characteristics section\n",
    "        # Let's try to construct a simple dataframe based on the information we have\n",
    "        sample_chars_dict = {0: ['age(yrs): 61', 'age(yrs): --', 'age(yrs): 53', 'age(yrs): 72', 'age(yrs): 65', 'age(yrs): 56', 'age(yrs): 62', 'age(yrs): 28.5', 'age(yrs): 24', 'age(yrs): 46', 'age(yrs): 9', 'age(yrs): 22.8', 'age(yrs): 26.6', 'age(yrs): 38.2', 'age(yrs): 45.6', 'age(yrs): 36', 'age(yrs): 36.2', 'age(yrs): 52', 'age(yrs): 12', 'age(yrs): 31', 'age(yrs): 55', 'age(yrs): 35', 'age(yrs): 34.8', 'age(yrs): 17', 'age(yrs): 30', 'age(yrs): 16', 'age(yrs): 61.4', 'age(yrs): 31.8', 'age(yrs): 32.6', 'age(yrs): 49.3'], 1: ['gender: male', 'gender: --', 'gender: female'], 2: ['tissue type: Normal', 'tissue type: paraganglioma (PGL) tumor', 'tissue type: pheochromocytoma (PHEO) tumor'], 3: ['mutation: none', 'mutation: SDHB', 'mutation: SDHD', 'mutation: VHL'], 4: ['tissue type: adrenal_medulla', 'tissue type: malignant_metastases', 'tissue type: extraadrenal_primary_malignant', 'tissue type: adrenal_primary_benign', 'tissue type: extraadrenal_multiple_benign', 'tissue type: extraadrenal_primary_benign', 'tissue type: adrenal_multiple_benign', 'tissue type: adrenal', 'tissue type: extraadrenal', 'tissue type: Head and neck', 'tissue type: bilateral_adrenal_benign']}\n",
    "        \n",
    "        # Assuming we don't have the actual clinical_data DataFrame yet,\n",
    "        # we'll need to load it from the appropriate file\n",
    "        \n",
    "        # Let's try to load from a potential clinical data file\n",
    "        clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.txt\")\n",
    "        \n",
    "        if os.path.exists(clinical_data_path):\n",
    "            clinical_data = pd.read_csv(clinical_data_path, sep='\\t')\n",
    "        else:\n",
    "            # If we don't have the file, we'll print a warning\n",
    "            print(\"Warning: clinical_data not found. Please make sure it was loaded in a previous step.\")\n",
    "            # Create a minimal representation based on the sample characteristics dictionary\n",
    "            # This is just a placeholder and might not be the correct structure\n",
    "            clinical_data = pd.DataFrame({key: value for key, value in sample_chars_dict.items()})\n",
    "    \n",
    "    # Extract and process clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the selected clinical features\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(preview)\n",
    "    \n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save the processed clinical data\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Saved clinical data to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8cbe94a",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b01c1202",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:11:45.956445Z",
     "iopub.status.busy": "2025-03-25T06:11:45.956334Z",
     "iopub.status.idle": "2025-03-25T06:11:46.117460Z",
     "shell.execute_reply": "2025-03-25T06:11:46.117074Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['7892501', '7892502', '7892503', '7892504', '7892505', '7892506',\n",
      "       '7892507', '7892508', '7892509', '7892510', '7892511', '7892512',\n",
      "       '7892513', '7892514', '7892515', '7892516', '7892517', '7892518',\n",
      "       '7892519', '7892520'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4e2e81",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0be2f90a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:11:46.118680Z",
     "iopub.status.busy": "2025-03-25T06:11:46.118568Z",
     "iopub.status.idle": "2025-03-25T06:11:46.120461Z",
     "shell.execute_reply": "2025-03-25T06:11:46.120181Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers appear to be probe IDs from a microarray platform, not human gene symbols.\n",
    "# They are numeric identifiers that likely need to be mapped to gene symbols for meaningful analysis.\n",
    "# Based on the '789xxxx' format, these appear to be Illumina BeadArray probe IDs, which require mapping.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bfcfcc",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7abf443",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:11:46.121555Z",
     "iopub.status.busy": "2025-03-25T06:11:46.121453Z",
     "iopub.status.idle": "2025-03-25T06:11:49.609200Z",
     "shell.execute_reply": "2025-03-25T06:11:49.608802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['7896736', '7896738', '7896740', '7896742', '7896744'], 'GB_LIST': [nan, nan, 'NM_001004195,NM_001005240,NM_001005484,BC136848,BC136867,BC136907,BC136908', 'NR_024437,XM_006711854,XM_006726377,XR_430662,AK298283,AL137655,BC032332,BC118988,BC122537,BC131690,NM_207366,AK301928,BC071667', 'NM_001005221,NM_001005224,NM_001005277,NM_001005504,BC137547,BC137568'], 'SPOT_ID': ['chr1:53049-54936', 'chr1:63015-63887', 'chr1:69091-70008', 'chr1:334129-334296', 'chr1:367659-368597'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': ['53049', '63015', '69091', '334129', '367659'], 'RANGE_STOP': ['54936', '63887', '70008', '334296', '368597'], 'total_probes': [7.0, 31.0, 24.0, 6.0, 36.0], 'gene_assignment': ['---', 'ENST00000328113 // OR4G2P // olfactory receptor, family 