{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0569e62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:40:33.304790Z",
     "iopub.status.busy": "2025-03-25T08:40:33.304384Z",
     "iopub.status.idle": "2025-03-25T08:40:33.471695Z",
     "shell.execute_reply": "2025-03-25T08:40:33.471372Z"
    }
   },
   "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 = \"GSE123088\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Eczema\"\n",
    "in_cohort_dir = \"../../input/GEO/Eczema/GSE123088\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Eczema/GSE123088.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Eczema/gene_data/GSE123088.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Eczema/clinical_data/GSE123088.csv\"\n",
    "json_path = \"../../output/preprocess/Eczema/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdd2df7",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff77bcd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:40:33.473107Z",
     "iopub.status.busy": "2025-03-25T08:40:33.472959Z",
     "iopub.status.idle": "2025-03-25T08:40:33.747524Z",
     "shell.execute_reply": "2025-03-25T08:40:33.747184Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases\"\n",
      "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "!Series_overall_design\t\"Refer to individual Series\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['cell type: CD4+ T cells'], 1: ['primary diagnosis: ASTHMA', 'primary diagnosis: ATHEROSCLEROSIS', 'primary diagnosis: BREAST_CANCER', 'primary diagnosis: CHRONIC_LYMPHOCYTIC_LEUKEMIA', 'primary diagnosis: CROHN_DISEASE', 'primary diagnosis: ATOPIC_ECZEMA', 'primary diagnosis: HEALTHY_CONTROL', 'primary diagnosis: INFLUENZA', 'primary diagnosis: OBESITY', 'primary diagnosis: PSORIASIS', 'primary diagnosis: SEASONAL_ALLERGIC_RHINITIS', 'primary diagnosis: TYPE_1_DIABETES', 'primary diagnosis: ACUTE_TONSILLITIS', 'primary diagnosis: ULCERATIVE_COLITIS', 'primary diagnosis: Breast cancer', 'primary diagnosis: Control'], 2: ['Sex: Male', 'diagnosis2: ATOPIC_ECZEMA', 'Sex: Female', 'diagnosis2: ATHEROSCLEROSIS', 'diagnosis2: ASTHMA_OBESITY', 'diagnosis2: ASTHMA', 'diagnosis2: ASTMHA_SEASONAL_ALLERGIC_RHINITIS', 'diagnosis2: OBESITY'], 3: ['age: 56', 'Sex: Male', 'age: 20', 'age: 51', 'age: 37', 'age: 61', 'age: 31', 'age: 41', 'age: 80', 'age: 53', 'age: 73', 'age: 60', 'age: 76', 'age: 77', 'age: 74', 'age: 69', 'age: 81', 'age: 70', 'age: 82', 'age: 67', 'age: 78', 'age: 72', 'age: 66', 'age: 36', 'age: 45', 'age: 65', 'age: 48', 'age: 50', 'age: 24', 'age: 42'], 4: [nan, 'age: 63', 'age: 74', 'age: 49', 'age: 60', 'age: 68', 'age: 38', 'age: 16', 'age: 12', 'age: 27']}\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": "6e5170a7",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42ed76ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:40:33.748684Z",
     "iopub.status.busy": "2025-03-25T08:40:33.748580Z",
     "iopub.status.idle": "2025-03-25T08:40:33.773690Z",
