{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a116024b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:15.098589Z",
     "iopub.status.busy": "2025-03-25T05:15:15.098483Z",
     "iopub.status.idle": "2025-03-25T05:15:15.263296Z",
     "shell.execute_reply": "2025-03-25T05:15:15.262943Z"
    }
   },
   "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 = \"Essential_Thrombocythemia\"\n",
    "cohort = \"GSE12295\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Essential_Thrombocythemia\"\n",
    "in_cohort_dir = \"../../input/GEO/Essential_Thrombocythemia/GSE12295\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Essential_Thrombocythemia/GSE12295.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE12295.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE12295.csv\"\n",
    "json_path = \"../../output/preprocess/Essential_Thrombocythemia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f87c00ef",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70f656be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:15.264744Z",
     "iopub.status.busy": "2025-03-25T05:15:15.264604Z",
     "iopub.status.idle": "2025-03-25T05:15:15.282299Z",
     "shell.execute_reply": "2025-03-25T05:15:15.282012Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Class prediction models of thrombocytosis using genetic biomarkers\"\n",
      "!Series_summary\t\"Using custom spotted oligonucelotide platelet-focused arrays, platelet samples were compared. 48 health controls, 23 reactive thrombocytosis (RT) and 24 essential thrombocythemia (ET) samples were compared. An 11-biomarker gene subset was identified that discriminated among the three cohorts with 86.3% accuracy.\"\n",
      "!Series_overall_design\t\"70 mer oligonucleotides were spotted in quadruplicate and hybridized versus reference RNA in two color method. Spiked control RNAs were also included.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['Essential thrombocythemia Patient Platelet', 'Reactive Thrombocytosis Patient platelets', 'Normal Patient Platelets']}\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": "55ea216e",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f78c13c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:15.283240Z",
     "iopub.status.busy": "2025-03-25T05:15:15.283137Z",
     "iopub.status.idle": "2025-03-25T05:15:15.292458Z",
     "shell.execute_reply": "2025-03-25T05:15:15.292184Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of clinical features:\n",
      "{'GSM309072': [1.0], 'GSM309073': [1.0], 'GSM309074': [1.0], 'GSM309075': [1.0], 'GSM309076': [1.0], 'GSM309077': [1.0], 'GSM309078': [1.0], 'GSM309079': [1.0], 'GSM309080': [1.0], 'GSM309081': [1.0], 'GSM309082': [1.0], 'GSM309083': [1.0], 'GSM309084': [1.0], 'GSM309085': [1.0], 'GSM309086': [1.0], 'GSM309087': [1.0], 'GSM309088': [1.0], 'GSM309089': [0.0], 'GSM309090': [1.0], 'GSM309091': [0.0], 'GSM309092': [1.0], 'GSM309093': [1.0], 'GSM309094': [1.0], 'GSM309095': [0.0], 'GSM309096': [0.0], 'GSM309097': [0.0], 'GSM309098': [0.0], 'GSM309099': [0.0], 'GSM309100': [0.0], 'GSM309101': [0.0], 'GSM309102': [0.0], 'GSM309103': [0.0], 'GSM309104': [0.0], 'GSM309105': [0.0], 'GSM309106': [0.0], 'GSM309107': [0.0], 'GSM309108': [0.0], 'GSM309109': [0.0], 'GSM309110': [0.0], 'GSM309111': [0.0], 'GSM309112': [0.0], 'GSM309113': [0.0], 'GSM309114': [0.0], 'GSM309115': [0.0], 'GSM309116': [0.0], 'GSM309117': [0.0], 'GSM309118': [0.0], 'GSM309119': [0.0], 'GSM309120': [0.0], 'GSM309121': [0.0], 'GSM309122': [0.0], 'GSM309123': [0.0], 'GSM309124': [0.0], 'GSM309125': [0.0], 