{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca0ecf26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:34:40.034492Z",
     "iopub.status.busy": "2025-03-25T08:34:40.034279Z",
     "iopub.status.idle": "2025-03-25T08:34:40.197068Z",
     "shell.execute_reply": "2025-03-25T08:34:40.196761Z"
    }
   },
   "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 = \"Crohns_Disease\"\n",
    "cohort = \"GSE83448\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE83448\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Crohns_Disease/GSE83448.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE83448.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE83448.csv\"\n",
    "json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dbf9eed",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1bf33921",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:34:40.198467Z",
     "iopub.status.busy": "2025-03-25T08:34:40.198333Z",
     "iopub.status.idle": "2025-03-25T08:34:40.291557Z",
     "shell.execute_reply": "2025-03-25T08:34:40.291272Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Genome-wide transcriptional analysis in intestinal biopsies from Crohn's disease (CD) patients.\"\n",
      "!Series_summary\t\"Differential gene expression analysis between CD patients and controls to identify the transcriptional signature that defines the inflamed intestinal mucosa in CD.\"\n",
      "!Series_overall_design\t\"Intestinal biopsy samples were obtained from CD patients and healthy controls. RNA was subsequently extracted from each sample. Gene expression intensities were measured using GE Healthcare/Amersham Biosciences CodeLink Human Whole Genome Bioarray. After performing the gene expression quality control analysis, we characterized the transcriptional profile of the inflamed intestinal mucosa in CD.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: intestinal mucosa'], 1: ['inflammation: Control', 'inflammation: Inflamed margin', 'inflammation: Non-inflamed margin']}\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": "8ff34959",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6c98889",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:34:40.292660Z",
     "iopub.status.busy": "2025-03-25T08:34:40.292557Z",
     "iopub.status.idle": "2025-03-25T08:34:40.300806Z",
     "shell.execute_reply": "2025-03-25T08:34:40.300522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data preview:\n",
      "{'GSM2203115': [0.0], 'GSM2203116': [0.0], 'GSM2203117': [0.0], 'GSM2203118': [1.0], 'GSM2203119': [1.0], 'GSM2203120': [1.0], 'GSM2203121': [0.0], 'GSM2203122': [1.0], 'GSM2203123': [1.0], 'GSM2203124': [1.0], 'GSM2203125': [0.0], 'GSM2203126': [1.0], 'GSM2203127': [1.0], 'GSM2203128': [1.0], 'GSM2203129': [0.0], 'GSM2203130': [1.0], 'GSM2203131': [1.0], 'GSM2203132': [0.0], 'GSM2203133': [1.0], 'GSM2203134': [1.0], 'GSM2203135': [1.0], 'GSM2203136': [0.0], 'GSM2203137': [1.0], 'GSM2203138': [1.0], 'GSM2203139': [1.0], 'GSM2203140': [0.0], 'GSM2203141': [0.0], 'GSM2203142': [1.0], 'GSM2203143': [1.0], 'GSM2203144': [0.0], 'GSM2203145': [0.0], 'GSM2203146': [0.0], 'GSM2203147': [0.0], 'GSM2203148': [1.0], 'GSM2203149': [1.0], 'GSM2203150': [1.0], 'GSM2203151': [1.0], 'GSM2203152': [1.0], 'GSM2203153': [1.0], 'GSM2203154': [1.0], 'GSM2203155': [1.0], 'GSM2203156': [1.0], 'GSM2203157': [1.0], 'GSM2203158': [1.0], 'GSM2203159': [1.0], 'GSM2203160': [1.0], 'GSM2203161': [1.0], 'GSM2203162': [1.0], 'GSM2203163': [1.0], 'GSM2203164': [1.0], 'GSM2203165': [1.0], 'GSM2203166': [1.0], 'GSM2203167': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE83448.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Analyze gene expression data availability\n",
    "# From the background info, we can see this is a study with gene expression data from GE Healthcare/Amersham Biosciences CodeLink Human Whole Genome Bioarray\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# Looking at the dictionary, we can see that key 1 has inflammation status\n",
    "# We can use this to infer Crohn's Disease status (inflamed = CD patient, control = healthy control)\n",
    "trait_row = 1\n",
    "# Age data is not available in the dictionary\n",
    "age_row = None\n",
    "# Gender data is not available in the dictionary\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "# Define conversion functions for each variable\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert inflammation status to binary Crohn's Disease indicator.\n",
    "    0 = No CD (Control), 1 = CD (Inflamed margin or Non-inflamed margin)\n",
    "    \"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value == \"Control\":\n",
    "        return 0\n",
