{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "197031cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:30.834728Z",
     "iopub.status.busy": "2025-03-25T06:18:30.834535Z",
     "iopub.status.idle": "2025-03-25T06:18:31.002182Z",
     "shell.execute_reply": "2025-03-25T06:18:31.001839Z"
    }
   },
   "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 = \"Acute_Myeloid_Leukemia\"\n",
    "cohort = \"GSE235070\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Acute_Myeloid_Leukemia\"\n",
    "in_cohort_dir = \"../../input/GEO/Acute_Myeloid_Leukemia/GSE235070\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/GSE235070.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE235070.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE235070.csv\"\n",
    "json_path = \"../../output/preprocess/Acute_Myeloid_Leukemia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa49416",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cc7c5ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:31.003603Z",
     "iopub.status.busy": "2025-03-25T06:18:31.003460Z",
     "iopub.status.idle": "2025-03-25T06:18:31.050153Z",
     "shell.execute_reply": "2025-03-25T06:18:31.049855Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Ferritinophagy is a Druggable Vulnerability of Quiescent Leukemic Stem Cells\"\n",
      "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "!Series_overall_design\t\"Refer to individual Series\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['disease state: patient with AML']}\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": "574fa913",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f655195c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:31.051176Z",
     "iopub.status.busy": "2025-03-25T06:18:31.051072Z",
     "iopub.status.idle": "2025-03-25T06:18:31.059996Z",
     "shell.execute_reply": "2025-03-25T06:18:31.059719Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{'GSM7494217': [1.0], 'GSM7494218': [1.0], 'GSM7494219': [1.0], 'GSM7494220': [1.0], 'GSM7494221': [1.0], 'GSM7494222': [1.0], 'GSM7494223': [1.0], 'GSM7494224': [1.0], 'GSM7494249': [1.0], 'GSM7494250': [1.0], 'GSM7494251': [1.0], 'GSM7494252': [1.0], 'GSM7494253': [1.0], 'GSM7494254': [1.0], 'GSM7494255': [1.0], 'GSM7494256': [1.0], 'GSM7494509': [1.0], 'GSM7494510': [1.0], 'GSM7494511': [1.0], 'GSM7494512': [1.0], 'GSM7494513': [1.0], 'GSM7494514': [1.0], 'GSM7494515': [1.0], 'GSM7494516': [1.0], 'GSM7494517': [1.0], 'GSM7494518': [1.0], 'GSM7494519': [1.0], 'GSM7494520': [1.0], 'GSM7494521': [1.0], 'GSM7494522': [1.0], 'GSM7494523': [1.0], 'GSM7494524': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE235070.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "from typing import Optional, Callable, Dict, Any, List\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this appears to be primarily about leukemic stem cells.\n",
    "# However, without more specific information about the data structure, we should be cautious.\n",
    "is_gene_available = True  # Assuming gene expression data is available for AML studies\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "trait_row = 0  # The dataset has AML disease state information (row 0)\n",
    "age_row = None  # Age information is not provided in the sample characteristics\n",
    "gender_row = None  # Gender information is not provided in the sample characteristics\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert AML trait value to binary (1 = has AML, 0 = control)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part (after colon if present)\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Check if the value indicates AML\n",
    "    if 'AML' in value.upper() or 'ACUTE MYELOID LEUKEMIA' in value.upper() or 'patient' in value.lower():\n",
    "        return 1\n",
    "    elif 'control' in value.lower() or 'healthy' in value.lower() or 'normal' in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to numerical value\"\"\"\n",
    "    # This function is included for completeness, but age data is not available\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part (after colon if present)\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to extract numerical age\n",
    "    try:\n",
    "        # Extract digits if embedded in text\n",
    "        import re\n",
    "        age_match = re.search(r'(\\d+)', value)\n",
    "        if age_match:\n",
    "            return float(age_match.group(1))\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0 = female, 1 = male)\"\"\"\n",
    "    # This function is included for completeness, but gender data is not available\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part (after colon if present)\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    \n",
    "    if isinstance(value, str):\n",
    "        value = value.lower()\n",
    "        if 'female' in value or 'f' == value:\n",
    "            return 0\n",
    "        elif 'male' in value or 'm' == value:\n",
    "            return 1\n",
    "    \n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata - Initial filtering\n",
    "is_trait_available = trait_row is not None\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\n",
    "if trait_row is not None:\n",
    "    # Assuming clinical_data is available from previous steps\n",
    "    try:\n",
    "        # Check if clinical_data is available\n",