     "shell.execute_reply": "2025-03-25T08:40:33.773407Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of clinical features:\n",
      "{'GSM3494884': [nan, 56.0, 1.0], 'GSM3494885': [nan, nan, nan], 'GSM3494886': [nan, 20.0, 0.0], 'GSM3494887': [nan, 51.0, 0.0], 'GSM3494888': [nan, 37.0, 1.0], 'GSM3494889': [nan, 61.0, 1.0], 'GSM3494890': [nan, nan, nan], 'GSM3494891': [nan, 31.0, 1.0], 'GSM3494892': [nan, 56.0, 0.0], 'GSM3494893': [nan, 41.0, 0.0], 'GSM3494894': [nan, 61.0, 0.0], 'GSM3494895': [nan, nan, nan], 'GSM3494896': [nan, 80.0, 1.0], 'GSM3494897': [nan, 53.0, 1.0], 'GSM3494898': [nan, 61.0, 1.0], 'GSM3494899': [nan, 73.0, 1.0], 'GSM3494900': [nan, 60.0, 1.0], 'GSM3494901': [nan, 76.0, 1.0], 'GSM3494902': [nan, 77.0, 0.0], 'GSM3494903': [nan, 74.0, 0.0], 'GSM3494904': [nan, 69.0, 1.0], 'GSM3494905': [nan, 77.0, 0.0], 'GSM3494906': [nan, 81.0, 0.0], 'GSM3494907': [nan, 70.0, 0.0], 'GSM3494908': [nan, 82.0, 0.0], 'GSM3494909': [nan, 69.0, 0.0], 'GSM3494910': [nan, 82.0, 0.0], 'GSM3494911': [nan, 67.0, 0.0], 'GSM3494912': [nan, 67.0, 0.0], 'GSM3494913': [nan, 78.0, 0.0], 'GSM3494914': [nan, 67.0, 0.0], 'GSM3494915': [nan, 74.0, 1.0], 'GSM3494916': [nan, nan, nan], 'GSM3494917': [nan, 51.0, 1.0], 'GSM3494918': [nan, 72.0, 1.0], 'GSM3494919': [nan, 66.0, 1.0], 'GSM3494920': [nan, 80.0, 0.0], 'GSM3494921': [nan, 36.0, 1.0], 'GSM3494922': [nan, 67.0, 0.0], 'GSM3494923': [nan, 31.0, 0.0], 'GSM3494924': [nan, 31.0, 0.0], 'GSM3494925': [nan, 45.0, 0.0], 'GSM3494926': [nan, 56.0, 0.0], 'GSM3494927': [nan, 65.0, 0.0], 'GSM3494928': [nan, 53.0, 0.0], 'GSM3494929': [nan, 48.0, 0.0], 'GSM3494930': [nan, 50.0, 0.0], 'GSM3494931': [nan, 76.0, 1.0], 'GSM3494932': [1.0, nan, nan], 'GSM3494933': [1.0, 24.0, 0.0], 'GSM3494934': [1.0, 42.0, 0.0], 'GSM3494935': [1.0, 76.0, 1.0], 'GSM3494936': [1.0, 22.0, 1.0], 'GSM3494937': [1.0, nan, nan], 'GSM3494938': [1.0, 23.0, 0.0], 'GSM3494939': [0.0, 34.0, 1.0], 'GSM3494940': [0.0, 43.0, 1.0], 'GSM3494941': [0.0, 47.0, 1.0], 'GSM3494942': [0.0, 24.0, 0.0], 'GSM3494943': [0.0, 55.0, 1.0], 'GSM3494944': [0.0, 48.0, 1.0], 'GSM3494945': [0.0, 58.0, 1.0], 'GSM3494946': [0.0, 30.0, 0.0], 'GSM3494947': [0.0, 28.0, 1.0], 'GSM3494948': [0.0, 41.0, 0.0], 'GSM3494949': [0.0, 63.0, 1.0], 'GSM3494950': [0.0, 55.0, 0.0], 'GSM3494951': [0.0, 55.0, 0.0], 'GSM3494952': [0.0, 67.0, 1.0], 'GSM3494953': [0.0, 47.0, 0.0], 'GSM3494954': [0.0, 46.0, 0.0], 'GSM3494955': [0.0, 49.0, 1.0], 'GSM3494956': [0.0, 23.0, 1.0], 'GSM3494957': [0.0, 68.0, 1.0], 'GSM3494958': [0.0, 39.0, 1.0], 'GSM3494959': [0.0, 24.0, 1.0], 'GSM3494960': [0.0, 36.0, 0.0], 'GSM3494961': [0.0, 58.0, 0.0], 'GSM3494962': [0.0, 38.0, 0.0], 'GSM3494963': [0.0, 27.0, 0.0], 'GSM3494964': [0.0, 67.0, 0.0], 'GSM3494965': [0.0, 61.0, 1.0], 'GSM3494966': [0.0, 69.0, 1.0], 'GSM3494967': [0.0, 63.0, 1.0], 'GSM3494968': [0.0, 60.0, 0.0], 'GSM3494969': [0.0, 17.0, 1.0], 'GSM3494970': [0.0, 10.0, 0.0], 'GSM3494971': [0.0, 9.0, 1.0], 'GSM3494972': [0.0, 13.0, 0.0], 'GSM3494973': [0.0, 10.0, 1.0], 'GSM3494974': [0.0, 