    "    elif value in [\"Inflamed margin\", \"Non-inflamed margin\"]:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# No age data, but define the function as required\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value.\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "# No gender data, but define the function as required\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary value (0=female, 1=male).\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    \n",
    "    if value in ['female', 'f']:\n",
    "        return 0\n",
    "    elif value in ['male', 'm']:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save metadata - initial filtering check\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. Extract clinical features if trait data is available\n",
    "if trait_row is not None:\n",
    "    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 clinical data\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save clinical data to CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4665e22e",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c06530fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:34:40.301772Z",
     "iopub.status.busy": "2025-03-25T08:34:40.301671Z",
     "iopub.status.idle": "2025-03-25T08:34:40.438609Z",
     "shell.execute_reply": "2025-03-25T08:34:40.438237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['GE469557', 'GE469567', 'GE469590', 'GE469632', 'GE469690', 'GE469802',\n",
      "       'GE469817', 'GE469849', 'GE469866', 'GE469875', 'GE469953', 'GE470103',\n",
      "       'GE470130', 'GE470157', 'GE470169', 'GE470208', 'GE470218', 'GE470249',\n",
      "       'GE470296', 'GE470328'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 20902 genes × 53 samples\n"
     ]
    }
   ],
   "source": [
    "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract the gene expression 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)\n",
    "print(\"\\nFirst 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# 4. Print the dimensions of the gene expression data\n",
    "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091d77b6",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dfa92f1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:34:40.439902Z",
     "iopub.status.busy": "2025-03-25T08:34:40.439793Z",
     "iopub.status.idle": "2025-03-25T08:34:40.441648Z",
     "shell.execute_reply": "2025-03-25T08:34:40.441381Z"
    }
   },
   "outputs": [],
   "source": [
    "# Examine the gene identifiers\n",
    "# These identifiers (like GE469557) are not standard human gene symbols\n",
    "# Standard human gene symbols would be like BRCA1, TP53, etc.\n",
    "# These look like custom probes/identifiers specific to a microarray platform\n",
    "# They would need to be mapped to standard gene symbols for biological interpretation\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a29bd4",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffa0f3a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:34:40.442749Z",
     "iopub.status.busy": "2025-03-25T08:34:40.442650Z",
     "iopub.status.idle": "2025-03-25T08:34:41.776334Z",
     "shell.execute_reply": "2025-03-25T08:34:41.775955Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['GE469530', 'GE469548', 'GE469549', 'GE469555', 'GE469557'], 'GB_ACC': ['AI650595.1', 'BU686968.1', 'BU623208.1', 'BE045962.1', 'AY077696.1'], 'Probe_Name': ['GE469530', 'GE469548', 'GE469549', 'GE469555', 'GE469557'], 'Probe_Type': ['DISCOVERY', 'DISCOVERY', 'DISCOVERY', 'DISCOVERY', 'DISCOVERY'], 'DESCRIPTION': [\"wa92h11x1 NCI_CGAP_GC6 cDNA clone IMAGE:2303685 3'\", \"UI-CF-DU1-ado-i-08-0-UIs1 UI-CF-DU1 cDNA clone UI-CF-DU1-ado-i-08-0-UI 3'\", \"UI-H-FL1-bgd-j-14-0-UI.s1 NCI_CGAP_FL1 cDNA clone UI-H-FL1-bgd-j-14-0-UI 3', mRNA sequence\", \"hd90g04x4 NCI_CGAP_GC6 cDNA clone IMAGE:2916822 3'\", 'clone qd65g07 PRED16 protein (PRED16) mRNA'], 'SPOT_ID': [nan, nan, nan, nan, nan]}\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths using geo_get_relevant_filepaths function\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. 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",
    "# 3. 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": "f830b849",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2ecca5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:34:41.777681Z",
     "iopub.status.busy": "2025-03-25T08:34:41.777563Z",
     "iopub.status.idle": "2025-03-25T08:34:44.226206Z",
     "shell.execute_reply": "2025-03-25T08:34:44.225820Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking for additional columns in annotation data:\n",
