{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "115d3272",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:07:29.587579Z",
     "iopub.status.busy": "2025-03-25T07:07:29.587474Z",
     "iopub.status.idle": "2025-03-25T07:07:29.748916Z",
     "shell.execute_reply": "2025-03-25T07:07:29.748581Z"
    }
   },
   "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 = \"Cardiovascular_Disease\"\n",
    "cohort = \"GSE256539\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Cardiovascular_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Cardiovascular_Disease/GSE256539\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Cardiovascular_Disease/GSE256539.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Cardiovascular_Disease/gene_data/GSE256539.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Cardiovascular_Disease/clinical_data/GSE256539.csv\"\n",
    "json_path = \"../../output/preprocess/Cardiovascular_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d625d2e",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f029f5ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:07:29.750299Z",
     "iopub.status.busy": "2025-03-25T07:07:29.750156Z",
     "iopub.status.idle": "2025-03-25T07:07:29.873251Z",
     "shell.execute_reply": "2025-03-25T07:07:29.872887Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Digital Spatial Profiling identifies distinct molecular signatures of vascular lesions in Pulmonary Arterial Hypertension\"\n",
      "!Series_summary\t\"Idiopathic Pulmonary Arterial Hypertension (IPAH) is a severe human disease, characterized by extensive pulmonary vascular remodeling due to plexiform and obliterative lesions, media hypertrophy, and alterations of adventitia. The objective of the study was to test the hypothesis that microscopic IPAH vascular lesions express unique molecular profiles, which collectively are different from control pulmonary arteries. We used digital spatial transcriptomics to profile the genome-wide differential transcriptomic signature of key pathological lesions (plexiform, obliterative, intima+media hypertrophy, and adventitia) in IPAH lungs (n= 11)  and compared these data to the intima+media and adventitia of control pulmonary artery (n=5). The IPAH lesions and pulmonary artery compartments were defined by the analyses of hematoxylin-eosin stained serial section, aided by labeling with CD31 (for endothelial cells), smooth muscle cell actin (SMA), and CD45 for inflammatory mononuclear cells, also in serial sections. Approximately 12 regions of interest (ROI) were sampled from a histological section of a paraffin-embedded block of each lung, which was selected based on the finding of enrichment for IPAH lesions or control pulmonary arteries. \"\n",
      "!Series_overall_design\t\"A total of 211 ROIs were studied with 149 ROIs representing IPAH lesions with 39 plexiform lesions, 37 obliterative lesions, 36 intima+media hypertrophy, and 37 IPAH adventitia; of 62 ROIs of control lungs, including 34 intima+media and 28 control adventitia. The ROIs were selected largely based on the histopathological identification of each type of lesion (in IPAH lungs) or control pulmonary artery compartments. The selected ROIs were then processed by the GeoMx Nanostring platform, followed by whole genome sequencing.\"\n",
      "!Series_overall_design\t\"\"\n",