13.0, 0.0], 'GSM3494975': [0.0, 15.0, 1.0], 'GSM3494976': [0.0, 12.0, 1.0], 'GSM3494977': [0.0, 13.0, 1.0], 'GSM3494978': [nan, 81.0, 0.0], 'GSM3494979': [nan, 94.0, 0.0], 'GSM3494980': [nan, 51.0, 1.0], 'GSM3494981': [nan, 40.0, 1.0], 'GSM3494982': [nan, nan, nan], 'GSM3494983': [nan, 97.0, 1.0], 'GSM3494984': [nan, 23.0, 1.0], 'GSM3494985': [nan, 93.0, 0.0], 'GSM3494986': [nan, 58.0, 1.0], 'GSM3494987': [nan, 28.0, 0.0], 'GSM3494988': [nan, 54.0, 1.0], 'GSM3494989': [nan, 15.0, 1.0], 'GSM3494990': [nan, 8.0, 1.0], 'GSM3494991': [nan, 11.0, 1.0], 'GSM3494992': [nan, 12.0, 1.0], 'GSM3494993': [nan, 8.0, 0.0], 'GSM3494994': [nan, 14.0, 1.0], 'GSM3494995': [nan, 8.0, 0.0], 'GSM3494996': [nan, 10.0, 1.0], 'GSM3494997': [nan, 14.0, 1.0], 'GSM3494998': [nan, 13.0, 1.0], 'GSM3494999': [nan, 40.0, 0.0], 'GSM3495000': [nan, 52.0, 0.0], 'GSM3495001': [nan, 42.0, 0.0], 'GSM3495002': [nan, 29.0, 0.0], 'GSM3495003': [nan, 43.0, 0.0], 'GSM3495004': [nan, 41.0, 0.0], 'GSM3495005': [nan, 54.0, 1.0], 'GSM3495006': [nan, 42.0, 1.0], 'GSM3495007': [nan, 49.0, 1.0], 'GSM3495008': [nan, 45.0, 0.0], 'GSM3495009': [nan, 56.0, 1.0], 'GSM3495010': [nan, 64.0, 0.0], 'GSM3495011': [nan, 71.0, 0.0], 'GSM3495012': [nan, 48.0, 0.0], 'GSM3495013': [nan, 20.0, 1.0], 'GSM3495014': [nan, 53.0, 0.0], 'GSM3495015': [nan, 32.0, 0.0], 'GSM3495016': [nan, 26.0, 0.0], 'GSM3495017': [nan, 28.0, 0.0], 'GSM3495018': [nan, 47.0, 0.0], 'GSM3495019': [nan, 24.0, 0.0], 'GSM3495020': [nan, 48.0, 0.0], 'GSM3495021': [nan, nan, nan], 'GSM3495022': [nan, 19.0, 0.0], 'GSM3495023': [nan, 41.0, 0.0], 'GSM3495024': [nan, 38.0, 0.0], 'GSM3495025': [nan, nan, nan], 'GSM3495026': [nan, 15.0, 0.0], 'GSM3495027': [nan, 12.0, 1.0], 'GSM3495028': [nan, 13.0, 0.0], 'GSM3495029': [nan, nan, nan], 'GSM3495030': [nan, 11.0, 1.0], 'GSM3495031': [nan, nan, nan], 'GSM3495032': [nan, 16.0, 1.0], 'GSM3495033': [nan, 11.0, 1.0], 'GSM3495034': [nan, nan, nan], 'GSM3495035': [nan, 35.0, 0.0], 'GSM3495036': [nan, 26.0, 0.0], 'GSM3495037': [nan, 39.0, 0.0], 'GSM3495038': [nan, 46.0, 0.0], 'GSM3495039': [nan, 42.0, 0.0], 'GSM3495040': [nan, 20.0, 1.0], 'GSM3495041': [nan, 69.0, 1.0], 'GSM3495042': [nan, 69.0, 0.0], 'GSM3495043': [nan, 47.0, 1.0], 'GSM3495044': [nan, 47.0, 1.0], 'GSM3495045': [nan, 56.0, 0.0], 'GSM3495046': [nan, 54.0, 0.0], 'GSM3495047': [nan, 53.0, 0.0], 'GSM3495048': [nan, 50.0, 0.0], 'GSM3495049': [nan, 22.0, 1.0], 'GSM3495050': [nan, 62.0, 0.0], 'GSM3495051': [nan, 74.0, 0.0], 'GSM3495052': [0.0, 57.0, 0.0], 'GSM3495053': [0.0, 47.0, 0.0], 'GSM3495054': [nan, 70.0, 0.0], 'GSM3495055': [nan, 50.0, 0.0], 'GSM3495056': [0.0, 52.0, 0.0], 'GSM3495057': [nan, 43.0, 0.0], 'GSM3495058': [0.0, 57.0, 0.0], 'GSM3495059': [nan, 53.0, 0.0], 'GSM3495060': [nan, 70.0, 0.0], 'GSM3495061': [0.0, 41.0, 0.0], 'GSM3495062': [nan, 61.0, 0.0], 'GSM3495063': [0.0, 39.0, 0.0], 'GSM3495064': [0.0, 58.0, 0.0], 'GSM3495065': [nan, 55.0, 0.0], 'GSM3495066': [nan, 63.0, 0.0], 'GSM3495067': [0.0, 60.0, 0.0], 'GSM3495068': [nan, 43.0, 0.0], 