4, subfamily G, member 2 pseudogene // --- // --- /// ENST00000492842 // OR4G11P // olfactory receptor, family 4, subfamily G, member 11 pseudogene // --- // --- /// ENST00000588632 // OR4G1P // olfactory receptor, family 4, subfamily G, member 1 pseudogene // --- // ---', 'NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000318050 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// ENST00000326183 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000585993 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136848 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136867 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136907 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// BC136908 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682', 'NR_024437 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// XM_006711854 // LOC101060626 // F-box only protein 25-like // --- // 101060626 /// XM_006726377 // LOC101060626 // F-box only protein 25-like // --- // 101060626 /// XR_430662 // LOC101927097 // uncharacterized LOC101927097 // --- // 101927097 /// ENST00000279067 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// ENST00000431812 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000431812 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000433444 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000436899 // LINC00266-3 // long intergenic non-protein coding RNA 266-3 // --- // --- /// ENST00000445252 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// ENST00000455207 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000455207 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000455464 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000455464 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000456398 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000601814 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000601814 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// AK298283 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// AL137655 // LOC100134822 // uncharacterized LOC100134822 // --- // 100134822 /// BC032332 // PCMTD2 // protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing 2 // 20q13.33 // 55251 /// BC118988 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// BC122537 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// BC131690 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// NM_207366 // SEPT14 // septin 14 // 7p11.2 // 346288 /// ENST00000388975 // SEPT14 // septin 14 // 7p11.2 // 346288 /// ENST00000427373 // LINC00266-4P // long intergenic non-protein coding RNA 266-4, pseudogene // --- // --- /// ENST00000431796 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000509776 // LINC00266-2P // long intergenic non-protein coding RNA 266-2, pseudogene // --- // --- /// ENST00000570230 // LOC101929008 // uncharacterized LOC101929008 // --- // 101929008 /// ENST00000570230 // LOC101929038 // uncharacterized LOC101929038 // --- // 101929038 /// ENST00000570230 // LOC101930130 // uncharacterized LOC101930130 // --- // 101930130 /// ENST00000570230 // LOC101930567 // uncharacterized LOC101930567 // --- // 101930567 /// AK301928 // SEPT14 // septin 14 // 7p11.2 // 346288', 'NM_001005221 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// NM_001005224 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// NM_001005277 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// NM_001005504 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// ENST00000320901 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// ENST00000332831 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000332831 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000332831 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000402444 // OR4F7P // olfactory receptor, family 4, subfamily F, member 7 pseudogene // --- // --- /// ENST00000405102 // OR4F1P // olfactory receptor, family 4, subfamily F, member 1 pseudogene // --- // --- /// ENST00000424047 // OR4F2P // olfactory receptor, family 4, subfamily F, member 2 pseudogene // --- // --- /// ENST00000426406 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000426406 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000426406 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000456475 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000456475 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000456475 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000559128 // OR4F28P // olfactory receptor, family 4, subfamily F, member 28 pseudogene // --- // --- /// BC137547 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137547 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137547 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// BC137568 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137568 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137568 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000589943 // OR4F8P // olfactory receptor, family 4, subfamily F, member 8 pseudogene // --- // ---'], 'mrna_assignment': ['NONHSAT060105 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 7 // 7 // 0', 'ENST00000328113 // ENSEMBL // havana:known chromosome:GRCh38:15:101926805:101927707:-1 gene:ENSG00000183909 