      "Column 'ID' sample: ['GE469530' 'GE469548' 'GE469549']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'GB_ACC' sample: ['AI650595.1' 'BU686968.1' 'BU623208.1']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'Probe_Name' sample: ['GE469530' 'GE469548' 'GE469549']\n",
      "Column 'Probe_Type' sample: ['DISCOVERY']\n",
      "Column 'DESCRIPTION' sample: [\"wa92h11x1 NCI_CGAP_GC6 cDNA clone IMAGE:2303685 3'\"\n",
      " \"UI-CF-DU1-ado-i-08-0-UIs1 UI-CF-DU1 cDNA clone UI-CF-DU1-ado-i-08-0-UI 3'\"\n",
      " \"UI-H-FL1-bgd-j-14-0-UI.s1 NCI_CGAP_FL1 cDNA clone UI-H-FL1-bgd-j-14-0-UI 3', mRNA sequence\"]\n",
      "Column 'SPOT_ID' sample: ['INCYTE UNIQUE']\n",
      "\n",
      "Using GenBank accessions as gene identifiers.\n",
      "\n",
      "Gene mapping dataframe shape: (1156663, 2)\n",
      "Sample of gene mapping:\n",
      "{'ID': ['GE469530', 'GE469548', 'GE469549', 'GE469555', 'GE469557'], 'Gene': ['AI650595.1', 'BU686968.1', 'BU623208.1', 'BE045962.1', 'AY077696.1']}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After mapping - Gene data dimensions: (5353, 53)\n",
      "\n",
      "First few gene identifiers after mapping:\n",
      "Index(['AA010870', 'AA021186', 'AA029225', 'AA057423', 'AA058586', 'AA127601',\n",
      "       'AA149620', 'AA150617', 'AA166934', 'AA187037'],\n",
      "      dtype='object', name='Gene')\n",
      "\n",
      "Note: The dataset is using GenBank accessions rather than standard gene symbols.\n",
      "This may affect downstream analysis that relies on gene symbol annotations.\n",
      "\n",
      "Gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE83448.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Look for alternative gene symbol columns in the annotation data\n",
    "# First, check if there are any hidden/unprefixed columns that might contain gene symbols\n",
    "print(\"Checking for additional columns in annotation data:\")\n",
    "for col in gene_annotation.columns:\n",
    "    unique_values = gene_annotation[col].dropna().unique()\n",
    "    if len(unique_values) > 0:\n",
    "        print(f\"Column '{col}' sample: {unique_values[:3]}\")\n",
    "\n",
    "# Since we don't see standard gene symbols, we'll use GB_ACC (GenBank accessions)\n",
    "# as identifiers for the gene expression data\n",
    "print(\"\\nUsing GenBank accessions as gene identifiers.\")\n",
    "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'GB_ACC')\n",
    "\n",
    "# Check the mapping dataframe\n",
    "print(f\"\\nGene mapping dataframe shape: {gene_mapping.shape}\")\n",
    "print(\"Sample of gene mapping:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Convert probe-level measurements to gene expression\n",
    "# Note: We're working with accession numbers, not gene symbols\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Preview the results\n",
    "print(\"\\nAfter mapping - Gene data dimensions:\", gene_data.shape)\n",
    "print(\"\\nFirst few gene identifiers after mapping:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Skip normalization since these are not standard gene symbols\n",
    "# We'll keep the accession numbers as identifiers\n",
    "print(\"\\nNote: The dataset is using GenBank accessions rather than standard gene symbols.\")\n",
    "print(\"This may affect downstream analysis that relies on gene symbol annotations.\")\n",
    "\n",
    "# Save the gene expression data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9445cb6",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9256f02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:34:44.227605Z",
     "iopub.status.busy": "2025-03-25T08:34:44.227497Z",
     "iopub.status.idle": "2025-03-25T08:34:45.641361Z",
     "shell.execute_reply": "2025-03-25T08:34:45.640822Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing gene expression data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE83448.csv\n",
      "Gene data shape: 5353 genes × 53 samples\n",
      "Extracting clinical features from original clinical data...\n",
      "Clinical features saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE83448.csv\n",
      "Clinical features preview:\n",