      "!Series_overall_design\t\"*** FASTQ raw data files have been requested. ***\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['individuial: AH006', 'individuial: AH015', 'individuial: UA013', 'individuial: BA023', 'individuial: BA009', 'individuial: AH014', 'individuial: VA008', 'individuial: UC010', 'individuial: UC007', 'individuial: AH001', 'individuial: VA010', 'individuial: UA005', 'individuial: BA035', 'individuial: CC016', 'individuial: BA021', 'individuial: UM003'], 1: ['batch: plate3', 'batch: plate2', 'batch: plate1', 'batch: hWTA_20210810T192_DSP-1012340068802']}\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": "e8b17af6",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6807228b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:07:29.874498Z",
     "iopub.status.busy": "2025-03-25T07:07:29.874385Z",
     "iopub.status.idle": "2025-03-25T07:07:29.889167Z",
     "shell.execute_reply": "2025-03-25T07:07:29.888876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical Features Preview:\n",
      "{'GSM8105899': [1.0], 'GSM8105900': [1.0], 'GSM8105901': [1.0], 'GSM8105902': [1.0], 'GSM8105903': [1.0], 'GSM8105904': [1.0], 'GSM8105905': [1.0], 'GSM8105906': [1.0], 'GSM8105907': [1.0], 'GSM8105908': [1.0], 'GSM8105909': [1.0], 'GSM8105910': [1.0], 'GSM8105911': [1.0], 'GSM8105912': [1.0], 'GSM8105913': [1.0], 'GSM8105914': [1.0], 'GSM8105915': [1.0], 'GSM8105916': [1.0], 'GSM8105917': [1.0], 'GSM8105918': [1.0], 'GSM8105919': [1.0], 'GSM8105920': [1.0], 'GSM8105921': [1.0], 'GSM8105922': [1.0], 'GSM8105923': [1.0], 'GSM8105924': [1.0], 'GSM8105925': [1.0], 'GSM8105926': [1.0], 'GSM8105927': [1.0], 'GSM8105928': [1.0], 'GSM8105929': [1.0], 'GSM8105930': [1.0], 'GSM8105931': [1.0], 'GSM8105932': [1.0], 'GSM8105933': [1.0], 'GSM8105934': [1.0], 'GSM8105935': [1.0], 'GSM8105936': [1.0], 'GSM8105937': [1.0], 'GSM8105938': [1.0], 'GSM8105939': [1.0], 'GSM8105940': [1.0], 'GSM8105941': [1.0], 'GSM8105942': [1.0], 'GSM8105943': [1.0], 'GSM8105944': [1.0], 'GSM8105945': [1.0], 'GSM8105946': [1.0], 'GSM8105947': [1.0], 'GSM8105948': [1.0], 'GSM8105949': [1.0], 'GSM8105950': [1.0], 'GSM8105951': [1.0], 'GSM8105952': [1.0], 'GSM8105953': [1.0], 'GSM8105954': [1.0], 'GSM8105955': [1.0], 'GSM8105956': [1.0], 'GSM8105957': [1.0], 'GSM8105958': [1.0], 'GSM8105959': [1.0], 'GSM8105960': [1.0], 'GSM8105961': [1.0], 'GSM8105962': [1.0], 'GSM8105963': [1.0], 'GSM8105964': [1.0], 'GSM8105965': [1.0], 'GSM8105966': [1.0], 'GSM8105967': [1.0], 'GSM8105968': [1.0], 'GSM8105969': [1.0], 'GSM8105970': [1.0], 'GSM8105971': [1.0], 'GSM8105972': [1.0], 'GSM8105973': [1.0], 'GSM8105974': [1.0], 'GSM8105975': [1.0], 'GSM8105976': [1.0], 'GSM8105977': [1.0], 'GSM8105978': [1.0], 'GSM8105979': [1.0], 'GSM8105980': [1.0], 'GSM8105981': [1.0], 'GSM8105982': [1.0], 'GSM8105983': [1.0], 'GSM8105984': [1.0], 'GSM8105985': [1.0], 'GSM8105986': [1.0], 'GSM8105987': [1.0], 'GSM8105988': [1.0], 'GSM8105989': [1.0], 'GSM8105990': [1.0], 'GSM8105991': [1.0], 'GSM8105992': [1.0], 'GSM8105993': [1.0], 'GSM8105994': [1.0], 'GSM8105995': [1.0], 'GSM8105996': [1.0], 'GSM8105997': [1.0], 'GSM8105998': [1.0], 'GSM8105999': [1.0], 'GSM8106000': [1.0], 'GSM8106001': [1.0], 'GSM8106002': [1.0], 'GSM8106003': [1.0], 'GSM8106004': [1.0], 'GSM8106005': [1.0], 'GSM8106006': [1.0], 'GSM8106007': [1.0], 'GSM8106008': [1.0], 'GSM8106009': [1.0], 'GSM8106010': [1.0], 'GSM8106011': [1.0], 'GSM8106012': [1.0], 'GSM8106013': [1.0], 'GSM8106014': [1.0], 'GSM8106015': [1.0], 'GSM8106016': [1.0], 'GSM8106017': [1.0], 'GSM8106018': [1.0], 'GSM8106019': [1.0], 'GSM8106020': [1.0], 'GSM8106021': [1.0], 'GSM8106022': [1.0], 'GSM8106023': [1.0], 'GSM8106024': [1.0], 'GSM8106025': [1.0], 'GSM8106026': [1.0], 'GSM8106027': [1.0], 'GSM8106028': [1.0], 'GSM8106029': [1.0], 'GSM8106030': [1.0], 'GSM8106031': [1.0], 'GSM8106032': [1.0], 'GSM8106033': [1.0], 'GSM8106034': [1.0], 'GSM8106035': [1.0], 'GSM8106036': [1.0], 'GSM8106037': [1.0], 'GSM8106038': [1.0], 'GSM8106039': [1.0], 'GSM8106040': [1.0], 'GSM8106041': [1.0], 'GSM8106042': [1.0], 