'GSM3495069': [nan, 68.0, 0.0], 'GSM3495070': [nan, 67.0, 0.0], 'GSM3495071': [nan, 50.0, 0.0], 'GSM3495072': [nan, 67.0, 0.0], 'GSM3495073': [0.0, 51.0, 0.0], 'GSM3495074': [0.0, 59.0, 0.0], 'GSM3495075': [0.0, 44.0, 0.0], 'GSM3495076': [nan, 35.0, 0.0], 'GSM3495077': [nan, 83.0, 0.0], 'GSM3495078': [nan, 78.0, 0.0], 'GSM3495079': [nan, 88.0, 0.0], 'GSM3495080': [nan, 41.0, 0.0], 'GSM3495081': [0.0, 60.0, 0.0], 'GSM3495082': [nan, 72.0, 0.0], 'GSM3495083': [nan, 53.0, 0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Eczema/clinical_data/GSE123088.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine gene expression data availability\n",
    "# This dataset appears to be a SuperSeries combining several studies\n",
    "# Since it mentions CD4+ T cells and includes various diagnoses, it likely contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# Trait (Eczema) appears in row 1 as \"primary diagnosis: ATOPIC_ECZEMA\"\n",
    "trait_row = 1\n",
    "\n",
    "# Age appears in row 3 and continues in row 4\n",
    "age_row = 3\n",
    "\n",
    "# Gender/Sex appears in rows 2 and 3\n",
    "gender_row = 2\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Check if Eczema is present in any form\n",
    "    if \"ATOPIC_ECZEMA\" in value:\n",
    "        return 1\n",
    "    elif \"HEALTHY_CONTROL\" in value or \"Control\" in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value.lower() == \"female\":\n",
    "        return 0\n",
    "    elif value.lower() == \"male\":\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available\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 (if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features\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 extracted features\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(\"Preview of clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3e3d004",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3539d90c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:40:33.774766Z",
     "iopub.status.busy": "2025-03-25T08:40:33.774664Z",
     "iopub.status.idle": "2025-03-25T08:40:34.269550Z",
     "shell.execute_reply": "2025-03-25T08:40:34.269177Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Eczema/GSE123088/GSE123088_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (24166, 204)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['1', '2', '3', '9', '10', '12', '13', '14', '15', '16', '18', '19',\n",
      "       '20', '21', '22', '23', '24', '25', '26', '27'],\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": "8529df35",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db623bcf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:40:34.270808Z",
     "iopub.status.busy": "2025-03-25T08:40:34.270704Z",
     "iopub.status.idle": "2025-03-25T08:40:34.272513Z",
     "shell.execute_reply": "2025-03-25T08:40:34.272257Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers appear to be numeric IDs, not human gene symbols.\n",