      "{'GSM2203115': [0.0], 'GSM2203116': [0.0], 'GSM2203117': [0.0], 'GSM2203118': [1.0], 'GSM2203119': [1.0], 'GSM2203120': [1.0], 'GSM2203121': [0.0], 'GSM2203122': [1.0], 'GSM2203123': [1.0], 'GSM2203124': [1.0], 'GSM2203125': [0.0], 'GSM2203126': [1.0], 'GSM2203127': [1.0], 'GSM2203128': [1.0], 'GSM2203129': [0.0], 'GSM2203130': [1.0], 'GSM2203131': [1.0], 'GSM2203132': [0.0], 'GSM2203133': [1.0], 'GSM2203134': [1.0], 'GSM2203135': [1.0], 'GSM2203136': [0.0], 'GSM2203137': [1.0], 'GSM2203138': [1.0], 'GSM2203139': [1.0], 'GSM2203140': [0.0], 'GSM2203141': [0.0], 'GSM2203142': [1.0], 'GSM2203143': [1.0], 'GSM2203144': [0.0], 'GSM2203145': [0.0], 'GSM2203146': [0.0], 'GSM2203147': [0.0], 'GSM2203148': [1.0], 'GSM2203149': [1.0], 'GSM2203150': [1.0], 'GSM2203151': [1.0], 'GSM2203152': [1.0], 'GSM2203153': [1.0], 'GSM2203154': [1.0], 'GSM2203155': [1.0], 'GSM2203156': [1.0], 'GSM2203157': [1.0], 'GSM2203158': [1.0], 'GSM2203159': [1.0], 'GSM2203160': [1.0], 'GSM2203161': [1.0], 'GSM2203162': [1.0], 'GSM2203163': [1.0], 'GSM2203164': [1.0], 'GSM2203165': [1.0], 'GSM2203166': [1.0], 'GSM2203167': [1.0]}\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (53, 5354)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (53, 5354)\n",
      "\n",
      "Checking for bias in feature variables:\n",
      "For the feature 'Crohns_Disease', the least common label is '0.0' with 14 occurrences. This represents 26.42% of the dataset.\n",
      "The distribution of the feature 'Crohns_Disease' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Crohns_Disease/GSE83448.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Skip gene symbol normalization and use the accession numbers directly\n",
    "print(\"Processing gene expression data...\")\n",
    "# Don't normalize - these are GenBank accessions, not gene symbols\n",
    "gene_data_normalized = gene_data  # Use the original gene data with accession numbers\n",
    "\n",
    "# Save the gene data (without normalization)\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
    "print(f\"Gene data shape: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# 2. Extract clinical features from scratch\n",
    "print(\"Extracting clinical features from original clinical data...\")\n",
    "clinical_features = geo_select_clinical_features(\n",
    "    clinical_data, \n",
    "    trait, \n",
    "    trait_row,\n",
    "    convert_trait,\n",
    "    age_row,\n",
    "    convert_age,\n",
    "    gender_row,\n",
    "    convert_gender\n",
    ")\n",
    "\n",
    "# Save the extracted clinical features\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "\n",
    "print(\"Clinical features preview:\")\n",
    "print(preview_df(clinical_features))\n",
    "\n",
    "# Check if clinical features were successfully extracted\n",
    "if clinical_features.empty:\n",
    "    print(\"Failed to extract clinical features. Dataset cannot be processed further.\")\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=False,\n",
    "        is_biased=True,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Clinical features could not be extracted from the dataset.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to lack of clinical features.\")\n",
    "else:\n",
    "    # 2. Link clinical and genetic data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "    # Check if the linked data has gene features\n",
    "    if linked_data.shape[1] <= 1:\n",
    "        print(\"Error: Linked data has no gene features. Dataset cannot be processed further.\")\n",
    "        is_usable = validate_and_save_cohort_info(\n",
    "            is_final=True,\n",
    "            cohort=cohort,\n",
    "            info_path=json_path,\n",
    "            is_gene_available=False,\n",
    "            is_trait_available=True,\n",
    "            is_biased=True,\n",
    "            df=linked_data,\n",
    "            note=\"Failed to link gene expression data with clinical features.\"\n",
    "        )\n",
    "    else:\n",
    "        # 3. Handle missing values systematically\n",
    "        linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
    "        print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "        \n",
    "        # Check if there are still samples after missing value handling\n",
    "        if linked_data.shape[0] == 0:\n",
    "            print(\"Error: No samples remain after handling missing values.\")\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=True,\n",
    "                df=pd.DataFrame(),\n",
    "                note=\"All samples were removed during missing value handling.\"\n",
    "            )\n",
    "        else:\n",
    "            # 4. Check if the dataset is biased\n",
    "            print(\"\\nChecking for bias in feature variables:\")\n",
    "            is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "            # 5. 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,\n",
    "                note=\"Dataset contains gene expression data for Crohn's Disease patients and healthy controls.\"\n",
    "            )\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 deemed not usable for trait association studies, linked data not saved.\")"
   ]
  }
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