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000492842 // ENSEMBL // havana:known chromosome:GRCh38:1:62948:63887:1 gene:ENSG00000240361 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000588632 // ENSEMBL // havana:known chromosome:GRCh38:19:104535:105471:1 gene:ENSG00000267310 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT000016 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT051704 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT060106 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0', 'NM_001004195 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 4 (OR4F4), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005240 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 17 (OR4F17), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000318050 // ENSEMBL // ensembl:known chromosome:GRCh38:19:110643:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000326183 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:15:101922042:101923095:-1 gene:ENSG00000177693 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000335137 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000585993 // ENSEMBL // havana:known chromosome:GRCh38:19:107461:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136848 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136867 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168481 IMAGE:9020858), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136907 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136908 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168522 IMAGE:9020899), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000618231 // ENSEMBL // havana:known chromosome:GRCh38:19:110613:111417:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:retained_intron // chr1 // 100 // 88 // 21 // 21 // 0', 'NR_024437 // RefSeq // Homo sapiens uncharacterized LOC728323 (LOC728323), long non-coding RNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XM_006711854 // RefSeq // PREDICTED: Homo sapiens F-box only protein 25-like (LOC101060626), partial mRNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XM_006726377 // RefSeq // PREDICTED: Homo sapiens F-box only protein 25-like (LOC101060626), partial mRNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XR_430662 // RefSeq // PREDICTED: Homo sapiens uncharacterized LOC101927097 (LOC101927097), misc_RNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000279067 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:20:64290385:64303559:1 gene:ENSG00000149656 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000431812 // ENSEMBL // havana:known chromosome:GRCh38:1:485066:489553:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000433444 // ENSEMBL // havana:putative chromosome:GRCh38:2:242122293:242138888:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000436899 // ENSEMBL // havana:known chromosome:GRCh38:6:131910:144885:-1 gene:ENSG00000170590 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000445252 // ENSEMBL // havana:known chromosome:GRCh38:20:64294897:64311371:1 gene:ENSG00000149656 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455207 // ENSEMBL // havana:known chromosome:GRCh38:1:373182:485208:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455464 // ENSEMBL // havana:known chromosome:GRCh38:1:476531:497259:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000456398 // ENSEMBL // havana:known chromosome:GRCh38:2:242088633:242140638:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000601814 // ENSEMBL // havana:known chromosome:GRCh38:1:484832:495476:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// AK298283 // GenBank // Homo sapiens cDNA FLJ60027 complete cds, moderately similar to F-box only protein 25. // chr1 // 100 // 100 // 6 // 6 // 0 /// AL137655 // GenBank // Homo sapiens mRNA; cDNA DKFZp434B2016 (from clone DKFZp434B2016). // chr1 // 100 // 100 // 6 // 6 // 0 /// BC032332 // GenBank // Homo sapiens protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing 2, mRNA (cDNA clone MGC:40288 IMAGE:5169056), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC118988 // GenBank // Homo sapiens chromosome 20 open reading frame 69, mRNA (cDNA clone MGC:141807 IMAGE:40035995), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC122537 // GenBank // Homo sapiens chromosome 20 open reading frame 69, mRNA (cDNA clone MGC:141808 IMAGE:40035996), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC131690 // GenBank // Homo sapiens similar to bA476I15.3 (novel protein similar to septin), mRNA (cDNA clone IMAGE:40119684), partial cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// NM_207366 // RefSeq // Homo sapiens septin 14 (SEPT14), mRNA. // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000388975 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:7:55793544:55862789:-1 gene:ENSG00000154997 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000427373 // ENSEMBL // havana:known chromosome:GRCh38:Y:25378300:25394719:-1 gene:ENSG00000228786 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000431796 // ENSEMBL // havana:known chromosome:GRCh38:2:242088693:242122405:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 60 // 83 // 3 // 5 // 0 /// ENST00000509776 // ENSEMBL // havana:known chromosome:GRCh38:Y:24278681:24291346:1 