'GSM8106043': [1.0], 'GSM8106044': [1.0], 'GSM8106045': [1.0], 'GSM8106046': [1.0], 'GSM8106047': [1.0], 'GSM8106048': [1.0], 'GSM8106049': [1.0], 'GSM8106050': [1.0], 'GSM8106051': [1.0], 'GSM8106052': [1.0], 'GSM8106053': [1.0], 'GSM8106054': [1.0], 'GSM8106055': [1.0], 'GSM8106056': [1.0], 'GSM8106057': [1.0], 'GSM8106058': [1.0], 'GSM8106059': [0.0], 'GSM8106060': [0.0], 'GSM8106061': [0.0], 'GSM8106062': [0.0], 'GSM8106063': [0.0], 'GSM8106064': [0.0], 'GSM8106065': [0.0], 'GSM8106066': [0.0], 'GSM8106067': [0.0], 'GSM8106068': [0.0], 'GSM8106069': [0.0], 'GSM8106070': [0.0], 'GSM8106071': [1.0], 'GSM8106072': [1.0], 'GSM8106073': [1.0], 'GSM8106074': [1.0], 'GSM8106075': [1.0], 'GSM8106076': [1.0], 'GSM8106077': [1.0], 'GSM8106078': [1.0], 'GSM8106079': [1.0], 'GSM8106080': [1.0], 'GSM8106081': [1.0], 'GSM8106082': [1.0], 'GSM8106083': [1.0], 'GSM8106084': [1.0], 'GSM8106085': [1.0], 'GSM8106086': [1.0], 'GSM8106087': [1.0], 'GSM8106088': [1.0], 'GSM8106089': [1.0], 'GSM8106090': [1.0], 'GSM8106091': [1.0], 'GSM8106092': [1.0], 'GSM8106093': [1.0], 'GSM8106094': [1.0], 'GSM8106095': [1.0], 'GSM8106096': [1.0], 'GSM8106097': [1.0], 'GSM8106098': [1.0]}\n",
      "Clinical features saved to ../../output/preprocess/Cardiovascular_Disease/clinical_data/GSE256539.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Availability assessment based on background information\n",
    "\n",
    "# This is a gene expression profiling study using digital spatial transcriptomics\n",
    "# The study mentions \"genome-wide differential transcriptomic signature\"\n",
    "# This clearly indicates gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Clinical feature extraction and conversion functions\n",
    "\n",
    "# From sample characteristics, we need to analyze what data is available\n",
    "# The dictionary shows 'individual' IDs and 'batch' information, but not disease status directly\n",
    "# From the background information, we know this is a case-control study of IPAH vs controls\n",
    "\n",
    "# 2.1 Determine row numbers for clinical features\n",
    "# Looking at the sample characteristics, we can infer trait information from individual IDs\n",
    "trait_row = 0  # We'll infer disease status from individual IDs\n",
    "\n",
    "# Age and gender information are not provided in the sample characteristics\n",
    "age_row = None  # Age data not available\n",
    "gender_row = None  # Gender data not available\n",
    "\n",
    "# 2.2 Define conversion functions for clinical features\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert individual IDs to binary trait values.\n",
    "    Based on the background information, we can infer:\n",
    "    - IPAH patients have IDs with patterns like AH*, VA*, UC*, etc.\n",
    "    - Control samples have IDs with patterns like CC*\n",
    "    \n",
    "    Returns:\n",
    "    - 1 for IPAH cases\n",
    "    - 0 for controls\n",
    "    - None for unclear cases\n",
    "    \"\"\"\n",
    "    if ':' in value:\n",
    "        # Extract the value after the colon and strip whitespace\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Based on the background, control subjects would be in the control pulmonary artery group\n",
    "    # The IDs \"CC*\" appear to be controls based on context and naming convention\n",
    "    if value.startswith('CC'):\n",
    "        return 0  # Control\n",
    "    else:\n",
    "        return 1  # IPAH case\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age values to continuous numerical values.\n",
    "    Not implemented as age data is not available.\n",
    "    \"\"\"\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"\n",
