{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2b26b7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:41:36.885459Z",
     "iopub.status.busy": "2025-03-25T08:41:36.885124Z",
     "iopub.status.idle": "2025-03-25T08:41:37.046538Z",
     "shell.execute_reply": "2025-03-25T08:41:37.046116Z"
    }
   },
   "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 = \"Eczema\"\n",
    "cohort = \"GSE63741\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Eczema\"\n",
    "in_cohort_dir = \"../../input/GEO/Eczema/GSE63741\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Eczema/GSE63741.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Eczema/gene_data/GSE63741.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Eczema/clinical_data/GSE63741.csv\"\n",
    "json_path = \"../../output/preprocess/Eczema/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7846782c",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df60fe3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:41:37.047928Z",
     "iopub.status.busy": "2025-03-25T08:41:37.047793Z",
     "iopub.status.idle": "2025-03-25T08:41:37.069790Z",
     "shell.execute_reply": "2025-03-25T08:41:37.069417Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene Expression Analyses of Homo sapiens Inflammatory Skin Diseases\"\n",
      "!Series_summary\t\"Transcriptional profiling of Homo sapiens inflammatory skin diseases (whole skin biospies): Psoriasis (Pso), vs Atopic Dermatitis  (AD) vs Lichen planus (Li), vs Contact Eczema (KE), vs Healthy control (KO)\"\n",
      "!Series_summary\t\"In recent years, different genes and proteins have been highlighted as potential biomarkers for psoriasis, one of the most common inflammatory skin diseases worldwide. However, most of these markers are not psoriasis-specific but also found in other inflammatory disorders.  We performed an unsupervised cluster analysis of gene expression profiles in 150 psoriasis patients and other inflammatory skin diseases (atopic dermatitis, lichen planus, contact eczema, and healthy controls). We identified a cluster of IL-17/TNFα-associated genes specifically expressed in psoriasis, among which IL-36γ was the most outstanding marker. In subsequent immunohistological analyses IL-36γ was confirmed to be expressed in psoriasis lesions only. IL-36γ peripheral blood serum levels were found to be closely associated with disease activity, and they decreased after anti-TNFα-treatment. Furthermore, IL-36γ immunohistochemistry was found to be a helpful marker in the histological differential diagnosis between psoriasis and eczema in diagnostically challenging cases.   These features highlight IL-36γ as a valuable biomarker in psoriasis patients, both for diagnostic purposes and measurement of disease activity during the clinical course. Furthermore, IL-36γ might also provide a future drug target, due to its potential amplifier role in TNFα- and IL-17 pathways in psoriatic skin inflammation.   In recent years, different genes and proteins have been highlighted as potential biomarkers for psoriasis, one of the most common inflammatory skin diseases worldwide. However, most of these markers are not psoriasis-specific but also found in other inflammatory disorders.  We performed an unsupervised cluster analysis of gene expression profiles in 150 psoriasis patients and other inflammatory skin diseases (atopic dermatitis, lichen planus, contact eczema, and healthy controls). We identified a cluster of IL-17/TNFα-associated genes specifically expressed in psoriasis, among which IL-36γ was the most outstanding marker. In subsequent immunohistological analyses IL-36γ was confirmed to be expressed in psoriasis lesions only. IL-36γ peripheral blood serum levels were found to be closely associated with disease activity, and they decreased after anti-TNFα-treatment. Furthermore, IL-36γ immunohistochemistry was found to be a helpful marker in the histological differential diagnosis between psoriasis and eczema in diagnostically challenging cases.   These features highlight IL-36γ as a valuable biomarker in psoriasis patients, both for diagnostic purposes and measurement of disease activity during the clinical course. Furthermore, IL-36γ might also provide a future drug target, due to its potential amplifier role in TNFα- and IL-17 pathways in psoriatic skin inflammation.\"\n",
