{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a183a19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:25:02.254323Z",
     "iopub.status.busy": "2025-03-25T07:25:02.254099Z",
     "iopub.status.idle": "2025-03-25T07:25:02.423328Z",
     "shell.execute_reply": "2025-03-25T07:25:02.422884Z"
    }
   },
   "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 = \"Lactose_Intolerance\"\n",
    "cohort = \"GSE136395\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Lactose_Intolerance\"\n",
    "in_cohort_dir = \"../../input/GEO/Lactose_Intolerance/GSE136395\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Lactose_Intolerance/GSE136395.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Lactose_Intolerance/gene_data/GSE136395.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Lactose_Intolerance/clinical_data/GSE136395.csv\"\n",
    "json_path = \"../../output/preprocess/Lactose_Intolerance/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b2f1e9",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efdb186f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:25:02.424710Z",
     "iopub.status.busy": "2025-03-25T07:25:02.424562Z",
     "iopub.status.idle": "2025-03-25T07:25:02.602599Z",
     "shell.execute_reply": "2025-03-25T07:25:02.602020Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"The effects of a novel oral nutritional supplement as compared to standard care on body composition, physical function and skeletal muscle mRNA expression in Dutch older adults with (or at risk of) undernutrition\"\n",
      "!Series_summary\t\"In a randomized controlled trial, 82 older adults (>65y) with (or at risk of) undernutrition (n=82) were randomly allocated to 12 weeks of supplementation with a novel supplement (586 kcal, 22 g protein of which 50% whey and 50% casein, 206 mg ursolic acid, 7 g free BCAAs, 11 µg vitamin D) or standard care (600 kcal, 24g protein of which 100% casein, 4 µg vitamin D). Body weight increased significantly in the 12 weeks, both in the intervention group (+1.6 ± 0.2 kg, p<.0001) and in the standard care group (+1.8 ± 0.2 kg, p<.0001). Gait speed during 4m and 400m tests improved over time in the intervention group, whereas the standard care showed no improvements (time*treatment effects 400m: p=0.038 and 4m: p=0.048). Gene sets related to mitochondrial functioning were strongly upregulated in the participants receiving the intervention product. We showed that a novel oral nutritional supplement improves gait speed in older adults via improvements in mitochondrial functioning.\"\n",
      "!Series_overall_design\t\"Microarray analysis was performed on pre- and post-treatment skeletal muscle biopsies (m. vastus lateralis) from undernourished older adults.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['sex (female=1, male=0): 1', 'sex (female=1, male=0): 0'], 1: ['subjectid: 202', 'subjectid: 203', 'subjectid: 205', 'subjectid: 211', 'subjectid: 212', 'subjectid: 214', 'subjectid: 215', 'subjectid: 219', 'subjectid: 231', 'subjectid: 238', 'subjectid: 243', 'subjectid: 245', 'subjectid: 250', 'subjectid: 252', 'subjectid: 253', 'subjectid: 258', 'subjectid: 259', 'subjectid: 261', 'subjectid: 264', 'subjectid: 265', 'subjectid: 266'], 2: ['age (yrs): 70', 'age (yrs): 66', 'age (yrs): 74', 'age (yrs): 69', 'age (yrs): 83', 'age (yrs): 5', 'age (yrs): 77', 'age (yrs): 75', 'age (yrs): 72', 'age (yrs): 71', 'age (yrs): 68', 'age (yrs): 80'], 3: ['moment of sampling (pre/post intervention): pre-intervention (at baseline)', 'moment of sampling (pre/post intervention): post-intervention (after 12 wks)'], 4: ['time of sampling (hr: min): 11:35', 'time of sampling (hr: min): 10:40', 'time of sampling (hr: min): 10:55', 'time of sampling (hr: min): 10:25', 'time of sampling (hr: min): 10:30', 'time of sampling (hr: min): 11:45', 'time of sampling (hr: min): 10:20', 'time of sampling (hr: min): 11:40', 'time of sampling (hr: min): 10:44', 'time of sampling (hr: min): 11:15', 'time of sampling (hr: min): 11:20', 'time of sampling (hr: min): 12:40', 'time of sampling (hr: min): 11:25', 'time of sampling (hr: min): 12:20', 'time of sampling (hr: min): 11:30', 'time of sampling (hr: min): 11:06', 'time of sampling (hr: min): 11:23', 'time of sampling (hr: min): 11:10', 'time of sampling (hr: min): 12:25', 'time of sampling (hr: min): 10:35', 'time of sampling (hr: min): 11:50', 'time of sampling (hr: min): 11:00', 'time of sampling (hr: min): 12:50', 'time of sampling (hr: min): 10:05', 'time of sampling (hr: min): 12:03'], 5: ['experimental condition: novel oral nutritional supplement', 'experimental condition: standard-care nutritional supplement'], 6: ['tissue: skeletal muscle'], 7: ['sample type: non-fasted morning sample']}\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": "6836c87d",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "755eb01d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:25:02.604576Z",
     "iopub.status.busy": "2025-03-25T07:25:02.604427Z",
     "iopub.status.idle": "2025-03-25T07:25:02.618040Z",
     "shell.execute_reply": "2025-03-25T07:25:02.617548Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical Features Preview:\n",
      "{0: [1.0, 70.0, 0.0], 1: [0.0, 66.0, 1.0], 2: [nan, 74.0, nan], 3: [nan, 69.0, nan], 4: [nan, 83.0, nan], 5: [nan, nan, nan], 6: [nan, 77.0, nan], 7: [nan, 75.0, nan], 8: [nan, 72.0, nan], 9: [nan, 71.0, nan], 10: [nan, 68.0, nan], 11: [nan, 80.0, nan], 12: [nan, nan, nan], 13: [nan, nan, nan], 14: [nan, nan, nan], 15: [nan, nan, nan], 16: [nan, nan, nan], 17: [nan, nan, nan], 18: [nan, nan, nan], 19: [nan, nan, nan], 20: [nan, nan, nan], 21: [nan, nan, nan], 22: [nan, nan, nan], 23: [nan, nan, nan], 24: [nan, nan, nan]}\n",