'GSM309126': [0.0], 'GSM309127': [0.0], 'GSM309128': [0.0], 'GSM309129': [0.0], 'GSM309130': [0.0], 'GSM309131': [0.0], 'GSM309132': [0.0], 'GSM309133': [0.0], 'GSM309134': [0.0], 'GSM309135': [0.0], 'GSM309136': [0.0], 'GSM309137': [0.0], 'GSM309138': [0.0], 'GSM309139': [0.0], 'GSM309140': [0.0], 'GSM309141': [0.0], 'GSM309142': [0.0], 'GSM309143': [0.0], 'GSM309144': [0.0], 'GSM309145': [0.0], 'GSM309146': [0.0], 'GSM309147': [0.0], 'GSM309148': [0.0], 'GSM309149': [1.0], 'GSM309150': [1.0], 'GSM309151': [0.0], 'GSM309152': [0.0], 'GSM309153': [0.0], 'GSM309154': [0.0], 'GSM309155': [0.0], 'GSM309156': [0.0], 'GSM309157': [0.0], 'GSM309158': [0.0], 'GSM309159': [0.0], 'GSM309160': [0.0], 'GSM309161': [0.0], 'GSM309162': [0.0], 'GSM309163': [0.0], 'GSM309164': [0.0], 'GSM309165': [0.0], 'GSM309166': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE12295.csv\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Determine gene expression data availability\n",
    "# Based on series title and overall design, this appears to be a gene expression dataset\n",
    "# using oligonucleotide arrays with platelet samples\n",
    "is_gene_available = True\n",
    "\n",
    "# Step 2: Clinical feature availability and type conversion\n",
    "# From sample characteristics, we can see that row 0 contains information about patient type\n",
    "# which includes \"Essential thrombocythemia\", \"Reactive Thrombocytosis\", and \"Normal\"\n",
    "# This is the trait information we need for Essential_Thrombocythemia\n",
    "\n",
    "# 2.1 Data Availability\n",
    "trait_row = 0  # The trait information is in row 0\n",
    "age_row = None  # No age information found\n",
    "gender_row = None  # No gender information found\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait information to binary format (0 or 1)\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        # Extract value after colon if it exists\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        # Check if \"Essential thrombocythemia\" is in the value\n",
    "        if \"Essential thrombocythemia\" in value:\n",
    "            return 1\n",
    "        elif \"Normal\" in value or \"Reactive Thrombocytosis\" in value:\n",
    "            return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age information to numeric format\"\"\"\n",
    "    # Not needed since age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender information to binary format (0 for female, 1 for male)\"\"\"\n",
    "    # Not needed since gender data is not available\n",
    "    return None\n",
    "\n",
    "# Step 3: Save metadata\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",
    "# Step 4: Clinical Feature Extraction (if trait data is available)\n",
    "if trait_row is not None:\n",
    "    # Load the clinical data (assuming clinical_data is already loaded)\n",
    "    try:\n",
    "        clinical_data\n",
    "    except NameError:\n",
    "        print(\"Clinical data not loaded yet. Loading it now.\")\n",
    "        # This is a placeholder. In a real scenario, clinical_data would be loaded from a previous step\n",
    "        # Since it's not provided, we'll create a placeholder to avoid errors\n",
    "        clinical_data = pd.DataFrame()\n",
    "    \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,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the dataframe\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save to CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=True)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a09001b",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7869e98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:15.293473Z",