gene:ENSG00000248792 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000570230 // ENSEMBL // havana:known chromosome:GRCh38:16:90157932:90178344:1 gene:ENSG00000260528 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 67 // 100 // 4 // 6 // 0 /// AK301928 // GenBank // Homo sapiens cDNA FLJ59065 complete cds, moderately similar to Septin-10. // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000413839 // ENSEMBL // havana:known chromosome:GRCh38:7:45816557:45821064:1 gene:ENSG00000226838 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000414688 // ENSEMBL // havana:known chromosome:GRCh38:1:711342:720200:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000419394 // ENSEMBL // havana:known chromosome:GRCh38:1:703685:720194:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000420830 // ENSEMBL // havana:known chromosome:GRCh38:1:243031272:243047869:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000428915 // ENSEMBL // havana:known chromosome:GRCh38:10:38453181:38466176:1 gene:ENSG00000203496 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000439401 // ENSEMBL // havana:known chromosome:GRCh38:3:198228194:198228376:1 gene:ENSG00000226008 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000440200 // ENSEMBL // havana:known chromosome:GRCh38:1:601436:720200:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000441245 // ENSEMBL // havana:known chromosome:GRCh38:1:701936:720150:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 67 // 4 // 4 // 0 /// ENST00000445840 // ENSEMBL // havana:known chromosome:GRCh38:1:485032:485211:-1 gene:ENSG00000224813 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000447954 // ENSEMBL // havana:known chromosome:GRCh38:1:720058:724550:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000450226 // ENSEMBL // havana:known chromosome:GRCh38:1:243038914:243047875:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000453405 // ENSEMBL // havana:known chromosome:GRCh38:2:242122287:242122469:1 gene:ENSG00000244528 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000477740 // ENSEMBL // havana:known chromosome:GRCh38:1:92230:129217:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000508026 // ENSEMBL // havana:known chromosome:GRCh38:8:200385:200562:-1 gene:ENSG00000255464 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000509192 // ENSEMBL // havana:known chromosome:GRCh38:5:181329241:181342213:1 gene:ENSG00000250765 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000513445 // ENSEMBL // havana:known chromosome:GRCh38:4:118640673:118640858:1 gene:ENSG00000251155 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000523795 // ENSEMBL // havana:known chromosome:GRCh38:8:192091:200563:-1 gene:ENSG00000250210 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000529266 // ENSEMBL // havana:known chromosome:GRCh38:11:121279:125784:-1 gene:ENSG00000254468 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000587432 // ENSEMBL // havana:known chromosome:GRCh38:19:191212:195696:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000610542 // ENSEMBL // ensembl:known chromosome:GRCh38:1:120725:133723:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000612088 // ENSEMBL // ensembl:known chromosome:GRCh38:10:38453181:38466176:1 gene:ENSG00000203496 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000612214 // ENSEMBL // havana:known chromosome:GRCh38:19:186371:191429:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000613471 // ENSEMBL // ensembl:known chromosome:GRCh38:1:476738:489710:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000615295 // ENSEMBL // ensembl:known chromosome:GRCh38:5:181329241:181342213:1 gene:ENSG00000250765 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000616585 // ENSEMBL // ensembl:known chromosome:GRCh38:1:711715:724707:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000618096 // ENSEMBL // havana:known chromosome:GRCh38:19:191178:191354:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000618222 // ENSEMBL // ensembl:known chromosome:GRCh38:6:131910:144885:-1 gene:ENSG00000170590 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000622435 // ENSEMBL // havana:known chromosome:GRCh38:2:242088684:242159382:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000622626 // ENSEMBL // ensembl:known chromosome:GRCh38:11:112967:125927:-1 gene:ENSG00000254468 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000007486 // ENSEMBL // cdna:genscan chromosome:GRCh38:2:242089132:242175655:1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000023775 // ENSEMBL // cdna:genscan chromosome:GRCh38:7:45812479:45856081:1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000045845 // ENSEMBL // cdna:genscan chromosome:GRCh38:8:166086:213433:-1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// BC071667 // GenBank HTC // Homo sapiens cDNA clone IMAGE:4384656, **** WARNING: chimeric clone ****. // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000053 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000055 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000063 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT000064 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000065 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000086 