    "        if 'clinical_data' in locals() or 'clinical_data' in globals():\n",
    "            # Extract 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 if age_row is not None else None,\n",
    "                gender_row=gender_row,\n",
    "                convert_gender=convert_gender if gender_row is not None else None\n",
    "            )\n",
    "            \n",
    "            # Preview the extracted clinical features\n",
    "            preview = preview_df(selected_clinical_df)\n",
    "            print(\"Preview of selected clinical features:\")\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 extracted clinical features\n",
    "            selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "            print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "        else:\n",
    "            print(\"Clinical data not available from previous steps\")\n",
    "    except NameError:\n",
    "        print(\"Clinical data not available from previous steps\")\n",
    "else:\n",
    "    print(\"Trait data not available, skipping clinical feature extraction\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f3e36d",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad267703",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:31.060961Z",
     "iopub.status.busy": "2025-03-25T06:18:31.060858Z",
     "iopub.status.idle": "2025-03-25T06:18:31.124991Z",
     "shell.execute_reply": "2025-03-25T06:18:31.124668Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1',\n",
      "       'TC0100006480.hg.1', 'TC0100006483.hg.1', 'TC0100006486.hg.1',\n",
      "       'TC0100006490.hg.1', 'TC0100006492.hg.1', 'TC0100006494.hg.1',\n",
      "       'TC0100006497.hg.1', 'TC0100006499.hg.1', 'TC0100006501.hg.1',\n",
      "       'TC0100006502.hg.1', 'TC0100006514.hg.1', 'TC0100006516.hg.1',\n",
      "       'TC0100006517.hg.1', 'TC0100006524.hg.1', 'TC0100006540.hg.1',\n",
      "       'TC0100006548.hg.1', 'TC0100006550.hg.1'],\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": "869602a5",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4045d6af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:31.126184Z",
     "iopub.status.busy": "2025-03-25T06:18:31.126074Z",
     "iopub.status.idle": "2025-03-25T06:18:31.127870Z",
     "shell.execute_reply": "2025-03-25T06:18:31.127599Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers \"TC0100006437.hg.1\" appear to be Affymetrix transcript cluster IDs \n",
    "# from a microarray platform, not standard human gene symbols.\n",
    "# They need to be mapped to human gene symbols for better interpretability and consistency.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b6a7d6",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "470c22fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:31.128842Z",
     "iopub.status.busy": "2025-03-25T06:18:31.128743Z",
     "iopub.status.idle": "2025-03-25T06:18:33.030702Z",
     "shell.execute_reply": "2025-03-25T06:18:33.030327Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1', 'TC0100006480.hg.1', 'TC0100006483.hg.1'], 'probeset_id': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1', 'TC0100006480.hg.1', 'TC0100006483.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+', '+', '+'], 'start': ['69091', '924880', '960587', '966497', '1001138'], 'stop': ['70008', '944581', '965719', '975865', '1014541'], 'total_probes': [10.0, 10.0, 10.0, 10.0, 10.0], 'category': ['main', 'main', 'main', 'main', 'main'], 'SPOT_ID': ['Coding', 'Multiple_Complex', 'Multiple_Complex', 'Multiple_Complex', 'Multiple_Complex'], 'SPOT_ID.1': ['NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // olfactory receptor, family 4, subfamily F, member 5 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // olfactory receptor, family 4, subfamily F, member 5[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30547.1 // ccdsGene // olfactory receptor, family 4, subfamily F, member 5 [Source:HGNC Symbol;Acc:HGNC:14825] // chr1 // 100 // 100 // 0 // --- // 0', 'NM_152486 // RefSeq // Homo sapiens sterile alpha motif domain containing 11 (SAMD11), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000341065 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000342066 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000420190 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000437963 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000455979 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000464948 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466827 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000474461 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000478729 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616016 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616125 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617307 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618181 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618323 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618779 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000620200 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622503 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// BC024295 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:39333 IMAGE:3354502), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// BC033213 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:45873 