      "Clinical features saved to ../../output/preprocess/Lactose_Intolerance/clinical_data/GSE136395.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this study involves \"Microarray analysis\" of skeletal muscle biopsies\n",
    "# This indicates gene expression data is likely available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait - we'll use the \"experimental condition\" as our trait row\n",
    "# Row 5 shows two values: 'novel oral nutritional supplement' and 'standard-care nutritional supplement'\n",
    "trait_row = 5\n",
    "\n",
    "# For age - present in row 2\n",
    "age_row = 2\n",
    "\n",
    "# For gender - present in row 0\n",
    "gender_row = 0\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert experimental condition to binary: 1 for novel supplement, 0 for standard care\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    value_lower = value.lower()\n",
    "    if 'novel' in value_lower:\n",
    "        return 1\n",
    "    elif 'standard' in value_lower:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the numeric part after the colon\n",
    "    try:\n",
    "        # Handle format like \"age (yrs): 70\"\n",
    "        value_parts = value.split(': ')\n",
    "        if len(value_parts) > 1:\n",
    "            age_str = value_parts[1].strip()\n",
    "            age = float(age_str)\n",
    "            # There appears to be an error in the data where one age is listed as 5\n",
    "            # This is likely a typo since all other ages are 65+\n",
    "            if age < 20:  # Assume any age under 20 is an error for older adults\n",
    "                return None\n",
    "            return age\n",
    "        return None\n",
    "    except (ValueError, IndexError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary: 0 for female, 1 for male\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # The format appears to be \"sex (female=1, male=0): 1\" or \"sex (female=1, male=0): 0\"\n",
    "    # Note: The original data has female=1, male=0, but we need to convert to female=0, male=1\n",
    "    try:\n",
    "        value_parts = value.split(': ')\n",
    "        if len(value_parts) > 1:\n",
    "            gender_value = value_parts[1].strip()\n",
    "            # Since in the data female=1 and male=0, we need to flip these values\n",
    "            if gender_value == '1':  # Female in original data\n",
    "                return 0\n",
    "            elif gender_value == '0':  # Male in original data\n",
    "                return 1\n",
    "        return None\n",
    "    except (ValueError, IndexError):\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Trait data is available if trait_row is not None\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",
    "    # Define clinical_data (assuming it was created in a previous step)\n",
    "    clinical_data = pd.DataFrame.from_dict(\n",
    "        {i: values for i, values in \n",
    "         {0: ['sex (female=1, male=0): 1', 'sex (female=1, male=0): 0'], \n",
    "          1: ['subjectid: 202', 'subjectid: 203', 'subjectid: 205', 'subjectid: 211', 'subjectid: 212', 'subjectid: 214', 'subjectid: 215', 'subjectid: 219', 'subjectid: 231', 'subjectid: 238', 'subjectid: 243', 'subjectid: 245', 'subjectid: 250', 'subjectid: 252', 'subjectid: 253', 'subjectid: 258', 'subjectid: 259', 'subjectid: 261', 'subjectid: 264', 'subjectid: 265', 'subjectid: 266'], \n",
    "          2: ['age (yrs): 70', 'age (yrs): 66', 'age (yrs): 74', 'age (yrs): 69', 'age (yrs): 83', 'age (yrs): 5', 'age (yrs): 77', 'age (yrs): 75', 'age (yrs): 72', 'age (yrs): 71', 'age (yrs): 68', 'age (yrs): 80'], \n",
    "          3: ['moment of sampling (pre/post intervention): pre-intervention (at baseline)', 'moment of sampling (pre/post intervention): post-intervention (after 12 wks)'], \n",
    "          4: ['time of sampling (hr: min): 11:35', 'time of sampling (hr: min): 10:40', 'time of sampling (hr: min): 10:55', 'time of sampling (hr: min): 10:25', 'time of sampling (hr: min): 10:30', 'time of sampling (hr: min): 11:45', 'time of sampling (hr: min): 10:20', 'time of sampling (hr: min): 11:40', 'time of sampling (hr: min): 10:44', 'time of sampling (hr: min): 11:15', 'time of sampling (hr: min): 11:20', 'time of sampling (hr: min): 12:40', 'time of sampling (hr: min): 11:25', 'time of sampling (hr: min): 12:20', 'time of sampling (hr: min): 11:30', 'time of sampling (hr: min): 11:06', 'time of sampling (hr: min): 11:23', 'time of sampling (hr: min): 11:10', 'time of sampling (hr: min): 12:25', 'time of sampling (hr: min): 10:35', 'time of sampling (hr: min): 11:50', 'time of sampling (hr: min): 11:00', 'time of sampling (hr: min): 12:50', 'time of sampling (hr: min): 10:05', 'time of sampling (hr: min): 12:03'], \n",
    "          5: ['experimental condition: novel oral nutritional supplement', 'experimental condition: standard-care nutritional supplement'], \n",
    "          6: ['tissue: skeletal muscle'], \n",
    "          7: ['sample type: non-fasted morning sample']}.items()\n",
    "    }, orient='index')\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 the extracted features\n",
    "    preview = preview_df(clinical_features)\n",
    "    print(\"Clinical Features Preview:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save clinical features to CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df05ca96",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92748447",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:25:02.619714Z",