     "iopub.status.busy": "2025-03-25T05:15:15.293368Z",
     "iopub.status.idle": "2025-03-25T05:15:15.308089Z",
     "shell.execute_reply": "2025-03-25T05:15:15.307789Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene/probe identifiers:\n",
      "Index(['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009',\n",
      "       '1010', '1011', '1012', '1013', '1014', '1015', '1016', '1017', '1018',\n",
      "       '1019', '1020'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths again to access the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data from the matrix_file\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
    "print(\"First 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a760222",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea0f74d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:15.309053Z",
     "iopub.status.busy": "2025-03-25T05:15:15.308943Z",
     "iopub.status.idle": "2025-03-25T05:15:15.310666Z",
     "shell.execute_reply": "2025-03-25T05:15:15.310381Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers ('1001', '1002', etc.) are not human gene symbols.\n",
    "# They appear to be numeric probe IDs that need to be mapped to gene symbols.\n",
    "# Human gene symbols typically look like BRCA1, TP53, IL6, etc.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2871c51",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3477c132",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:15.311677Z",
     "iopub.status.busy": "2025-03-25T05:15:15.311575Z",
     "iopub.status.idle": "2025-03-25T05:15:15.387618Z",
     "shell.execute_reply": "2025-03-25T05:15:15.387302Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['1001', '1002', '1003', '1004', '1005'], 'ProbeID': ['1405_i_at', '1773_at', '200024_at', '200033_at', '200063_s_at'], 'Length': ['69', '69', '70', '70', '70'], 'TM': [76.668, 81.422, 78.843, 74.743, 74.743], 'Conc. (uM)': [50.0, 50.0, 50.0, 50.0, 50.0], 'Vol. (uL)': [200.0, 200.0, 200.0, 200.0, 200.0], 'SEQUENCE': ['AAAAGCTTCCCCAACTAAAGCCTAGAAGAGCTTCTGAGGCGCTGCTTTGTCAAAAGGAAGTCTCTAGGT', 'AGCTTAAGGATGAGACATCGGCAGAGCCTGCAACCGACTAGAGGACCTGGGTCCCGGCAGCTCTTTGCT', 'TCCTCGAACTCCTATGCCATTAAGAAGAAGGACGAGCTGGAGCGTGTGGCCAAGTCCAACCGCTGATTTT', 'TGCTACTGCAGCTGCACCTATGATTGGTTATCCAATGCCAACAGGATATTCCCAATAAGACTTTAGAAGT', 'AGAGTGAGAACTTTCCCTACCGTGTTTGATAAATGTTGTCCAGGTTCTATTGCCAAGAATGTGTTGTCCA'], 'Gene Symbol': ['CCL5', 'FNTB', '---', 'DDX5', 'NPM1'], 'ORF': ['CCL5', 'FNTB', nan, 'DDX5', 'NPM1'], 'SPOT_ID': [nan, nan, '200024_at', nan, nan]}\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": "752a4010",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3cf14881",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:15.388958Z",
     "iopub.status.busy": "2025-03-25T05:15:15.388686Z",
     "iopub.status.idle": "2025-03-25T05:15:15.411828Z",
     "shell.execute_reply": "2025-03-25T05:15:15.411537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping preview:\n",
      "{'ID': ['1001', '1002', '1003', '1004', '1005'], 'Gene': ['CCL5', 'FNTB', '---', 'DDX5', 'NPM1']}\n",
      "Gene expression data preview after mapping:\n",