    "    Convert gender values to binary (0 for female, 1 for male).\n",
    "    Not implemented as gender data is not available.\n",
    "    \"\"\"\n",
    "    return None\n",
    "\n",
    "# 3. Save initial metadata about dataset usability\n",
    "is_trait_available = trait_row is not None\n",
    "\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. Extract clinical features if available\n",
    "if trait_row is not None:\n",
    "    # This assumes clinical_data is provided by previous steps\n",
    "    try:\n",
    "        # Load clinical data (this should have been done in previous steps)\n",
    "        # Let's ensure it's available before proceeding\n",
    "        if 'clinical_data' not in locals():\n",
    "            # Try to load from the input directory\n",
    "            clinical_file = os.path.join(in_cohort_dir, \"sample_characteristics.csv\")\n",
    "            if os.path.exists(clinical_file):\n",
    "                clinical_data = pd.read_csv(clinical_file)\n",
    "            else:\n",
    "                # Create from the sample characteristics dictionary if needed\n",
    "                sample_chars_dict = {0: ['individuial: AH006', 'individuial: AH015', 'individuial: UA013', 'individuial: BA023', 'individuial: BA009', 'individuial: AH014', 'individuial: VA008', 'individuial: UC010', 'individuial: UC007', 'individuial: AH001', 'individuial: VA010', 'individuial: UA005', 'individuial: BA035', 'individuial: CC016', 'individuial: BA021', 'individuial: UM003'], \n",
    "                                    1: ['batch: plate3', 'batch: plate2', 'batch: plate1', 'batch: hWTA_20210810T192_DSP-1012340068802']}\n",
    "                clinical_data = pd.DataFrame(sample_chars_dict)\n",
    "        \n",
    "        # Extract clinical features\n",
    "        clinical_features = 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 and save clinical features\n",
    "        print(\"Clinical Features Preview:\")\n",
    "        print(preview_df(clinical_features))\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 clinical features\n",
    "        clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error extracting clinical features: {e}\")\n",
    "else:\n",
    "    print(\"Clinical data not available. Skipping clinical feature extraction.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a588583",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25aeef3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:07:29.890249Z",
     "iopub.status.busy": "2025-03-25T07:07:29.890141Z",
     "iopub.status.idle": "2025-03-25T07:07:30.134233Z",
     "shell.execute_reply": "2025-03-25T07:07:30.133891Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Cardiovascular_Disease/GSE256539/GSE256539_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (8273, 220)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['A2M', 'A4GALT', 'AAAS', 'AACS', 'AAGAB', 'AAK1', 'AAMDC', 'AAMP',\n",
      "       'AARS1', 'AARSD1', 'AATF', 'ABCA10', 'ABCA13', 'ABCA3', 'ABCA6',\n",
      "       'ABCA8', 'ABCB10', 'ABCB9', 'ABCC1', 'ABCC3'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. Get the SOFT and matrix file paths again \n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "print(f\"Matrix file found: {matrix_file}\")\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(f\"Gene data shape: {gene_data.shape}\")\n",
    "    \n",
    "    # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89b9c35d",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2077017a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:07:30.135532Z",
     "iopub.status.busy": "2025-03-25T07:07:30.135411Z",
     "iopub.status.idle": "2025-03-25T07:07:30.137274Z",