     "iopub.status.busy": "2025-03-25T07:25:02.619601Z",
     "iopub.status.idle": "2025-03-25T07:25:02.881179Z",
     "shell.execute_reply": "2025-03-25T07:25:02.880530Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examining matrix file structure...\n",
      "Line 0: !Series_title\t\"The effects of a novel oral nutritional supplement as compared to standard care on body composition, physical function and skeletal muscle mRNA expression in Dutch older adults with (or at risk of) undernutrition\"\n",
      "Line 1: !Series_geo_accession\t\"GSE136395\"\n",
      "Line 2: !Series_status\t\"Public on Apr 06 2021\"\n",
      "Line 3: !Series_submission_date\t\"Aug 27 2019\"\n",
      "Line 4: !Series_last_update_date\t\"Apr 09 2021\"\n",
      "Line 5: !Series_pubmed_id\t\"33799307\"\n",
      "Line 6: !Series_summary\t\"In a randomized controlled trial, 82 older adults (>65y) with (or at risk of) undernutrition (n=82) were randomly allocated to 12 weeks of supplementation with a novel supplement (586 kcal, 22 g protein of which 50% whey and 50% casein, 206 mg ursolic acid, 7 g free BCAAs, 11 µg vitamin D) or standard care (600 kcal, 24g protein of which 100% casein, 4 µg vitamin D). Body weight increased significantly in the 12 weeks, both in the intervention group (+1.6 ± 0.2 kg, p<.0001) and in the standard care group (+1.8 ± 0.2 kg, p<.0001). Gait speed during 4m and 400m tests improved over time in the intervention group, whereas the standard care showed no improvements (time*treatment effects 400m: p=0.038 and 4m: p=0.048). Gene sets related to mitochondrial functioning were strongly upregulated in the participants receiving the intervention product. We showed that a novel oral nutritional supplement improves gait speed in older adults via improvements in mitochondrial functioning.\"\n",
      "Line 7: !Series_overall_design\t\"Microarray analysis was performed on pre- and post-treatment skeletal muscle biopsies (m. vastus lateralis) from undernourished older adults.\"\n",
      "Line 8: !Series_type\t\"Expression profiling by array\"\n",
      "Line 9: !Series_contributor\t\"Pol,,Grootswagers\"\n",
      "Found table marker at line 68\n",
      "First few lines after marker:\n",
      "\"ID_REF\"\t\"GSM4047976\"\t\"GSM4047977\"\t\"GSM4047978\"\t\"GSM4047979\"\t\"GSM4047980\"\t\"GSM4047981\"\t\"GSM4047982\"\t\"GSM4047983\"\t\"GSM4047984\"\t\"GSM4047985\"\t\"GSM4047986\"\t\"GSM4047987\"\t\"GSM4047988\"\t\"GSM4047989\"\t\"GSM4047990\"\t\"GSM4047991\"\t\"GSM4047992\"\t\"GSM4047993\"\t\"GSM4047994\"\t\"GSM4047995\"\t\"GSM4047996\"\t\"GSM4047997\"\t\"GSM4047998\"\t\"GSM4047999\"\t\"GSM4048000\"\t\"GSM4048001\"\t\"GSM4048002\"\t\"GSM4048003\"\t\"GSM4048004\"\t\"GSM4048005\"\t\"GSM4048006\"\t\"GSM4048007\"\t\"GSM4048008\"\t\"GSM4048009\"\t\"GSM4048010\"\t\"GSM4048011\"\t\"GSM4048012\"\t\"GSM4048013\"\t\"GSM4048014\"\t\"GSM4048015\"\t\"GSM4048016\"\t\"GSM4048017\"\n",
      "16650001\t0.921870808\t0.530152954\t0.756759146\t0.769807736\t1.549294871\t1.854587189\t0.482116543\t0.8096838\t0.814715919\t0.424478151\t0.545187116\t0.405601513\t0.338633937\t0.932038514\t0.361127267\t0.706791783\t0.690730035\t0.492038615\t0.279776649\t0.708476738\t0.921510422\t0.857943113\t2.20404239\t1.610630362\t2.38311589\t0.401240327\t1.493156353\t0.455290228\t0.245919566\t2.863136988\t0.689221987\t0.921552734\t2.556896369\t1.426723247\t0.486754942\t0.345676909\t0.75955785\t1.184668222\t0.69494521\t1.41305724\t0.783314159\t0.742542268\n",
      "16650003\t0.579271708\t0.259648234\t0.862950609\t0.895520471\t0.756204821\t0.672755213\t1.101893268\t0.56911549\t0.665188807\t0.619120599\t1.62086805\t1.459663992\t0.201842233\t0.808168887\t1.074059794\t0.582766165\t1.134588929\t0.873584833\t1.087444902\t0.80137227\t0.709094776\t0.911162052\t1.406933772\t1.69050053\t0.449161543\t0.712137285\t0.291182845\t0.787467947\t0.979618206\t0.823769005\t0.32401978\t0.569892914\t1.116726783\t1.159745387\t1.035858585\t0.685783655\t1.193817216\t0.654209904\t0.41715519\t0.603980944\t0.505372597\t0.954429049\n",
      "16650005\t0.671714204\t1.142914973\t2.814022146\t1.641632162\t1.502576615\t1.030709501\t0.506760379\t0.921806801\t2.339047239\t0.539733584\t1.468627896\t1.736970671\t1.253040325\t1.431135704\t2.263758435\t1.204947957\t0.787394062\t1.981787297\t3.07513362\t0.908376151\t1.207305452\t0.551538147\t0.622966489\t1.148700777\t2.344214972\t0.977961197\t1.11317389\t1.094055403\t1.00736021\t1.109094543\t1.13644913\t1.653104258\t1.49590451\t1.818820715\t0.980248101\t0.605065468\t1.383320329\t0.623771101\t1.680298052\t1.465487155\t1.0859718\t1.606663443\n",
      "16650007\t0.790475998\t1.151186563\t1.461116264\t0.605458323\t0.844674561\t0.47990631\t0.826873896\t0.989969104\t0.563209452\t0.798079605\t0.476845287\t1.143701546\t1.129649557\t0.443205766\t0.79567626\t0.74155806\t0.365106024\t1.72162047\t0.844435089\t0.679170899\t0.577378544\t0.85610594\t0.453788863\t1.009421575\t1.086660912\t0.627059169\t1.740979467\t0.97993658\t0.232732015\t2.121708035\t1.135186397\t0.587805222\t1.062980704\t1.237155655\t0.312292874\t1.108727651\t1.61861305\t0.496639014\t1.521733987\t0.589513214\t1.095906204\t3.16173481\n",
      "Total lines examined: 69\n",
      "\n",
      "Attempting to extract gene data from matrix file...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully extracted gene data with 53617 rows\n",
      "First 20 gene IDs:\n",
      "Index(['16650001', '16650003', '16650005', '16650007', '16650009', '16650011',\n",