      "{'GSM309072': [0.3613, 1.2385, 3.8373999999999997, 1.0297, 1.1287], 'GSM309073': [2.2002, 0.1765, 0.9791000000000001, -0.5281, 1.7099], 'GSM309074': [0.2529, 1.2249, 1.4834, 0.3542, 0.6599], 'GSM309075': [0.222, 1.194, 1.5643, 0.2396, 0.629], 'GSM309076': [0.0, -0.2831, 0.7523000000000001, 1.5443, 0.4921], 'GSM309077': [0.0, 0.371, -2.1963, 0.0623, -0.1802], 'GSM309078': [0.0, -0.9493, -1.4156, -1.1107, 0.0], 'GSM309079': [-3.2136, -0.3648, -0.7977, 0.0715, -0.5518], 'GSM309080': [0.0, -1.5601, -7.0526, -2.5894, -1.1948], 'GSM309081': [0.0, 1.6709, 4.8239, 0.8932, 1.7434], 'GSM309082': [-0.0265, 0.6641, 1.0421, -1.0281, -0.6279], 'GSM309083': [0.7839, -0.8244, -2.0099, -0.3996, 1.149], 'GSM309084': [-3.1558, -1.4091, -3.7744, -1.3303, -1.4615], 'GSM309085': [0.0, 0.0, 0.0, 1.0552, 0.0], 'GSM309086': [0.0, 0.0, 2.016, 0.0699, 0.0], 'GSM309087': [0.0, 0.1945, 0.0, 0.4222, 0.0], 'GSM309088': [0.0, 0.0, -1.0885, -0.0391, -0.2265], 'GSM309089': [-1.519, 0.9638, -0.6178000000000001, 0.0696, 0.6705], 'GSM309090': [0.0, -1.1378, -1.213, 0.5458, 0.2208], 'GSM309091': [0.0, 0.5391, 2.1229999999999998, 1.556, 1.3992], 'GSM309092': [0.0, 0.8574, 1.766, 0.5056, -0.2356], 'GSM309093': [0.0, -0.1333, 1.5826, 0.0189, 0.0], 'GSM309094': [0.0, 1.0775, 1.3574, 0.1331, -1.6419], 'GSM309095': [0.0, 1.3461, 1.5451000000000001, -0.2872, 1.3072], 'GSM309096': [0.0, 0.0297, -1.4036000000000002, 0.0649, -0.6549], 'GSM309097': [0.0, 0.5596, 3.9409, 0.3991, 1.7206], 'GSM309098': [0.6893, 1.3763, 3.7793, -0.2381, 1.9205], 'GSM309099': [0.0, 1.3028, 5.6052, 1.2953, 2.8148], 'GSM309100': [0.0, -0.0847, -5.5847, -1.8389, 0.7864], 'GSM309101': [0.0, 0.3395, 3.6105, 0.9164, 0.0], 'GSM309102': [0.0, -1.2011, -2.7195, -0.5296, 0.2284], 'GSM309103': [0.0, -0.6915, -3.7036, -3.9667, -0.3136], 'GSM309104': [0.0, -1.294, -1.0558, -0.9046, 0.0], 'GSM309105': [0.0, 1.551, 0.0, 1.259, 2.8663], 'GSM309106': [0.0, -0.0214, 1.0021, -0.2993, -0.7268], 'GSM309107': [0.0, -0.7853, -0.6104, -1.9839, -0.6835], 'GSM309108': [-1.4821, -0.0309, -2.4136, -0.6209, 0.2303], 'GSM309109': [-0.6151, 0.1763, 1.9387, 1.0081, -2.5828], 'GSM309110': [-0.1323, 0.6175, 0.7125000000000001, -0.1722, -0.0692], 'GSM309111': [1.3508, 1.148, 4.1967, 1.1691, 0.7933], 'GSM309112': [0.0409, 1.0696, 2.2758, 0.3195, 0.7553], 'GSM309113': [-0.3511, 0.8563, -0.4036, 1.012, 0.8532], 'GSM309114': [0.0, 1.7623, 0.0, 0.018, 0.0], 'GSM309115': [0.0, 0.076, -2.5332, 1.846, 1.2725], 'GSM309116': [-0.9815, -0.302, 0.0, 0.0, 0.0], 'GSM309117': [0.0, 0.6294, 4.6808, 1.1432, 2.0719], 'GSM309118': [0.0, 2.4793, 4.9186, 0.0, 1.8317], 'GSM309119': [0.0, 1.5272, 3.3418, 1.4698, 1.0476], 'GSM309120': [0.0, 0.2517, -0.5357, -0.7192, -1.7276], 'GSM309121': [0.0, -1.4377, -2.2374, -1.9243, -2.5887], 'GSM309122': [0.0, -1.7137, -3.3864, -3.8124, -3.3397], 'GSM309123': [0.0, 1.656, 3.4787999999999997, 0.5698, 1.5321], 'GSM309124': [0.0, 2.3282, 1.4104, 1.6687, 2.0998], 'GSM309125': [0.0, 0.8636, 2.8536, 1.07, 0.8455], 'GSM309126': [0.0, -1.4459, -1.0336, -0.5223, -2.1063], 'GSM309127': [0.0, 1.4965, 3.6075, 0.129, 3.1556], 'GSM309128': [0.1379, 1.3935, 2.7613, 0.9427, 2.1999], 'GSM309129': [1.2156, 2.8242, 6.8843, 2.2627, 4.7448], 'GSM309130': [0.0, -0.7558, 1.5112, -1.5102, 0.0], 'GSM309131': [0.3525, 1.8534, 1.1275, 1.844, 0.0], 'GSM309132': [0.0, 1.7093, 2.6828000000000003, 1.0324, 1.7875], 'GSM309133': [0.0, 1.3018, 1.491, 0.5426, -0.117], 'GSM309134': [-0.2891, 1.1009, 1.9446999999999999, -0.375, -0.0111], 'GSM309135': [0.0, 0.9167, 2.5248999999999997, -0.7523, -0.063], 'GSM309136': [0.0, -0.6571, 0.0, -0.8067, -0.9832], 'GSM309137': [0.0, 0.8455, -0.3629, -1.0066, 0.3992], 'GSM309138': [0.0, 0.9634, 0.814, 0.6151, 2.3526], 'GSM309139': [0.0, -0.9418, -1.0356, -1.767, -0.0553], 'GSM309140': [-0.1069, -1.387, -2.0241, -0.5792, -1.6081], 'GSM309141': [0.0, -0.0727, 0.1575, -1.3271, -0.3351], 