IMAGE:5014368), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097860 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097862 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097863 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097865 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097867 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097868 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000276866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000316521 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS2.2 // ccdsGene // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009185 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009186 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009187 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009188 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009189 // circbase // Salzman2013 ALT_DONOR, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009190 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009191 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009192 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009193 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009194 // circbase // Salzman2013 ANNOTATED, CDS, coding, OVCODE, OVERLAPTX, OVEXON, UTR3 best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009195 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001abw.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pjt.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pju.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkg.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkh.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkk.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkm.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pko.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axs.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axt.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axu.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axv.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axw.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axx.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axy.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axz.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057aya.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0', 'NM_198317 // RefSeq // Homo sapiens kelch-like family member 17 (KLHL17), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000338591 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000463212 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466300 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000481067 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622660 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097875 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097877 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097878 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097931 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// BC166618 // GenBank // Synthetic construct Homo sapiens clone IMAGE:100066344, MGC:195481 kelch-like 17 (Drosophila) (KLHL17) mRNA, encodes complete protein. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30550.1 // ccdsGene // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009209 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_198317 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aca.3 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001acb.2 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayg.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayh.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayi.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayj.1 // UCSC Genes // N/A // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617073 // ENSEMBL // ncrna:novel chromosome:GRCh38:1:965110:965166:1 gene:ENSG00000277294 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0', 'NM_001160184 // RefSeq // Homo sapiens pleckstrin homology domain containing, family N member 1 (PLEKHN1), transcript variant 2, mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// NM_032129 // RefSeq // Homo sapiens pleckstrin homology domain containing, family N member 1 (PLEKHN1), transcript variant 1, mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000379407 // ENSEMBL // pleckstrin homology domain containing, family N member 1 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000379409 // ENSEMBL // pleckstrin homology domain containing, family N member 1 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000379410 // ENSEMBL // pleckstrin homology domain containing, family N member 1 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000480267 // ENSEMBL // pleckstrin homology domain containing, family N member 1 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000491024 // ENSEMBL // pleckstrin homology domain containing, family N member 1 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// BC101386 // GenBank // Homo sapiens pleckstrin homology domain containing, family N member 1, mRNA (cDNA clone MGC:120613 IMAGE:40026400), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// BC101387 // GenBank // Homo sapiens pleckstrin homology domain containing, family N member 1, mRNA (cDNA clone MGC:120616 IMAGE:40026404), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097940 // Havana transcript // pleckstrin homology domain containing, family N member 1[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097941 // Havana transcript // pleckstrin homology domain containing, family N member 1[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097942 // Havana transcript // pleckstrin homology domain containing, family N member 1[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000473255 // Havana transcript // pleckstrin homology domain containing, family N member 1[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000473256 // Havana transcript // pleckstrin homology domain containing, family N member 1[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS4.1 // ccdsGene // pleckstrin homology domain containing, family N member 1 [Source:HGNC Symbol;Acc:HGNC:25284] // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS53256.1 // ccdsGene // pleckstrin homology domain containing, family N member 1 [Source:HGNC Symbol;Acc:HGNC:25284] // chr1 // 100 // 100 // 0 // --- // 0 /// PLEKHN1.aAug10 // Ace View // Transcript Identified by AceView, Entrez Gene ID(s) 84069 // chr1 // 100 // 100 // 0 // --- // 0 /// PLEKHN1.bAug10 // Ace View // Transcript Identified by AceView, Entrez Gene ID(s) 84069, RefSeq ID(s) NM_032129 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001acd.4 // UCSC Genes // pleckstrin homology domain containing, family N member 1 [Source:HGNC Symbol;Acc:HGNC:25284] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001ace.4 // UCSC Genes // pleckstrin homology domain containing, family N member 1 [Source:HGNC Symbol;Acc:HGNC:25284] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001acf.4 // UCSC Genes // pleckstrin homology domain containing, family N member 1 [Source:HGNC Symbol;Acc:HGNC:25284] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayk.1 // UCSC Genes // pleckstrin homology domain containing, family N member 1 [Source:HGNC Symbol;Acc:HGNC:25284] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayl.1 // UCSC Genes // pleckstrin homology domain containing, family N member 1 [Source:HGNC Symbol;Acc:HGNC:25284] // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000217 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000217 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0', 'NM_005101 // RefSeq // Homo sapiens ISG15 ubiquitin-like modifier (ISG15), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000379389 // ENSEMBL // ISG15 ubiquitin-like modifier [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000624652 // ENSEMBL // ISG15 ubiquitin-like modifier [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000624697 // ENSEMBL // ISG15 ubiquitin-like modifier [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// BC009507 // GenBank // Homo sapiens ISG15 ubiquitin-like modifier, mRNA (cDNA clone MGC:3945 IMAGE:3545944), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097989 // Havana transcript // ISG15 ubiquitin-like modifier[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000479384 // Havana transcript // ISG15 ubiquitin-like modifier[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000479385 // Havana transcript // ISG15 ubiquitin-like modifier[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS6.1 // ccdsGene // ISG15 ubiquitin-like modifier [Source:HGNC Symbol;Acc:HGNC:4053] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009211 // circbase // Salzman2013 ANNOTATED, CDS, coding, OVCODE, OVEXON, UTR3 best transcript NM_005101 // chr1 // 100 // 100 // 0 // --- // 0 /// ISG15.bAug10 // Ace View // Transcript Identified by AceView, Entrez Gene ID(s) 9636 // chr1 // 100 // 100 // 0 // --- // 0 /// ISG15.cAug10 // Ace View // Transcript Identified by AceView, Entrez Gene ID(s) 9636 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001acj.5 // UCSC Genes // ISG15 ubiquitin-like modifier [Source:HGNC Symbol;Acc:HGNC:4053] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayq.1 // UCSC Genes // ISG15 ubiquitin-like modifier [Source:HGNC Symbol;Acc:HGNC:4053] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayr.1 // UCSC Genes // ISG15 ubiquitin-like modifier [Source:HGNC Symbol;Acc:HGNC:4053] // chr1 // 100 // 100 // 0 // --- // 0']}\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": "c04d3293",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "60741312",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:33.031974Z",
     "iopub.status.busy": "2025-03-25T06:18:33.031855Z",
     "iopub.status.idle": "2025-03-25T06:18:35.400654Z",
     "shell.execute_reply": "2025-03-25T06:18:35.400197Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data shape (probes): 85633 genes × 32 samples\n",
      "First 5 gene symbols after mapping:\n",
      "Index(['A-', 'A-1', 'A-2', 'A-52', 'A-E'], dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Analyze the gene annotation dataframe to identify columns containing probe IDs and gene symbols\n",
    "# From the preview, we can see that 'ID' matches the gene identifiers in gene_data's index (TC01... format)\n",
    "# For gene symbols, we need to extract them from the 'SPOT_ID.1' column which contains transcript info\n",
    "\n",
    "# 2. Create the gene mapping dataframe using the appropriate columns\n",
    "# ID column already matches the probe IDs\n",