      "!Series_overall_design\t\"Ex vivo analyses: gene expression analyses (total RNA) of lesional skin versus common skin reference (two channel)\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: whole skin biopsy'], 1: ['sample type: skin biopsies from pool of 160 patients with skin disorders and healthy donors']}\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": "01532724",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68f4466b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:41:37.070782Z",
     "iopub.status.busy": "2025-03-25T08:41:37.070677Z",
     "iopub.status.idle": "2025-03-25T08:41:37.082095Z",
     "shell.execute_reply": "2025-03-25T08:41:37.081752Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{'GSM1556392': [0.0], 'GSM1556393': [0.0], 'GSM1556394': [0.0], 'GSM1556395': [0.0], 'GSM1556396': [0.0], 'GSM1556397': [0.0], 'GSM1556398': [0.0], 'GSM1556399': [0.0], 'GSM1556400': [0.0], 'GSM1556401': [0.0], 'GSM1556402': [0.0], 'GSM1556403': [0.0], 'GSM1556404': [0.0], 'GSM1556405': [0.0], 'GSM1556406': [0.0], 'GSM1556407': [0.0], 'GSM1556408': [0.0], 'GSM1556409': [0.0], 'GSM1556410': [0.0], 'GSM1556411': [0.0], 'GSM1556412': [0.0], 'GSM1556413': [0.0], 'GSM1556414': [0.0], 'GSM1556415': [0.0], 'GSM1556416': [0.0], 'GSM1556417': [0.0], 'GSM1556418': [0.0], 'GSM1556419': [0.0], 'GSM1556420': [0.0], 'GSM1556421': [0.0], 'GSM1556422': [0.0], 'GSM1556423': [0.0], 'GSM1556424': [0.0], 'GSM1556425': [0.0], 'GSM1556426': [0.0], 'GSM1556427': [0.0], 'GSM1556428': [0.0], 'GSM1556429': [0.0], 'GSM1556430': [0.0], 'GSM1556431': [0.0], 'GSM1556432': [0.0], 'GSM1556433': [0.0], 'GSM1556434': [0.0], 'GSM1556435': [0.0], 'GSM1556436': [0.0], 'GSM1556437': [0.0], 'GSM1556438': [0.0], 'GSM1556439': [0.0], 'GSM1556440': [0.0], 'GSM1556441': [0.0], 'GSM1556442': [0.0], 'GSM1556443': [0.0], 'GSM1556444': [0.0], 'GSM1556445': [0.0], 'GSM1556446': [0.0], 'GSM1556447': [0.0], 'GSM1556448': [0.0], 'GSM1556449': [0.0], 'GSM1556450': [0.0], 'GSM1556451': [0.0], 'GSM1556452': [0.0], 'GSM1556453': [0.0], 'GSM1556454': [0.0], 'GSM1556455': [0.0], 'GSM1556456': [0.0], 'GSM1556457': [0.0], 'GSM1556458': [0.0], 'GSM1556459': [0.0], 'GSM1556460': [0.0], 'GSM1556461': [0.0], 'GSM1556462': [0.0], 'GSM1556463': [0.0], 'GSM1556464': [0.0], 'GSM1556465': [0.0], 'GSM1556466': [0.0], 'GSM1556467': [0.0], 'GSM1556468': [0.0], 'GSM1556469': [0.0], 'GSM1556470': [0.0], 'GSM1556471': [0.0], 'GSM1556472': [0.0], 'GSM1556473': [0.0], 'GSM1556474': [0.0], 'GSM1556475': [0.0], 'GSM1556476': [0.0], 'GSM1556477': [0.0], 'GSM1556478': [0.0], 'GSM1556479': [0.0], 'GSM1556480': [0.0], 'GSM1556481': [0.0], 'GSM1556482': [0.0], 'GSM1556483': [0.0], 'GSM1556484': [0.0], 'GSM1556485': [0.0], 'GSM1556486': [0.0], 'GSM1556487': [0.0], 'GSM1556488': [0.0], 'GSM1556489': [0.0], 'GSM1556490': [0.0], 'GSM1556491': [0.0], 'GSM1556492': [0.0], 'GSM1556493': [0.0], 'GSM1556494': [0.0], 'GSM1556495': [0.0], 'GSM1556496': [0.0], 'GSM1556497': [0.0], 'GSM1556498': [0.0], 'GSM1556499': [0.0], 'GSM1556500': [0.0], 'GSM1556501': [0.0], 'GSM1556502': [0.0], 'GSM1556503': [0.0], 'GSM1556504': [0.0], 'GSM1556505': [0.0], 'GSM1556506': [0.0], 'GSM1556507': [0.0], 'GSM1556508': [0.0], 'GSM1556509': [0.0], 'GSM1556510': [0.0], 'GSM1556511': [0.0], 'GSM1556512': [0.0], 'GSM1556513': [0.0], 