'GSM309142': [1.8101, 2.6405, 6.577400000000001, 3.0272, 3.1111], 'GSM309143': [0.0, -1.2786, -2.9162, -2.8368, -1.7426], 'GSM309144': [0.0, 0.658, 1.3676, 0.5045, -1.0619], 'GSM309145': [0.0, -1.1593, -0.585, -0.2536, -0.4637], 'GSM309146': [0.0, -1.1261, 2.6986, -0.8031, -0.6314], 'GSM309147': [0.0, 0.0, 0.0, -1.2803, -0.5364], 'GSM309148': [0.0, -3.3032, 1.5988, -1.1619, -0.4155], 'GSM309149': [0.0, 0.0, -3.7561999999999998, -2.354, -1.8862], 'GSM309150': [-2.0645, -5.4597, -1.2686, -3.0706, 0.0], 'GSM309151': [0.274, 0.0, -3.7332, -0.4449, -0.0831], 'GSM309152': [0.0, 0.0, -1.8303, -2.0458, 0.0], 'GSM309153': [0.0, 0.0, -3.3146, -3.7171, -2.2778], 'GSM309154': [0.0, 0.0, 0.0, -0.8025, -0.8618], 'GSM309155': [0.0, -2.155, -3.4703, -2.039, -0.96], 'GSM309156': [0.0, 0.0, 0.5435, 2.3734, 1.6223], 'GSM309157': [0.0, -0.5322, -0.8053999999999999, 0.4497, -0.7048], 'GSM309158': [0.0, -0.0007, 1.813, 1.2003, 1.9106], 'GSM309159': [0.0, -0.1732, -0.1768, 0.1513, 0.3777], 'GSM309160': [0.0, -0.5567, -1.0189, 0.5674, 0.4684], 'GSM309161': [0.0742, -2.559, -3.0284, -2.3005, -1.3133], 'GSM309162': [0.0, -1.1636, -1.3293, -2.0252, -0.4163], 'GSM309163': [0.7325, 0.1887, 0.19659999999999997, -0.053, 1.1867], 'GSM309164': [-1.295, -1.1098, -1.2967, -0.0183, 1.2539], 'GSM309165': [0.6059, -1.4014, -0.6243, -0.8428, -1.1621], 'GSM309166': [0.0, -0.1921, 1.275, 0.5519, 1.2219]}\n",
      "Shape of gene expression data: (421, 95)\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the columns for gene identifiers and gene symbols in the annotation data\n",
    "# From the preview, we can see that:\n",
    "# - 'ID' in gene_annotation corresponds to gene identifiers in gene_data\n",
    "# - 'Gene Symbol' contains the corresponding gene symbols\n",
    "\n",
    "# 2. Get a gene mapping dataframe by extracting these two columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
    "\n",
    "# Print a preview of the mapping to verify\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print the first few rows of the resulting gene expression data\n",
    "print(\"Gene expression data preview after mapping:\")\n",
    "print(preview_df(gene_data))\n",
    "\n",
    "# Print the shape of the resulting gene expression dataframe\n",
    "print(\"Shape of gene expression data:\", gene_data.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a4e2e09",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2c156a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:15.412941Z",
     "iopub.status.busy": "2025-03-25T05:15:15.412833Z",
     "iopub.status.idle": "2025-03-25T05:15:15.612103Z",
     "shell.execute_reply": "2025-03-25T05:15:15.611767Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape after normalization: (400, 95)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE12295.csv\n",
      "Loading the original clinical data...\n",
      "Extracting clinical features...\n",
      "Clinical data preview:\n",
      "{'GSM309072': [1.0], 'GSM309073': [1.0], 'GSM309074': [1.0], 'GSM309075': [1.0], 'GSM309076': [1.0], 'GSM309077': [1.0], 'GSM309078': [1.0], 'GSM309079': [1.0], 'GSM309080': [1.0], 'GSM309081': [1.0], 'GSM309082': [1.0], 'GSM309083': [1.0], 'GSM309084': [1.0], 'GSM309085': [1.0], 'GSM309086': [1.0], 'GSM309087': [1.0], 'GSM309088': [1.0], 'GSM309089': [0.0], 'GSM309090': [1.0], 'GSM309091': [0.0], 'GSM309092': [1.0], 'GSM309093': [1.0], 'GSM309094': [1.0], 'GSM309095': [0.0], 'GSM309096': [0.0], 'GSM309097': [0.0], 'GSM309098': [0.0], 'GSM309099': [0.0], 'GSM309100': [0.0], 'GSM309101': [0.0], 'GSM309102': [0.0], 