     "shell.execute_reply": "2025-03-25T07:07:30.137009Z"
    }
   },
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers in the output, I can see they are already human gene symbols (like A2M, AAAS, ABCC1, etc.)\n",
    "# These are standard HUGO gene symbols, not probe IDs or other identifiers that would need mapping\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428c44c5",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41986bdc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:07:30.138403Z",
     "iopub.status.busy": "2025-03-25T07:07:30.138294Z",
     "iopub.status.idle": "2025-03-25T07:07:36.150465Z",
     "shell.execute_reply": "2025-03-25T07:07:36.150093Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original gene data shape: (8273, 220)\n",
      "Normalized gene data shape: (8271, 220)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Cardiovascular_Disease/gene_data/GSE256539.csv\n",
      "Clinical features shape: (1, 220)\n",
      "Clinical features preview:\n",
      "{'GSM8105899': [1.0], 'GSM8105900': [1.0], 'GSM8105901': [1.0], 'GSM8105902': [1.0], 'GSM8105903': [1.0], 'GSM8105904': [1.0], 'GSM8105905': [1.0], 'GSM8105906': [1.0], 'GSM8105907': [1.0], 'GSM8105908': [1.0], 'GSM8105909': [1.0], 'GSM8105910': [1.0], 'GSM8105911': [1.0], 'GSM8105912': [1.0], 'GSM8105913': [1.0], 'GSM8105914': [1.0], 'GSM8105915': [1.0], 'GSM8105916': [1.0], 'GSM8105917': [1.0], 'GSM8105918': [1.0], 'GSM8105919': [1.0], 'GSM8105920': [1.0], 'GSM8105921': [1.0], 'GSM8105922': [1.0], 'GSM8105923': [1.0], 'GSM8105924': [1.0], 'GSM8105925': [1.0], 'GSM8105926': [1.0], 'GSM8105927': [1.0], 'GSM8105928': [1.0], 'GSM8105929': [1.0], 'GSM8105930': [1.0], 'GSM8105931': [1.0], 'GSM8105932': [1.0], 'GSM8105933': [1.0], 'GSM8105934': [1.0], 'GSM8105935': [1.0], 'GSM8105936': [1.0], 'GSM8105937': [1.0], 'GSM8105938': [1.0], 'GSM8105939': [1.0], 'GSM8105940': [1.0], 'GSM8105941': [1.0], 'GSM8105942': [1.0], 'GSM8105943': [1.0], 'GSM8105944': [1.0], 'GSM8105945': [1.0], 'GSM8105946': [1.0], 'GSM8105947': [1.0], 'GSM8105948': [1.0], 'GSM8105949': [1.0], 'GSM8105950': [1.0], 'GSM8105951': [1.0], 'GSM8105952': [1.0], 'GSM8105953': [1.0], 'GSM8105954': [1.0], 'GSM8105955': [1.0], 'GSM8105956': [1.0], 'GSM8105957': [1.0], 'GSM8105958': [1.0], 'GSM8105959': [1.0], 'GSM8105960': [1.0], 'GSM8105961': [1.0], 'GSM8105962': [1.0], 'GSM8105963': [1.0], 'GSM8105964': [1.0], 'GSM8105965': [1.0], 'GSM8105966': [1.0], 'GSM8105967': [1.0], 'GSM8105968': [1.0], 'GSM8105969': [1.0], 'GSM8105970': [1.0], 'GSM8105971': [1.0], 'GSM8105972': [1.0], 'GSM8105973': [1.0], 'GSM8105974': [1.0], 'GSM8105975': [1.0], 'GSM8105976': [1.0], 'GSM8105977': [1.0], 'GSM8105978': [1.0], 'GSM8105979': [1.0], 'GSM8105980': [1.0], 'GSM8105981': [1.0], 'GSM8105982': [1.0], 'GSM8105983': [1.0], 'GSM8105984': [1.0], 'GSM8105985': [1.0], 'GSM8105986': [1.0], 'GSM8105987': [1.0], 'GSM8105988': [1.0], 'GSM8105989': [1.0], 'GSM8105990': [1.0], 'GSM8105991': [1.0], 'GSM8105992': [1.0], 'GSM8105993': [1.0], 'GSM8105994': [1.0], 'GSM8105995': [1.0], 'GSM8105996': [1.0], 'GSM8105997': [1.0], 'GSM8105998': [1.0], 'GSM8105999': [1.0], 'GSM8106000': [1.0], 'GSM8106001': [1.0], 'GSM8106002': [1.0], 'GSM8106003': [1.0], 'GSM8106004': [1.0], 'GSM8106005': [1.0], 'GSM8106006': [1.0], 'GSM8106007': [1.0], 'GSM8106008': [1.0], 'GSM8106009': [1.0], 'GSM8106010': [1.0], 'GSM8106011': [1.0], 'GSM8106012': [1.0], 'GSM8106013': [1.0], 'GSM8106014': [1.0], 'GSM8106015': [1.0], 'GSM8106016': [1.0], 'GSM8106017': [1.0], 'GSM8106018': [1.0], 'GSM8106019': [1.0], 'GSM8106020': [1.0], 'GSM8106021': [1.0], 'GSM8106022': [1.0], 'GSM8106023': [1.0], 'GSM8106024': [1.0], 'GSM8106025': [1.0], 'GSM8106026': [1.0], 'GSM8106027': [1.0], 