'GSM1556514': [0.0], 'GSM1556515': [0.0], 'GSM1556516': [0.0], 'GSM1556517': [0.0], 'GSM1556518': [0.0], 'GSM1556519': [0.0], 'GSM1556520': [0.0], 'GSM1556521': [0.0], 'GSM1556522': [0.0], 'GSM1556523': [0.0], 'GSM1556524': [0.0], 'GSM1556525': [0.0], 'GSM1556526': [0.0], 'GSM1556527': [0.0], 'GSM1556528': [0.0], 'GSM1556529': [0.0], 'GSM1556530': [0.0], 'GSM1556531': [0.0], 'GSM1556532': [0.0], 'GSM1556533': [0.0], 'GSM1556534': [0.0], 'GSM1556535': [0.0], 'GSM1556536': [0.0], 'GSM1556537': [0.0], 'GSM1556538': [0.0], 'GSM1556539': [0.0], 'GSM1556540': [0.0], 'GSM1556541': [0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Eczema/clinical_data/GSE63741.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on background information, this dataset contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# From the background information, we can see this is a study comparing different skin conditions\n",
    "# including Atopic Dermatitis (AD) which is a form of Eczema\n",
    "# The dataset contains samples from patients with Eczema (Contact Eczema - KE) and other conditions\n",
    "\n",
    "# For trait (Eczema), we need to infer from 'sample type' information\n",
    "trait_row = 1  # The information about disease status is in row 1\n",
    "\n",
    "# Age is not explicitly mentioned in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender is not explicitly mentioned in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert trait value to binary (0 or 1).\n",
    "    1 if the sample is from an Eczema patient (AD - Atopic Dermatitis or KE - Contact Eczema)\n",
    "    0 if the sample is from a non-Eczema patient or healthy control\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    else:\n",
    "        value = value.strip().lower()\n",
    "    \n",
    "    # Check if the value indicates Eczema\n",
    "    if 'atopic dermatitis' in value or 'contact eczema' in value or 'ad' in value or 'ke' in value:\n",
    "        return 1\n",
    "    elif 'healthy' in value or 'control' in value or 'ko' in value or 'psoriasis' in value or 'lichen planus' in value or 'pso' in value or 'li' in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# These functions are not needed as age and gender data are not available,\n",
    "# but we'll define them as placeholders\n",
    "def convert_age(value):\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Conduct 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",
    "    # 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 selected 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": "7349de24",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e83951f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:41:37.083122Z",
     "iopub.status.busy": "2025-03-25T08:41:37.083007Z",
     "iopub.status.idle": "2025-03-25T08:41:37.118278Z",
     "shell.execute_reply": "2025-03-25T08:41:37.117975Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Eczema/GSE63741/GSE63741_series_matrix.txt.gz\n",
      "Gene data shape: (1542, 150)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['3', '5', '16', '18', '20', '33', '35', '37', '39', '43', '47', '49',\n",
      "       '55', '57', '59', '61', '67', '71', '73', '81'],\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": "68d97f8a",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7f41dc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:41:37.119544Z",
     "iopub.status.busy": "2025-03-25T08:41:37.119433Z",
     "iopub.status.idle": "2025-03-25T08:41:37.121732Z",
     "shell.execute_reply": "2025-03-25T08:41:37.121452Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the observed identifiers which are numeric ('3', '5', '16', etc.), these are not human gene symbols.\n",
      "These appear to be probe IDs that require mapping to standard gene symbols.\n",