'GSM309103': [0.0], 'GSM309104': [0.0], 'GSM309105': [0.0], 'GSM309106': [0.0], 'GSM309107': [0.0], 'GSM309108': [0.0], 'GSM309109': [0.0], 'GSM309110': [0.0], 'GSM309111': [0.0], 'GSM309112': [0.0], 'GSM309113': [0.0], 'GSM309114': [0.0], 'GSM309115': [0.0], 'GSM309116': [0.0], 'GSM309117': [0.0], 'GSM309118': [0.0], 'GSM309119': [0.0], 'GSM309120': [0.0], 'GSM309121': [0.0], 'GSM309122': [0.0], 'GSM309123': [0.0], 'GSM309124': [0.0], 'GSM309125': [0.0], 'GSM309126': [0.0], 'GSM309127': [0.0], 'GSM309128': [0.0], 'GSM309129': [0.0], 'GSM309130': [0.0], 'GSM309131': [0.0], 'GSM309132': [0.0], 'GSM309133': [0.0], 'GSM309134': [0.0], 'GSM309135': [0.0], 'GSM309136': [0.0], 'GSM309137': [0.0], 'GSM309138': [0.0], 'GSM309139': [0.0], 'GSM309140': [0.0], 'GSM309141': [0.0], 'GSM309142': [0.0], 'GSM309143': [0.0], 'GSM309144': [0.0], 'GSM309145': [0.0], 'GSM309146': [0.0], 'GSM309147': [0.0], 'GSM309148': [0.0], 'GSM309149': [1.0], 'GSM309150': [1.0], 'GSM309151': [0.0], 'GSM309152': [0.0], 'GSM309153': [0.0], 'GSM309154': [0.0], 'GSM309155': [0.0], 'GSM309156': [0.0], 'GSM309157': [0.0], 'GSM309158': [0.0], 'GSM309159': [0.0], 'GSM309160': [0.0], 'GSM309161': [0.0], 'GSM309162': [0.0], 'GSM309163': [0.0], 'GSM309164': [0.0], 'GSM309165': [0.0], 'GSM309166': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE12295.csv\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (95, 401)\n",
      "Handling missing values...\n",
      "Linked data shape after handling missing values: (95, 401)\n",
      "Checking for bias in trait distribution...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the feature 'Essential_Thrombocythemia', the least common label is '1.0' with 24 occurrences. This represents 25.26% of the dataset.\n",
      "The distribution of the feature 'Essential_Thrombocythemia' in this dataset is fine.\n",
      "\n",
      "Dataset usability: True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Essential_Thrombocythemia/GSE12295.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols...\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to a CSV file\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. Link the clinical and genetic data\n",
    "print(\"Loading the original clinical data...\")\n",
    "# Get the matrix file again to ensure we have the proper data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "print(\"Extracting clinical features...\")\n",
    "# Use the clinical_data obtained directly from the matrix file\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",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Save the clinical data to a CSV file\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Link clinical and genetic data using the normalized gene data\n",
    "print(\"Linking clinical and genetic data...\")\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "print(\"Handling missing values...\")\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check if trait is biased\n",
    "print(\"Checking for bias in trait distribution...\")\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Final validation\n",
    "note = \"Dataset contains gene expression data from patients with Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (PMF).\"\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "print(f\"Dataset usability: {is_usable}\")\n",
    "\n",
    "# 6. Save linked data if usable\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset is not usable for trait-gene association studies due to bias or other issues.\")"
   ]
  }
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