    "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'SPOT_ID.1')\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level data to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print information about the mapped data\n",
    "print(f\"Original data shape (probes): {len(gene_data.index)} genes × {len(gene_data.columns)} samples\")\n",
    "print(\"First 5 gene symbols after mapping:\")\n",
    "print(gene_data.index[:5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0795b6f3",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5fe389ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:35.402168Z",
     "iopub.status.busy": "2025-03-25T06:18:35.402059Z",
     "iopub.status.idle": "2025-03-25T06:18:41.818178Z",
     "shell.execute_reply": "2025-03-25T06:18:41.817853Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE235070.csv\n",
      "Clinical data shape: (1, 33)\n",
      "Sample characteristics dictionary:\n",
      "{0: ['disease state: patient with AML']}\n",
      "Clinical data preview:\n",
      "{'GSM7494217': [1.0], 'GSM7494218': [1.0], 'GSM7494219': [1.0], 'GSM7494220': [1.0], 'GSM7494221': [1.0], 'GSM7494222': [1.0], 'GSM7494223': [1.0], 'GSM7494224': [1.0], 'GSM7494249': [1.0], 'GSM7494250': [1.0], 'GSM7494251': [1.0], 'GSM7494252': [1.0], 'GSM7494253': [1.0], 'GSM7494254': [1.0], 'GSM7494255': [1.0], 'GSM7494256': [1.0], 'GSM7494509': [1.0], 'GSM7494510': [1.0], 'GSM7494511': [1.0], 'GSM7494512': [1.0], 'GSM7494513': [1.0], 'GSM7494514': [1.0], 'GSM7494515': [1.0], 'GSM7494516': [1.0], 'GSM7494517': [1.0], 'GSM7494518': [1.0], 'GSM7494519': [1.0], 'GSM7494520': [1.0], 'GSM7494521': [1.0], 'GSM7494522': [1.0], 'GSM7494523': [1.0], 'GSM7494524': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE235070.csv\n",
      "Linked data shape: (32, 19976)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (32, 19976)\n",
      "Quartiles for 'Acute_Myeloid_Leukemia':\n",
      "  25%: 1.0\n",
      "  50% (Median): 1.0\n",
      "  75%: 1.0\n",
      "Min: 1.0\n",
      "Max: 1.0\n",
      "The distribution of the feature 'Acute_Myeloid_Leukemia' in this dataset is severely biased.\n",
      "\n",
      "Dataset not usable due to bias in trait distribution. Data not saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(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",
    "# Let's first check what's actually in the clinical_data to avoid errors\n",
    "print(\"Clinical data shape:\", clinical_data.shape)\n",
    "print(\"Sample characteristics dictionary:\")\n",
    "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "print(sample_characteristics_dict)\n",
    "\n",
    "# Define the trait conversion function based on the actual available data\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert AML status to binary (1 = has AML, 0 = control/healthy)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part (after colon if present)\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # In this dataset, all samples appear to be AML patients\n",
    "    if 'AML' in value.upper() or 'patient' in value.lower():\n",
    "        return 1\n",
    "    elif 'control' in value.lower() or 'healthy' in value.lower() or 'normal' in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Use the correct row index based on the sample characteristics dictionary\n",
    "trait_row = 0  # The only row available (disease state: patient with AML)\n",
    "age_row = None  # Age information not available\n",
    "gender_row = None  # Gender information not available\n",
    "\n",
    "# Check if clinical_data actually contains data before proceeding\n",
    "if clinical_data.shape[0] > 0:\n",
    "    # Extract clinical features\n",
    "    selected_clinical_data = 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=None,\n",
    "        convert_age=None,\n",
    "        gender_row=None,\n",
    "        convert_gender=None\n",
    "    )\n",
    "    \n",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview_df(selected_clinical_data))\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_data.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "    # 2. Link the clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)\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\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "    # 4. Determine whether the trait and some 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",
    "    note = \"Dataset contains only AML patients without controls, which may limit its utility for some analyses\"\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=note\n",
    "    )\n",
    "\n",
    "    # 6. If the linked data is usable, save it\n",
    "    if is_usable:\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        unbiased_linked_data.to_csv(out_data_file)\n",
    "        print(f\"Processed dataset saved to {out_data_file}\")\n",
    "    else:\n",
    "        print(\"Dataset not usable due to bias in trait distribution. Data not saved.\")\n",
    "else:\n",
    "    print(\"No clinical data available. Cannot proceed with linking and subsequent steps.\")\n",
    "    # Still need to save the cohort info indicating the dataset isn't usable\n",
    "    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=False, \n",
    "        is_biased=None, \n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Dataset doesn't contain usable clinical data for trait analysis\"\n",
    "    )"
   ]
  }
 ],
 "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
}