      "       '16650013', '16650015', '16650017', '16650019', '16650021', '16650023',\n",
      "       '16650025', '16650027', '16650029', '16650031', '16650033', '16650035',\n",
      "       '16650037', '16650041'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene expression data available: True\n"
     ]
    }
   ],
   "source": [
    "# 1. Get the file paths for the SOFT file and matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# Add diagnostic code to check file content and structure\n",
    "print(\"Examining matrix file structure...\")\n",
    "with gzip.open(matrix_file, 'rt') as file:\n",
    "    table_marker_found = False\n",
    "    lines_read = 0\n",
    "    for i, line in enumerate(file):\n",
    "        lines_read += 1\n",
    "        if '!series_matrix_table_begin' in line:\n",
    "            table_marker_found = True\n",
    "            print(f\"Found table marker at line {i}\")\n",
    "            # Read a few lines after the marker to check data structure\n",
    "            next_lines = [next(file, \"\").strip() for _ in range(5)]\n",
    "            print(\"First few lines after marker:\")\n",
    "            for next_line in next_lines:\n",
    "                print(next_line)\n",
    "            break\n",
    "        if i < 10:  # Print first few lines to see file structure\n",
    "            print(f\"Line {i}: {line.strip()}\")\n",
    "        if i > 100:  # Don't read the entire file\n",
    "            break\n",
    "    \n",
    "    if not table_marker_found:\n",
    "        print(\"Table marker '!series_matrix_table_begin' not found in first 100 lines\")\n",
    "    print(f\"Total lines examined: {lines_read}\")\n",
    "\n",
    "# 2. Try extracting gene expression data from the matrix file again with better diagnostics\n",
    "try:\n",
    "    print(\"\\nAttempting to extract gene data from matrix file...\")\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    if gene_data.empty:\n",
    "        print(\"Extracted gene expression data is empty\")\n",
    "        is_gene_available = False\n",
    "    else:\n",
    "        print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n",
    "        print(\"First 20 gene IDs:\")\n",
    "        print(gene_data.index[:20])\n",
    "        is_gene_available = True\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {str(e)}\")\n",
    "    print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n",
    "    is_gene_available = False\n",
    "\n",
    "print(f\"\\nGene expression data available: {is_gene_available}\")\n",
    "\n",
    "# If data extraction failed, try an alternative approach using pandas directly\n",
    "if not is_gene_available:\n",
    "    print(\"\\nTrying alternative approach to read gene expression data...\")\n",
    "    try:\n",
    "        with gzip.open(matrix_file, 'rt') as file:\n",
    "            # Skip lines until we find the marker\n",
    "            for line in file:\n",
    "                if '!series_matrix_table_begin' in line:\n",
    "                    break\n",
    "            \n",
    "            # Try to read the data directly with pandas\n",
    "            gene_data = pd.read_csv(file, sep='\\t', index_col=0)\n",
    "            \n",
    "            if not gene_data.empty:\n",
    "                print(f\"Successfully extracted gene data with alternative method: {gene_data.shape}\")\n",
    "                print(\"First 20 gene IDs:\")\n",
    "                print(gene_data.index[:20])\n",
    "                is_gene_available = True\n",
    "            else:\n",
    "                print(\"Alternative extraction method also produced empty data\")\n",
    "    except Exception as e:\n",
    "        print(f\"Alternative extraction failed: {str(e)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee9a645f",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ff606cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:25:02.882984Z",
     "iopub.status.busy": "2025-03-25T07:25:02.882835Z",
     "iopub.status.idle": "2025-03-25T07:25:02.885332Z",
     "shell.execute_reply": "2025-03-25T07:25:02.884907Z"
    }
   },
   "outputs": [],
   "source": [
    "# Based on examining the gene identifiers such as '16650001', '16650003', etc., these appear to be \n",
    "# probe identifiers from an Illumina microarray platform rather than human gene symbols.\n",
    "#\n",
    "# These numeric identifiers (starting with 1665...) are typical of Illumina BeadArray probes\n",
    "# and will need to be mapped to standard human gene symbols for interpretability and\n",
    "# cross-study compatibility.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a28551f",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da67be7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:25:02.887127Z",
     "iopub.status.busy": "2025-03-25T07:25:02.886982Z",
     "iopub.status.idle": "2025-03-25T07:25:11.538798Z",
     "shell.execute_reply": "2025-03-25T07:25:11.538121Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting gene annotation data from SOFT file...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully extracted gene annotation data with 2305573 rows\n",
      "\n",
      "Gene annotation preview (first few rows):\n",