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000097 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 67 // 4 // 4 // 0 /// NONHSAT000098 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT010578 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT012829 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT017180 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT060112 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078034 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078035 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078039 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078040 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078041 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT081035 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT081036 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT094494 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT094497 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT098010 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT105956 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT105968 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT120472 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT124571 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00001800-XLOC_l2_001331 // Broad TUCP // linc-TP53BP2-4 chr1:-:224133091-224222680 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00001926-XLOC_l2_000004 // Broad TUCP // linc-OR4F16-1 chr1:+:329783-334271 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00001927-XLOC_l2_000004 // Broad TUCP // linc-OR4F16-1 chr1:+:334139-342806 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002370-XLOC_l2_000720 // Broad TUCP // linc-ZNF692-5 chr1:-:92229-129217 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00002386-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:637315-655530 // chr1 // 100 // 67 // 4 // 4 // 0 /// TCONS_l2_00002387-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:639064-655574 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002388-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:646721-655580 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002389-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:655437-659930 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00002812-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243194573-243211171 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00003949-XLOC_l2_001561 // Broad TUCP // linc-BMS1-9 chr10:+:38742108-38755311 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00003950-XLOC_l2_001561 // Broad TUCP // linc-BMS1-9 chr10:+:38742265-38764837 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014349-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030831-243101574 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014350-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030855-243102147 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014351-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030868-243101569 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014352-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030886-243064759 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014354-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030931-243067562 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014355-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030941-243102157 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014357-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243037045-243101538 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014358-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243058329-243064628 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015637-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030783-243082789 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015638-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243065243 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015639-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015640-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015641-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015643-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243064443-243081039 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00016828-XLOC_l2_008724 // Broad TUCP // linc-HNF1B-4 chr20:+:62921737-62934707 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00020055-XLOC_l2_010084 // Broad TUCP // linc-MCMBP-2 chr3:+:197937115-197955676 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025304-XLOC_l2_012836 // Broad TUCP // linc-PDCD2-1 chr6:-:131909-144885 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025849-XLOC_l2_013383 // Broad TUCP // linc-IGFBP1-1 chr7:+:45831387-45863181 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025850-XLOC_l2_013383 // Broad TUCP // linc-IGFBP1-1 chr7:+:45836951-45863174 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000437691 // ENSEMBL // havana:known chromosome:GRCh38:1:243047737:243052252:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000447236 // ENSEMBL // havana:known chromosome:GRCh38:7:56360362:56360541:-1 gene:ENSG00000231299 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000453576 // ENSEMBL // havana:known chromosome:GRCh38:1:129081:133566:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000611754 // ENSEMBL // ensembl:known chromosome:GRCh38:Y:25378671:25391610:-1 gene:ENSG00000228786 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000617978 // ENSEMBL // havana:known chromosome:GRCh38:1:227980051:227980227:1 gene:ENSG00000274886 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000621799 // ENSEMBL // ensembl:known chromosome:GRCh38:16:90173217:90186204:1 gene:ENSG00000260528 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT000022 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT010579 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT010580 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT120743 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 50 // 100 // 3 // 6 // 0 /// NONHSAT139746 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT144650 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT144655 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002372-XLOC_l2_000720 // Broad TUCP // linc-ZNF692-5 chr1:-:129080-133566 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002813-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243202215-243211826 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002814-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243211038-243215554 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00010440-XLOC_l2_005352 // Broad TUCP // linc-RBM11-5 chr16:+:90244124-90289080 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00031062-XLOC_l2_015962 // Broad TUCP // linc-BPY2B-4 chrY:-:27524446-27540866 // chr1 // 67 // 100 // 4 // 6 // 0', 'NM_001005221 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 29 (OR4F29), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005224 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 3 (OR4F3), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005277 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 16 (OR4F16), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005504 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 21 (OR4F21), mRNA. // chr1 // 89 // 100 // 32 // 36 // 0 /// ENST00000320901 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:8:166049:167043:-1 gene:ENSG00000176269 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 89 // 100 // 32 // 36 // 0 /// ENST00000332831 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:685716:686654:-1 gene:ENSG00000273547 gene_biotype:protein_coding transcript_biotype:protein_coding gene:ENSG00000185097 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000402444 // ENSEMBL // havana:known chromosome:GRCh38:6:170639606:170640536:1 gene:ENSG00000217874 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 78 // 100 // 28 // 36 // 0 /// ENST00000405102 // ENSEMBL // havana:known chromosome:GRCh38:6:105919:106856:-1 gene:ENSG00000220212 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 81 // 100 // 29 // 36 // 0 /// ENST00000424047 // ENSEMBL // havana:known chromosome:GRCh38:11:86649:87586:-1 gene:ENSG00000224777 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 78 // 100 // 28 // 36 // 0 /// ENST00000426406 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:450740:451678:-1 gene:ENSG00000278566 gene_biotype:protein_coding transcript_biotype:protein_coding gene:ENSG00000235249 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000456475 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:5:181367268:181368262:1 gene:ENSG00000230178 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000559128 // ENSEMBL // havana:known chromosome:GRCh38:15:101875964:101876901:1 gene:ENSG00000257109 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 100 // 30 // 36 // 0 /// BC137547 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169170 IMAGE:9021547), complete cds. // chr1 // 100 // 100 // 36 // 36 // 0 /// BC137568 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169191 IMAGE:9021568), complete cds. // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000589943 // ENSEMBL // havana:known chromosome:GRCh38:19:156279:157215:-1 gene:ENSG00000266971 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 72 // 100 // 26 // 36 // 0 /// GENSCAN00000011446 // ENSEMBL // cdna:genscan chromosome:GRCh38:5:181367527:181368225:1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000017675 // ENSEMBL // cdna:genscan chromosome:GRCh38:1:685716:686414:-1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000017679 // ENSEMBL // cdna:genscan chromosome:GRCh38:1:450740:451438:-1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000045845 // ENSEMBL // cdna:genscan chromosome:GRCh38:8:166086:213433:-1 transcript_biotype:protein_coding // chr1 // 87 // 83 // 26 // 30 // 0 /// NONHSAT051700 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 30 // 36 // 0 /// NONHSAT051701 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 30 // 36 // 0 /// NONHSAT105966 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 81 // 100 // 29 // 36 // 0 /// NONHSAT060109 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 72 // 100 // 26 // 36 // 0'], 'category': ['main', 'main', 'main', 'main', 'main']}\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238f6d40",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8afa1e74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:11:49.610631Z",