      "requires_gene_mapping = True\n"
     ]
    }
   ],
   "source": [
    "# The gene identifiers in the data appear to be numeric identifiers (e.g., '3', '5', '16', etc.)\n",
    "# These are not standard human gene symbols (which would look like BRCA1, TP53, IL6, etc.)\n",
    "# These appear to be probe IDs or some other numeric identifiers that need to be mapped to gene symbols\n",
    "\n",
    "# Therefore, we need to perform gene mapping\n",
    "requires_gene_mapping = True\n",
    "\n",
    "# Print the conclusion for clarity\n",
    "print(f\"Based on the observed identifiers which are numeric ('3', '5', '16', etc.), these are not human gene symbols.\")\n",
    "print(f\"These appear to be probe IDs that require mapping to standard gene symbols.\")\n",
    "print(f\"requires_gene_mapping = {requires_gene_mapping}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1809e337",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36057f55",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:41:37.122938Z",
     "iopub.status.busy": "2025-03-25T08:41:37.122830Z",
     "iopub.status.idle": "2025-03-25T08:41:37.423643Z",
     "shell.execute_reply": "2025-03-25T08:41:37.423257Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene annotation preview:\n",
      "Columns in gene annotation: ['ID', 'description', 'uniprot', 'gene ID', 'REFSEQ']\n",
      "{'ID': ['3', '5', '16', '18', '20'], 'description': ['IL1B: (IL1B) INTERLEUKIN-1 BETA PRECURSOR (IL-1 BETA) (CATABOLIN).', 'IL2: (IL2 OR IL-2) INTERLEUKIN-2 PRECURSOR (IL-2) (T-CELL GROWTH FACTOR) (TCGF) (ALDESLEUKIN).', 'IL7: (IL7 OR IL-7) INTERLEUKIN-7 PRECURSOR (IL-7).', 'IL8_HUMAN: (IL8) INTERLEUKIN-8 PRECURSOR (IL-8) (CXCL8) (MONOCYTE-DERIVED NEUTROPHIL CHEMOTACTIC FACTOR) (MDNCF) (T-CELL CHEMOTACTIC FACTOR) (NEUTROPHIL-ACTIVATING PROTEIN 1) (NAP-1) (LYMPHOCYTE-DERIVED NEUTROPHIL-ACTIVATING FACTOR) (LYNAP) (PROTEIN 3-10C) (NEUTROPHIL-ACTIVATING FACTOR) (NAF) (GRANULOCYTE CHEMOTACTIC PROTEIN 1) (GCP-1) (EMOCTAKIN).', 'IL9: (IL9) INTERLEUKIN-9 PRECURSOR (IL-9) (T-CELL GROWTH FACTOR P40) (P40 CYTOKINE).'], 'uniprot': ['sp|P01584,sp|Q96HE5,sp|Q9UCT6,sp|Q7RU01', 'sp|P01585,tr|Q13169,sp|P60568', 'sp|P13232', 'sp|P10145,sp|Q9C077,sp|Q96RG6,sp|Q6FGF6,sp|Q6LAE6', 'sp|P15248'], 'gene ID': ['3553', '3558', '3574', '3576', '3578'], 'REFSEQ': ['NM_000576', 'NM_000586', 'NM_000880', 'NM_000584', 'NM_000590']}\n",
      "\n",
      "Searching for platform information in SOFT file:\n",
      "Platform ID not found in first 100 lines\n",
      "\n",
      "Searching for gene symbol information in SOFT file:\n",
      "No explicit gene symbol references found in first 1000 lines\n",
      "\n",
      "Checking for additional annotation files in the directory:\n",
      "[]\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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Let's look for platform information in the SOFT file to understand the annotation better\n",
    "print(\"\\nSearching for platform information in SOFT file:\")\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for i, line in enumerate(f):\n",
    "        if '!Series_platform_id' in line:\n",
    "            print(line.strip())\n",
    "            break\n",
    "        if i > 100:  # Limit search to first 100 lines\n",
    "            print(\"Platform ID not found in first 100 lines\")\n",
    "            break\n",
    "\n",
    "# Check if the SOFT file includes any reference to gene symbols\n",
    "print(\"\\nSearching for gene symbol information in SOFT file:\")\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    gene_symbol_lines = []\n",
    "    for i, line in enumerate(f):\n",
    "        if 'GENE_SYMBOL' in line or 'gene_symbol' in line.lower() or 'symbol' in line.lower():\n",
    "            gene_symbol_lines.append(line.strip())\n",