      "{'ID': ['16657436', '16657440', '16657445', '16657447', '16657450'], 'probeset_id': ['16657436', '16657440', '16657445', '16657447', '16657450'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+', '+', '+'], 'start': ['12190', '29554', '69091', '160446', '317811'], 'stop': ['13639', '31109', '70008', '161525', '328581'], 'total_probes': [25.0, 28.0, 8.0, 13.0, 36.0], 'gene_assignment': ['NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// NR_051985 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// NR_045117 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // 2q13 // 84771 /// NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // 2q13 // 84771 /// NR_051986 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // 9p24.3 // 100287596 /// ENST00000456328 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000559159 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000562189 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000513886 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000515242 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000518655 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000515173 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000545636 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000450305 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000560040 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000430178 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000538648 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000535848 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000457993 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000437401 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000426146 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // --- // --- /// ENST00000445777 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000421620 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // --- // ---', 'ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000473358 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000473358 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000473358 // MIR1302-2 // microRNA 1302-2 // --- // 100302278', 'NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501', '---', 'AK302511 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK294489 // LOC729737 // uncharacterized LOC729737 // 1p36.33 // 729737 /// AK303380 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK316554 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK316556 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK302573 // LOC729737 // uncharacterized LOC729737 // 1p36.33 // 729737 /// AK123446 // LOC441124 // uncharacterized LOC441124 // 1q42.11 // 441124 /// ENST00000425496 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000425496 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000425496 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000425496 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000456623 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000456623 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000456623 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000456623 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000418377 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000418377 // LOC100288102 // uncharacterized LOC100288102 // 1q42.11 // 100288102 /// ENST00000418377 // LOC731275 // uncharacterized LOC731275 // 1q43 // 731275 /// ENST00000534867 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000534867 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000534867 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000534867 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000544678 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000544678 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000544678 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000544678 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000544678 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000544678 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326 /// ENST00000419160 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000419160 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000419160 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000419160 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000432964 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000432964 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000432964 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000432964 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000423728 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000423728 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000423728 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000423728 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000457364 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000457364 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000457364 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000457364 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000457364 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000457364 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326 /// ENST00000438516 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000438516 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000438516 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000438516 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000438516 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000438516 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326'], 'mrna_assignment': ['NR_046018 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 (DDX11L1), non-coding RNA. // chr1 // 100 // 100 // 25 // 25 // 0 /// NR_034090 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 (DDX11L9), transcript variant 1, non-coding RNA. // chr1 // 96 // 100 // 24 // 25 // 0 /// NR_051985 