     "iopub.status.busy": "2025-03-25T06:11:49.610505Z",
     "iopub.status.idle": "2025-03-25T06:11:50.519849Z",
     "shell.execute_reply": "2025-03-25T06:11:50.519461Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data with human gene symbols:\n",
      "(117474, 53)\n",
      "First 10 gene symbols:\n",
      "['A-', 'A-3-', 'A-52', 'A-E', 'A-I', 'A-II', 'A-IV', 'A-V', 'A0', 'A1']\n"
     ]
    }
   ],
   "source": [
    "# Looking at the gene annotation, we need to determine the columns for mapping\n",
    "# We previously observed that our gene data has probe IDs like '7892501', '7892502', etc.\n",
    "# The gene annotation data has a similar column 'ID' which contains probe IDs like '7896736', '7896738', etc.\n",
    "\n",
    "# For gene symbols, we need to examine the 'gene_assignment' column as it contains gene information\n",
    "# The gene_assignment column contains gene information in the format:\n",
    "# \"NM_001005277 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399\"\n",
    "# where OR4F16 is the gene symbol\n",
    "\n",
    "# Let's extract the gene mapping from the annotation dataframe\n",
    "# 1. First, determine which columns to use\n",
    "probe_col = 'ID'  # This matches the index in gene_data\n",
    "gene_col = 'gene_assignment'  # This contains gene symbols embedded in text\n",
    "\n",
    "# 2. Get a gene mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene-symbol measurements\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Let's preview the converted gene expression data\n",
    "print(\"Gene expression data with human gene symbols:\")\n",
    "print(gene_data.shape)\n",
    "print(\"First 10 gene symbols:\")\n",
    "print(list(gene_data.index[:10]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14ebc96",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ca98585",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:11:50.521281Z",
     "iopub.status.busy": "2025-03-25T06:11:50.521164Z",
     "iopub.status.idle": "2025-03-25T06:12:05.091965Z",
     "shell.execute_reply": "2025-03-25T06:12:05.091430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Pheochromocytoma_and_Paraganglioma/gene_data/GSE39716.csv\n",
      "Clinical data shape: (3, 53)\n",
      "Clinical data columns: ['GSM978080', 'GSM978081', 'GSM978082', 'GSM978083', 'GSM978084'] ...\n",
      "Processed clinical data with shape: (53, 3)\n",
      "Linked data shape: (53, 24232)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after handling missing values: (53, 24232)\n",
      "For the feature 'Pheochromocytoma_and_Paraganglioma', the least common label is '0.0' with 8 occurrences. This represents 15.09% of the dataset.\n",
      "The distribution of the feature 'Pheochromocytoma_and_Paraganglioma' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 26.6\n",
      "  50% (Median): 32.6\n",
      "  75%: 46.0\n",
      "Min: 9.0\n",
      "Max: 72.0\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 21 occurrences. This represents 39.62% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Pheochromocytoma_and_Paraganglioma/GSE39716.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "\n",
    "# Save the 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",
    "\n",
    "# 2. Load the clinical data that was created in step 2\n",
    "clinical_data = pd.read_csv(out_clinical_data_file)\n",
    "\n",
    "# Diagnose the clinical data structure\n",
    "print(f\"Clinical data shape: {clinical_data.shape}\")\n",
    "print(\"Clinical data columns:\", clinical_data.columns.tolist()[:5], \"...\")  # Show first 5 columns to avoid clutter\n",
    "\n",
    "# The clinical data is in a format where sample IDs are columns\n",
    "# Transpose it to have samples as rows and features as columns\n",
    "clinical_df = clinical_data.T\n",
    "# Based on our implementation in step 2, we know the rows would be trait, age, gender\n",
    "clinical_df.columns = [trait, 'Age', 'Gender']\n",
    "\n",
    "print(f\"Processed clinical data with shape: {clinical_df.shape}\")\n",
    "\n",
    "# Link the clinical and genetic data\n",
    "linked_data = pd.merge(\n",
    "    clinical_df, \n",
    "    normalized_gene_data.T, \n",
    "    left_index=True, \n",
    "    right_index=True,\n",
    "    how='inner'\n",
    ")\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait and demographic features are severely biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct quality check and save the cohort information\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=True, \n",
    "    is_trait_available=True,\n",
    "    is_biased=is_trait_biased, \n",
    "    df=unbiased_linked_data,\n",
    "    note=f\"Dataset contains gene expression data for pheochromocytoma/paraganglioma samples with normal vs. tumor classification.\"\n",
    ")\n",
    "\n",
    "# 6. Save the data if it's usable\n",
    "if is_usable:\n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    # Save the data\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(f\"Data quality check failed. The dataset is not suitable for association studies.\")"
   ]
  }
 ],
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