    "        if i > 1000 and len(gene_symbol_lines) > 0:  # Limit search but ensure we found something\n",
    "            break\n",
    "    \n",
    "    if gene_symbol_lines:\n",
    "        print(\"Found references to gene symbols:\")\n",
    "        for line in gene_symbol_lines[:5]:  # Show just first 5 matches\n",
    "            print(line)\n",
    "    else:\n",
    "        print(\"No explicit gene symbol references found in first 1000 lines\")\n",
    "\n",
    "# Look for alternative annotation files or references in the directory\n",
    "print(\"\\nChecking for additional annotation files in the directory:\")\n",
    "all_files = os.listdir(in_cohort_dir)\n",
    "print([f for f in all_files if 'annotation' in f.lower() or 'platform' in f.lower() or 'gpl' in f.lower()])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0098e116",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "afcc815f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:41:37.425056Z",
     "iopub.status.busy": "2025-03-25T08:41:37.424932Z",
     "iopub.status.idle": "2025-03-25T08:41:37.669024Z",
     "shell.execute_reply": "2025-03-25T08:41:37.668691Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting gene identifier mapping...\n",
      "Sample of extracted gene symbols:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID Gene_Symbol\n",
      "0   3        IL1B\n",
      "1   5         IL2\n",
      "2  16         IL7\n",
      "3  18   IL8_HUMAN\n",
      "4  20         IL9\n",
      "Creating gene mapping dataframe...\n",
      "Converting probe data to gene expression data...\n",
      "Gene expression data shape after mapping: (1369, 150)\n",
      "First few gene symbols in mapped data:\n",
      "['ABCA12', 'ABI3BP', 'ABME', 'ACADVL', 'ACP5', 'ACPP', 'ACSL3', 'ACTA2', 'ACTB', 'ACTG1']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Eczema/gene_data/GSE63741.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify which columns contain gene IDs and gene symbols\n",
    "# From the preview, the 'ID' column matches the numeric identifiers in gene_data\n",
    "# The 'description' column contains gene symbols at the start (e.g., \"IL1B:\")\n",
    "\n",
    "print(\"Starting gene identifier mapping...\")\n",
    "\n",
    "# Function to extract gene symbols from the description field\n",
    "def extract_gene_symbol(description):\n",
    "    if not isinstance(description, str):\n",
    "        return None\n",
    "    # Extract text before the colon\n",
    "    match = re.match(r'^([^:]+):', description)\n",
    "    if match:\n",
    "        return match.group(1).strip()\n",
    "    return None\n",
    "\n",
    "# Add a column with extracted gene symbols\n",
    "gene_annotation['Gene_Symbol'] = gene_annotation['description'].apply(extract_gene_symbol)\n",
    "\n",
    "# Preview the extracted gene symbols\n",
    "print(\"Sample of extracted gene symbols:\")\n",
    "print(gene_annotation[['ID', 'Gene_Symbol']].head())\n",
    "\n",
    "# 2. Create gene mapping dataframe\n",
    "print(\"Creating gene mapping dataframe...\")\n",
    "mapping_df = gene_annotation[['ID', 'Gene_Symbol']].dropna()\n",
    "mapping_df = mapping_df.rename(columns={'Gene_Symbol': 'Gene'})\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene expression data\n",
    "print(\"Converting probe data to gene expression data...\")\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print shape and preview of the gene expression data\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First few gene symbols in mapped data:\")\n",
    "print(gene_data.index[:10].tolist())\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\"Gene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a578a8",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb2ec526",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:41:37.670348Z",