'GSM8106028': [1.0], 'GSM8106029': [1.0], 'GSM8106030': [1.0], 'GSM8106031': [1.0], 'GSM8106032': [1.0], 'GSM8106033': [1.0], 'GSM8106034': [1.0], 'GSM8106035': [1.0], 'GSM8106036': [1.0], 'GSM8106037': [1.0], 'GSM8106038': [1.0], 'GSM8106039': [1.0], 'GSM8106040': [1.0], 'GSM8106041': [1.0], 'GSM8106042': [1.0], 'GSM8106043': [1.0], 'GSM8106044': [1.0], 'GSM8106045': [1.0], 'GSM8106046': [1.0], 'GSM8106047': [1.0], 'GSM8106048': [1.0], 'GSM8106049': [1.0], 'GSM8106050': [1.0], 'GSM8106051': [1.0], 'GSM8106052': [1.0], 'GSM8106053': [1.0], 'GSM8106054': [1.0], 'GSM8106055': [1.0], 'GSM8106056': [1.0], 'GSM8106057': [1.0], 'GSM8106058': [1.0], 'GSM8106059': [0.0], 'GSM8106060': [0.0], 'GSM8106061': [0.0], 'GSM8106062': [0.0], 'GSM8106063': [0.0], 'GSM8106064': [0.0], 'GSM8106065': [0.0], 'GSM8106066': [0.0], 'GSM8106067': [0.0], 'GSM8106068': [0.0], 'GSM8106069': [0.0], 'GSM8106070': [0.0], 'GSM8106071': [1.0], 'GSM8106072': [1.0], 'GSM8106073': [1.0], 'GSM8106074': [1.0], 'GSM8106075': [1.0], 'GSM8106076': [1.0], 'GSM8106077': [1.0], 'GSM8106078': [1.0], 'GSM8106079': [1.0], 'GSM8106080': [1.0], 'GSM8106081': [1.0], 'GSM8106082': [1.0], 'GSM8106083': [1.0], 'GSM8106084': [1.0], 'GSM8106085': [1.0], 'GSM8106086': [1.0], 'GSM8106087': [1.0], 'GSM8106088': [1.0], 'GSM8106089': [1.0], 'GSM8106090': [1.0], 'GSM8106091': [1.0], 'GSM8106092': [1.0], 'GSM8106093': [1.0], 'GSM8106094': [1.0], 'GSM8106095': [1.0], 'GSM8106096': [1.0], 'GSM8106097': [1.0], 'GSM8106098': [1.0]}\n",
      "Linked data shape: (220, 8272)\n",
      "Linked data preview (first 5 rows, 5 columns):\n",
      "            Cardiovascular_Disease       A2M    A4GALT      AAAS      AACS\n",
      "GSM8105899                     1.0  0.838248 -0.154053 -0.395053 -0.635978\n",
      "GSM8105900                     1.0  0.116291  0.256922 -0.817642 -0.995015\n",
      "GSM8105901                     1.0  0.522361 -0.468963 -0.782454 -0.203852\n",
      "GSM8105902                     1.0 -0.208534 -0.174747 -0.987014 -1.104161\n",
      "GSM8105903                     1.0  0.755866 -0.158793 -1.216798 -0.301321\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (220, 8272)\n",
      "For the feature 'Cardiovascular_Disease', the least common label is '0.0' with 12 occurrences. This represents 5.45% of the dataset.\n",
      "The distribution of the feature 'Cardiovascular_Disease' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Cardiovascular_Disease/GSE256539.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols\n",
    "print(f\"Original gene data shape: {gene_data.shape}\")\n",
    "\n",
    "try:\n",
    "    # Attempt to normalize gene symbols\n",
    "    gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
    "    print(f\"Normalized gene data shape: {gene_data_normalized.shape}\")\n",
    "except Exception as e:\n",
    "    print(f\"Gene normalization failed: {e}\")\n",
    "    # If normalization fails, use the original gene data\n",
    "    gene_data_normalized = gene_data.copy()\n",
    "    print(f\"Using original gene data with shape: {gene_data_normalized.shape}\")\n",
    "\n",
    "# Save the gene expression data \n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data_normalized.to_csv(out_gene_data_file)\n",
    "print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Recreate clinical features from scratch\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert individual IDs to binary trait values.\n",
    "    Based on the background information:\n",
    "    - IPAH patients have IDs with patterns like AH*, VA*, UC*, etc.\n",
    "    - Control samples have IDs with patterns like CC*\n",
    "    \"\"\"\n",
    "    if ':' in value:\n",