    "# They are likely probe IDs or some other form of identifiers that need to be mapped.\n",
    "# Looking at the first 20 identifiers, they are simply numbers like '1', '2', '3', etc.\n",
    "# These are not standard human gene symbols, which would typically be alphanumeric like 'BRCA1', 'TP53', etc.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58e26c6f",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02a6fc9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:40:34.273629Z",
     "iopub.status.busy": "2025-03-25T08:40:34.273533Z",
     "iopub.status.idle": "2025-03-25T08:40:40.135514Z",
     "shell.execute_reply": "2025-03-25T08:40:40.135140Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene annotation preview:\n",
      "Columns in gene annotation: ['ID', 'ENTREZ_GENE_ID', 'SPOT_ID']\n",
      "{'ID': ['1', '2', '3', '9', '10'], 'ENTREZ_GENE_ID': ['1', '2', '3', '9', '10'], 'SPOT_ID': [1.0, 2.0, 3.0, 9.0, 10.0]}\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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": "bd741756",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09706985",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:40:40.136702Z",
     "iopub.status.busy": "2025-03-25T08:40:40.136583Z",
     "iopub.status.idle": "2025-03-25T08:40:48.121950Z",
     "shell.execute_reply": "2025-03-25T08:40:48.121583Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene mapping dataframe preview:\n",
      "{'ID': ['1', '2', '3', '9', '10'], 'Gene': ['1', '2', '3', '9', '10']}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene expression data after mapping:\n",
      "Shape: (0, 204)\n",
      "First 10 gene identifiers: []\n",
      "Gene data saved to ../../output/preprocess/Eczema/gene_data/GSE123088.csv\n"
     ]
    }
   ],
   "source": [
    "# Looking at the annotation data, we can see it includes:\n",
    "# ID: probe identifiers that match gene_data index\n",
    "# ENTREZ_GENE_ID: Entrez Gene IDs which can serve as gene identifiers\n",
    "\n",
    "# 1. Identify the appropriate columns for mapping\n",
    "# From the preview, we can see that ID column in annotation matches the index in gene_data\n",
    "# ENTREZ_GENE_ID appears to be the closest to gene identifiers we have\n",
    "\n",
    "# Since the ENTREZ_GENE_ID is numeric, we'll check if it can be mapped to gene symbols\n",
    "# We'll use the gene_mapping function from the library with necessary columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'ENTREZ_GENE_ID')\n",
    "\n",
    "print(\"\\nGene mapping dataframe preview:\")\n",
    "print(preview_df(mapping_df, n=5))\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(f\"Shape: {gene_data.shape}\")\n",
    "print(f\"First 10 gene identifiers: {list(gene_data.index[:10])}\")\n",
    "\n",
    "# Save the processed gene data to the output file\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 data saved to {out_gene_data_file}\")"
   ]
  }
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