     "iopub.status.busy": "2025-03-25T08:41:37.670231Z",
     "iopub.status.idle": "2025-03-25T08:41:38.077464Z",
     "shell.execute_reply": "2025-03-25T08:41:38.077139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape after normalization: (1259, 150)\n",
      "Sample of normalized gene symbols: ['ABCA12', 'ABI1', 'ABI3BP', 'ACADVL', 'ACER1', 'ACER3', 'ACP3', 'ACP5', 'ACSL3', 'ACTA2']\n",
      "Normalized gene data saved to ../../output/preprocess/Eczema/gene_data/GSE63741.csv\n",
      "\n",
      "Loading clinical data...\n",
      "Clinical features shape: (1, 150)\n",
      "Clinical features preview:\n",
      "{'GSM1556392': [0.0], 'GSM1556393': [0.0], 'GSM1556394': [0.0], 'GSM1556395': [0.0], 'GSM1556396': [0.0], 'GSM1556397': [0.0], 'GSM1556398': [0.0], 'GSM1556399': [0.0], 'GSM1556400': [0.0], 'GSM1556401': [0.0], 'GSM1556402': [0.0], 'GSM1556403': [0.0], 'GSM1556404': [0.0], 'GSM1556405': [0.0], 'GSM1556406': [0.0], 'GSM1556407': [0.0], 'GSM1556408': [0.0], 'GSM1556409': [0.0], 'GSM1556410': [0.0], 'GSM1556411': [0.0], 'GSM1556412': [0.0], 'GSM1556413': [0.0], 'GSM1556414': [0.0], 'GSM1556415': [0.0], 'GSM1556416': [0.0], 'GSM1556417': [0.0], 'GSM1556418': [0.0], 'GSM1556419': [0.0], 'GSM1556420': [0.0], 'GSM1556421': [0.0], 'GSM1556422': [0.0], 'GSM1556423': [0.0], 'GSM1556424': [0.0], 'GSM1556425': [0.0], 'GSM1556426': [0.0], 'GSM1556427': [0.0], 'GSM1556428': [0.0], 'GSM1556429': [0.0], 'GSM1556430': [0.0], 'GSM1556431': [0.0], 'GSM1556432': [0.0], 'GSM1556433': [0.0], 'GSM1556434': [0.0], 'GSM1556435': [0.0], 'GSM1556436': [0.0], 'GSM1556437': [0.0], 'GSM1556438': [0.0], 'GSM1556439': [0.0], 'GSM1556440': [0.0], 'GSM1556441': [0.0], 'GSM1556442': [0.0], 'GSM1556443': [0.0], 'GSM1556444': [0.0], 'GSM1556445': [0.0], 'GSM1556446': [0.0], 'GSM1556447': [0.0], 'GSM1556448': [0.0], 'GSM1556449': [0.0], 'GSM1556450': [0.0], 'GSM1556451': [0.0], 'GSM1556452': [0.0], 'GSM1556453': [0.0], 'GSM1556454': [0.0], 'GSM1556455': [0.0], 'GSM1556456': [0.0], 'GSM1556457': [0.0], 'GSM1556458': [0.0], 'GSM1556459': [0.0], 'GSM1556460': [0.0], 'GSM1556461': [0.0], 'GSM1556462': [0.0], 'GSM1556463': [0.0], 'GSM1556464': [0.0], 'GSM1556465': [0.0], 'GSM1556466': [0.0], 'GSM1556467': [0.0], 'GSM1556468': [0.0], 'GSM1556469': [0.0], 'GSM1556470': [0.0], 'GSM1556471': [0.0], 'GSM1556472': [0.0], 'GSM1556473': [0.0], 'GSM1556474': [0.0], 'GSM1556475': [0.0], 'GSM1556476': [0.0], 'GSM1556477': [0.0], 'GSM1556478': [0.0], 'GSM1556479': [0.0], 'GSM1556480': [0.0], 'GSM1556481': [0.0], 'GSM1556482': [0.0], 'GSM1556483': [0.0], 'GSM1556484': [0.0], 'GSM1556485': [0.0], 'GSM1556486': [0.0], 'GSM1556487': [0.0], 'GSM1556488': [0.0], 'GSM1556489': [0.0], 'GSM1556490': [0.0], 'GSM1556491': [0.0], 'GSM1556492': [0.0], 'GSM1556493': [0.0], 'GSM1556494': [0.0], 'GSM1556495': [0.0], 'GSM1556496': [0.0], 'GSM1556497': [0.0], 'GSM1556498': [0.0], 'GSM1556499': [0.0], 'GSM1556500': [0.0], 'GSM1556501': [0.0], 'GSM1556502': [0.0], 'GSM1556503': [0.0], 'GSM1556504': [0.0], 'GSM1556505': [0.0], 'GSM1556506': [0.0], 'GSM1556507': [0.0], 'GSM1556508': [0.0], 'GSM1556509': [0.0], 'GSM1556510': [0.0], 'GSM1556511': [0.0], 'GSM1556512': [0.0], 'GSM1556513': [0.0], 'GSM1556514': [0.0], 'GSM1556515': [0.0], 'GSM1556516': [0.0], 'GSM1556517': [0.0], 'GSM1556518': [0.0], 'GSM1556519': [0.0], 'GSM1556520': [0.0], 'GSM1556521': [0.0], 'GSM1556522': [0.0], 'GSM1556523': [0.0], 'GSM1556524': [0.0], 'GSM1556525': [0.0], 'GSM1556526': [0.0], 'GSM1556527': [0.0], 'GSM1556528': [0.0], 'GSM1556529': [0.0], 'GSM1556530': [0.0], 'GSM1556531': [0.0], 'GSM1556532': [0.0], 'GSM1556533': [0.0], 'GSM1556534': [0.0], 'GSM1556535': [0.0], 'GSM1556536': [0.0], 'GSM1556537': [0.0], 'GSM1556538': [0.0], 'GSM1556539': [0.0], 'GSM1556540': [0.0], 'GSM1556541': [0.0]}\n",