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 (DDX11L9), transcript variant 2, non-coding RNA. // chr1 // 96 // 100 // 24 // 25 // 0 /// NR_045117 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 (DDX11L10), non-coding RNA. // chr1 // 92 // 96 // 22 // 24 // 0 /// NR_024004 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 (DDX11L2), transcript variant 1, non-coding RNA. // chr1 // 83 // 96 // 20 // 24 // 0 /// NR_024005 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 (DDX11L2), transcript variant 2, non-coding RNA. // chr1 // 83 // 96 // 20 // 24 // 0 /// NR_051986 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 (DDX11L5), non-coding RNA. // chr1 // 50 // 96 // 12 // 24 // 0 /// TCONS_l2_00010384-XLOC_l2_005087 // Broad TUCP // linc-SNRNP25-2 chr16:+:61554-64041 // chr1 // 92 // 96 // 22 // 24 // 0 /// TCONS_l2_00010385-XLOC_l2_005087 // Broad TUCP // linc-SNRNP25-2 chr16:+:61554-64090 // chr1 // 92 // 96 // 22 // 24 // 0 /// TCONS_l2_00030644-XLOC_l2_015857 // Broad TUCP // linc-TMLHE chrX:-:155255810-155257756 // chr1 // 50 // 96 // 12 // 24 // 0 /// TCONS_l2_00028588-XLOC_l2_014685 // Broad TUCP // linc-DOCK8-2 chr9:+:11235-13811 // chr1 // 50 // 64 // 8 // 16 // 0 /// TCONS_l2_00030643-XLOC_l2_015857 // Broad TUCP // linc-TMLHE chrX:-:155255810-155257756 // chr1 // 50 // 64 // 8 // 16 // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000559159 // ENSEMBL // cdna:known chromosome:GRCh37:15:102516761:102519296:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000562189 // ENSEMBL // cdna:known chromosome:GRCh37:15:102516761:102519296:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000513886 // ENSEMBL // cdna:known chromosome:GRCh37:16:61555:64090:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 92 // 96 // 22 // 24 // 0 /// AK125998 // GenBank // Homo sapiens cDNA FLJ44010 fis, clone TESTI4024344. // chr1 // 50 // 96 // 12 // 24 // 0 /// BC070227 // GenBank // Homo sapiens similar to DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 isoform 1, mRNA (cDNA clone IMAGE:6103207). // chr1 // 100 // 44 // 11 // 11 // 0 /// ENST00000515242 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:11872:14412:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000518655 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:11874:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000515173 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102516758:102519298:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000545636 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:16:61553:64093:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 92 // 96 // 22 // 24 // 0 /// ENST00000450305 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:12010:13670:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 68 // 17 // 17 // 0 /// ENST00000560040 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102517497:102518994:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 94 // 68 // 16 // 17 // 0 /// ENST00000430178 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:16:61861:63351:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 88 // 64 // 14 // 16 // 0 /// ENST00000538648 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102517351:102517622:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:pseudogene // chr1 // 100 // 16 // 4 // 4 // 0 /// ENST00000535848 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356606:114359144:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 96 // 20 // 24 // 0 /// ENST00000457993 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356613:114358838:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 85 // 80 // 17 // 20 // 0 /// ENST00000437401 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356613:114358838:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 80 // 80 // 16 // 20 // 0 /// ENST00000426146 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:9:11987:14522:1 gene:ENSG00000236875 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 96 // 12 // 24 // 0 /// ENST00000445777 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:X:155255323:155257848:-1 gene:ENSG00000227159 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 96 // 12 // 24 // 0 /// ENST00000507418 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:X:155255329:155257542:-1 gene:ENSG00000227159 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 64 // 8 // 16 // 0 /// ENST00000421620 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:9:12134:13439:1 gene:ENSG00000236875 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 12 // 3 // 3 // 0 /// GENSCAN00000003613 // ENSEMBL // cdna:genscan chromosome:GRCh37:15:102517021:102518980:-1 transcript_biotype:protein_coding // chr1 // 100 // 52 // 13 // 13 // 0 /// GENSCAN00000026650 // ENSEMBL // cdna:genscan chromosome:GRCh37:1:12190:14149:1 transcript_biotype:protein_coding // chr1 // 100 // 52 // 13 // 13 // 0 /// GENSCAN00000029586 // ENSEMBL // cdna:genscan chromosome:GRCh37:16:61871:63830:1 transcript_biotype:protein_coding // chr1 // 100 // 48 // 12 // 12 // 0 /// ENST00000535849 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:12:92239:93430:-1 gene:ENSG00000256263 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// ENST00000575871 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:HG858_PATCH:62310:63501:1 gene:ENSG00000262195 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// ENST00000572276 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:HSCHR12_1_CTG1:62310:63501:1 gene:ENSG00000263289 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// GENSCAN00000048516 // ENSEMBL // cdna:genscan chromosome:GRCh37:HG858_PATCH:62740:64276:1 transcript_biotype:protein_coding // chr1 // 25 // 48 // 3 // 12 // 1 /// GENSCAN00000048612 // ENSEMBL // cdna:genscan chromosome:GRCh37:HSCHR12_1_CTG1:62740:64276:1 transcript_biotype:protein_coding // chr1 // 25 // 48 // 3 // 12 // 1', 'ENST00000473358 // ENSEMBL // cdna:known chromosome:GRCh37:1:29554:31097:1 gene:ENSG00000243485 gene_biotype:antisense transcript_biotype:antisense // chr1 // 100 // 71 // 20 // 20 // 0', 'NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 8 // 8 // 0 /// ENST00000335137 // ENSEMBL // cdna:known chromosome:GRCh37:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 8 // 8 // 0', 'TCONS_00000119-XLOC_000001 // Rinn lincRNA // linc-OR4F16-10 chr1:+:160445-161525 // chr1 // 100 // 100 // 13 // 13 // 0', 'AK302511 // GenBank // Homo sapiens cDNA FLJ61476 complete cds. // chr1 // 92 // 33 // 11 // 12 // 0 /// AK294489 // GenBank // Homo sapiens cDNA FLJ52615 complete cds. // chr1 // 77 // 36 // 10 // 13 // 0 /// AK303380 // GenBank // Homo sapiens cDNA FLJ53527 complete cds. // chr1 // 100 // 14 // 5 // 5 // 0 /// AK316554 // GenBank // Homo sapiens cDNA, FLJ79453 complete cds. // chr1 // 100 // 11 // 4 // 4 // 0 /// AK316556 // GenBank // Homo sapiens cDNA, FLJ79455 complete cds. // chr1 // 100 // 11 // 4 // 4 // 0 /// AK302573 // GenBank // Homo sapiens cDNA FLJ52612 complete cds. // chr1 // 80 // 14 // 4 // 5 // 0 /// TCONS_l2_00002815-XLOC_l2_001399 // Broad TUCP // linc-PLD5-5 chr1:-:243219130-243221165 // chr1 // 92 // 33 // 11 // 12 // 0 /// TCONS_l2_00001802-XLOC_l2_001332 // Broad TUCP // linc-TP53BP2-3 chr1:-:224139117-224140327 // chr1 // 100 // 14 // 5 // 5 // 0 /// TCONS_l2_00001804-XLOC_l2_001332 // Broad TUCP // linc-TP53BP2-3 chr1:-:224139117-224142371 // chr1 // 100 // 14 // 5 // 5 // 0 /// TCONS_00000120-XLOC_000002 // Rinn lincRNA // linc-OR4F16-9 chr1:+:320161-321056 // chr1 // 100 // 11 // 4 // 4 // 0 /// TCONS_l2_00002817-XLOC_l2_001399 // Broad TUCP // linc-PLD5-5 chr1:-:243220177-243221150 // chr1 // 100 // 6 // 2 // 2 // 0 /// TCONS_00000437-XLOC_000658 // Rinn lincRNA // linc-ZNF692-6 chr1:-:139789-140339 // chr1 // 100 // 6 // 2 // 2 // 0 /// AK299469 // GenBank // Homo sapiens cDNA FLJ52610 complete cds. // chr1 // 100 // 33 // 12 // 12 // 0 /// AK302889 // GenBank // Homo sapiens cDNA FLJ54896 complete cds. // chr1 // 100 // 22 // 8 // 8 // 0 /// AK123446 // GenBank // Homo sapiens cDNA FLJ41452 fis, clone BRSTN2010363. // chr1 // 100 // 19 // 7 // 7 // 0 /// ENST00000425496 // ENSEMBL // cdna:known chromosome:GRCh37:1:324756:328453:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 33 // 13 // 12 // 0 /// ENST00000456623 // ENSEMBL // cdna:known chromosome:GRCh37:1:324515:326852:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 33 // 12 // 12 // 0 /// ENST00000418377 // ENSEMBL // cdna:known chromosome:GRCh37:1:243219131:243221165:-1 gene:ENSG00000214837 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 92 // 33 // 11 // 12 // 0 /// ENST00000534867 // ENSEMBL // cdna:known chromosome:GRCh37:1:324438:325896:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 28 // 10 // 10 // 0 /// ENST00000544678 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751053:180752511:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 22 // 8 // 8 // 0 /// ENST00000419160 // ENSEMBL // cdna:known chromosome:GRCh37:1:322732:324955:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 17 // 6 // 6 // 0 /// ENST00000432964 // ENSEMBL // cdna:known chromosome:GRCh37:1:320162:321056:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 11 // 4 // 4 // 0 /// ENST00000423728 // ENSEMBL // cdna:known chromosome:GRCh37:1:320162:324461:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 11 // 4 // 4 // 0 /// BC092421 // GenBank // Homo sapiens cDNA clone IMAGE:30378758. // chr1 // 100 // 33 // 12 // 12 // 0 /// ENST00000426316 // ENSEMBL // cdna:known chromosome:GRCh37:1:317811:328455:1 gene:ENSG00000240876 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000465971 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:7:128291239:128292388:1 gene:ENSG00000243302 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 31 // 11 // 11 // 0 /// ENST00000535314 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:7:128291243:128292355:1 gene:ENSG00000243302 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 31 // 11 // 11 // 0 /// ENST00000423372 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:134901:139379:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000435839 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:137283:139620:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000537461 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:138239:139697:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 19 // 7 // 7 // 0 /// ENST00000494149 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:135247:138039:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000514436 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:326096:328112:1 gene:ENSG00000250575 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000457364 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751371:180755068:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 28 // 11 // 10 // 0 /// ENST00000438516 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751130:180753467:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 28 // 10 // 10 // 0 /// ENST00000526704 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:129531:139099:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 