    "        # Extract the value after the colon and strip whitespace\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Based on the background, control subjects would be in the control pulmonary artery group\n",
    "    # The IDs \"CC*\" appear to be controls based on context and naming convention\n",
    "    if value.startswith('CC'):\n",
    "        return 0  # Control\n",
    "    else:\n",
    "        return 1  # IPAH case\n",
    "\n",
    "# Reload clinical data\n",
    "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
    "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "_, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "\n",
    "try:\n",
    "    # Create clinical features\n",
    "    clinical_features = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data, \n",
    "        trait=trait, \n",
    "        trait_row=0,  # Using individual ID information as identified in Step 2\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=None,  # No age information available\n",
    "        convert_age=None,\n",
    "        gender_row=None,  # No gender information available\n",
    "        convert_gender=None\n",
    "    )\n",
    "    \n",
    "    print(f\"Clinical features shape: {clinical_features.shape}\")\n",
    "    print(\"Clinical features preview:\")\n",
    "    print(preview_df(clinical_features))\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_features.to_csv(out_clinical_data_file)\n",
    "    \n",
    "    # Check if we have valid clinical data\n",
    "    if clinical_features.isna().all().all():\n",
    "        print(\"Clinical features contain only NaN values. Cannot proceed with linking.\")\n",
    "        raise ValueError(\"No valid clinical data found\")\n",
    "    \n",
    "    # 3. Link clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data_normalized)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "    print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty DataFrame\")\n",
    "    \n",
    "    # 4. Handle missing values\n",
    "    linked_data_clean = handle_missing_values(linked_data, trait)\n",
    "    print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
    "    \n",
    "    # Check if we still have data after handling missing values\n",
    "    if linked_data_clean.shape[0] == 0 or linked_data_clean.shape[1] <= 1:\n",
    "        raise ValueError(\"Dataset is empty after handling missing values\")\n",
    "    \n",
    "    # 5. Check for bias in the dataset\n",
    "    is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "    \n",
    "    # 6. Conduct final quality validation\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_biased,\n",
    "        df=linked_data_clean,\n",
    "        note=\"Dataset contains gene expression data from IPAH (Idiopathic Pulmonary Arterial Hypertension) patients and controls.\"\n",
    "    )\n",
    "    \n",
    "    # 7. Save the linked data if it's usable\n",
    "    if is_usable:\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        linked_data_clean.to_csv(out_data_file, index=True)\n",
    "        print(f\"Linked data saved to {out_data_file}\")\n",
    "    else:\n",
    "        print(\"Dataset deemed not usable for associative studies. Linked data not saved.\")\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"Error in data processing: {e}\")\n",
    "    # Ensure we still validate and record the dataset as not 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=True,\n",
    "        df=pd.DataFrame(),\n",
    "        note=f\"Processing error: {str(e)}\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to processing errors. Linked data not saved.\")"
   ]
  }
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