      "\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (150, 1260)\n",
      "Linked data preview (first 5 rows, 5 columns):\n",
      "            Eczema  ABCA12    ABI1  ABI3BP  ACADVL\n",
      "GSM1556392     0.0 -0.2239 -0.0303  0.2056 -0.4101\n",
      "GSM1556393     0.0 -0.1982 -0.1678  0.0363 -0.1599\n",
      "GSM1556394     0.0  0.0442 -0.2122 -0.2602 -0.5624\n",
      "GSM1556395     0.0 -0.3612  0.2005 -0.0170 -1.1014\n",
      "GSM1556396     0.0  0.1589  0.0489 -0.3016 -0.2533\n",
      "\n",
      "Handling missing values...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (150, 1260)\n",
      "\n",
      "Checking for bias in dataset features...\n",
      "Quartiles for 'Eczema':\n",
      "  25%: 0.0\n",
      "  50% (Median): 0.0\n",
      "  75%: 0.0\n",
      "Min: 0.0\n",
      "Max: 0.0\n",
      "The distribution of the feature 'Eczema' in this dataset is severely biased.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset deemed not usable for associative studies. Linked data not saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols using NCBI Gene database information\n",
    "print(\"Normalizing gene symbols...\")\n",
    "try:\n",
    "    # Load the gene data if needed\n",
    "    if 'gene_data' not in locals() or gene_data is None:\n",
    "        gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
    "        \n",
    "    # Normalize 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",
    "    print(f\"Sample of normalized gene symbols: {normalized_gene_data.index[:10].tolist()}\")\n",
    "    \n",
    "    # Save the normalized gene data\n",
    "    normalized_gene_data.to_csv(out_gene_data_file)\n",
    "    print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "except Exception as e:\n",
    "    print(f\"Error normalizing gene symbols: {e}\")\n",
    "\n",
    "# 2. Load the clinical data that was saved in step 2\n",
    "print(\"\\nLoading clinical data...\")\n",
    "try:\n",
    "    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(f\"Clinical features shape: {clinical_features.shape}\")\n",
    "    print(\"Clinical features preview:\")\n",
    "    print(preview_df(clinical_features))\n",
    "    \n",
    "    # 3. Link clinical and genetic data\n",
    "    print(\"\\nLinking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "    if linked_data.shape[0] > 0 and linked_data.shape[1] > 5:\n",
    "        print(linked_data.iloc[:5, :5])\n",
    "    else:\n",
    "        print(linked_data)\n",
    "    \n",
    "    # 4. Handle missing values\n",
    "    print(\"\\nHandling 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",
    "    # 5. Check for bias in the dataset\n",
    "    print(\"\\nChecking for bias in dataset features...\")\n",
    "    is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "    \n",
    "    # 6. Conduct final quality validation\n",
    "    note = \"Dataset contains gene expression data from skin biopsies comparing different skin conditions including eczema (atopic dermatitis and contact eczema) against other conditions like psoriasis and healthy controls.\"\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=note\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 processing data: {e}\")\n",
    "    # If processing fails, we should still validate the dataset status\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,  # We know trait data is available from step 2\n",
    "        is_biased=True,  # Set to True to ensure it's not marked usable\n",
    "        df=pd.DataFrame(),  # Empty dataframe since processing failed\n",
    "        note=f\"Failed to process data: {e}\"\n",
    "    )\n",
    "    print(\"Dataset validation completed with error status.\")"
   ]
  }
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