93 // 42 // 14 // 15 // 0 /// ENST00000540375 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:127115:131056:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 28 // 11 // 10 // 0 /// ENST00000457006 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:128960:131297:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000427071 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:130207:131297:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 25 // 9 // 9 // 0 /// ENST00000542435 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:129916:131374:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 22 // 8 // 8 // 0'], 'swissprot': ['NR_046018 // B7ZGW9 /// NR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 /// NR_046018 // B7ZGX3 /// NR_046018 // B7ZGX5 /// NR_046018 // B7ZGX6 /// NR_046018 // B7ZGX7 /// NR_046018 // B7ZGX8 /// NR_046018 // B7ZGX9 /// NR_046018 // B7ZGY0 /// NR_034090 // B7ZGW9 /// NR_034090 // B7ZGX0 /// NR_034090 // B7ZGX2 /// NR_034090 // B7ZGX3 /// NR_034090 // B7ZGX5 /// NR_034090 // B7ZGX6 /// NR_034090 // B7ZGX7 /// NR_034090 // B7ZGX8 /// NR_034090 // B7ZGX9 /// NR_034090 // B7ZGY0 /// NR_051985 // B7ZGW9 /// NR_051985 // B7ZGX0 /// NR_051985 // B7ZGX2 /// NR_051985 // B7ZGX3 /// NR_051985 // B7ZGX5 /// NR_051985 // B7ZGX6 /// NR_051985 // B7ZGX7 /// NR_051985 // B7ZGX8 /// NR_051985 // B7ZGX9 /// NR_051985 // B7ZGY0 /// NR_045117 // B7ZGW9 /// NR_045117 // B7ZGX0 /// NR_045117 // B7ZGX2 /// NR_045117 // B7ZGX3 /// NR_045117 // B7ZGX5 /// NR_045117 // B7ZGX6 /// NR_045117 // B7ZGX7 /// NR_045117 // B7ZGX8 /// NR_045117 // B7ZGX9 /// NR_045117 // B7ZGY0 /// NR_024005 // B7ZGW9 /// NR_024005 // B7ZGX0 /// NR_024005 // B7ZGX2 /// NR_024005 // B7ZGX3 /// NR_024005 // B7ZGX5 /// NR_024005 // B7ZGX6 /// NR_024005 // B7ZGX7 /// NR_024005 // B7ZGX8 /// NR_024005 // B7ZGX9 /// NR_024005 // B7ZGY0 /// NR_051986 // B7ZGW9 /// NR_051986 // B7ZGX0 /// NR_051986 // B7ZGX2 /// NR_051986 // B7ZGX3 /// NR_051986 // B7ZGX5 /// NR_051986 // B7ZGX6 /// NR_051986 // B7ZGX7 /// NR_051986 // B7ZGX8 /// NR_051986 // B7ZGX9 /// NR_051986 // B7ZGY0 /// AK125998 // Q6ZU42 /// AK125998 // B7ZGW9 /// AK125998 // B7ZGX0 /// AK125998 // B7ZGX2 /// AK125998 // B7ZGX3 /// AK125998 // B7ZGX5 /// AK125998 // B7ZGX6 /// AK125998 // B7ZGX7 /// AK125998 // B7ZGX8 /// AK125998 // B7ZGX9 /// AK125998 // B7ZGY0', '---', '---', '---', 'AK302511 // B4DYM5 /// AK294489 // B4DGA0 /// AK294489 // Q6ZSN7 /// AK303380 // B4E0H4 /// AK303380 // Q6ZQS4 /// AK303380 // A8E4K2 /// AK316554 // B4E3X0 /// AK316554 // Q6ZSN7 /// AK316556 // B4E3X2 /// AK316556 // Q6ZSN7 /// AK302573 // B7Z7W4 /// AK302573 // Q6ZQS4 /// AK302573 // A8E4K2 /// AK299469 // B7Z5V7 /// AK299469 // Q6ZSN7 /// AK302889 // B7Z846 /// AK302889 // Q6ZSN7 /// AK123446 // B3KVU4'], 'unigene': ['NR_046018 // Hs.714157 // testis| normal| adult /// NR_034090 // Hs.644359 // blood| normal| adult /// NR_051985 // Hs.644359 // blood| normal| adult /// NR_045117 // Hs.592089 // brain| glioma /// NR_024004 // Hs.712940 // bladder| bone marrow| brain| embryonic tissue| intestine| mammary gland| muscle| pharynx| placenta| prostate| skin| spleen| stomach| testis| thymus| breast (mammary gland) tumor| gastrointestinal tumor| glioma| non-neoplasia| normal| prostate cancer| skin tumor| soft tissue/muscle tissue tumor|embryoid body| adult /// NR_024005 // Hs.712940 // bladder| bone marrow| brain| embryonic tissue| intestine| mammary gland| muscle| pharynx| placenta| prostate| skin| spleen| stomach| testis| thymus| breast (mammary gland) tumor| gastrointestinal tumor| glioma| non-neoplasia| normal| prostate cancer| skin tumor| soft tissue/muscle tissue tumor|embryoid body| adult /// NR_051986 // Hs.719844 // brain| normal /// ENST00000456328 // Hs.714157 // testis| normal| adult /// ENST00000559159 // Hs.644359 // blood| normal| adult /// ENST00000562189 // Hs.644359 // blood| normal| adult /// ENST00000513886 // Hs.592089 // brain| glioma /// ENST00000515242 // Hs.714157 // testis| normal| adult /// ENST00000518655 // Hs.714157 // testis| normal| adult /// ENST00000515173 // Hs.644359 // blood| normal| adult /// ENST00000545636 // Hs.592089 // brain| glioma /// ENST00000450305 // Hs.714157 // testis| normal| adult /// ENST00000560040 // Hs.644359 // blood| normal| adult /// ENST00000430178 // Hs.592089 // brain| glioma /// ENST00000538648 // Hs.644359 // blood| normal| adult', '---', 'NM_001005484 // Hs.554500 // --- /// ENST00000335137 // Hs.554500 // ---', '---', 'AK302511 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK294489 // Hs.534942 // blood| brain| embryonic tissue| intestine| lung| mammary gland| mouth| ovary| pancreas| pharynx| placenta| spleen| stomach| testis| thymus| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor|embryoid body| blastocyst| fetus| adult /// AK294489 // Hs.734488 // blood| brain| esophagus| intestine| kidney| lung| mammary gland| mouth| placenta| prostate| testis| thymus| thyroid| uterus| breast (mammary gland) tumor| colorectal tumor| esophageal tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| adult /// AK303380 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK316554 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK316556 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK302573 // Hs.534942 // blood| brain| embryonic tissue| intestine| lung| mammary gland| mouth| ovary| pancreas| pharynx| placenta| spleen| stomach| testis| thymus| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor|embryoid body| blastocyst| fetus| adult /// AK302573 // Hs.734488 // blood| brain| esophagus| intestine| kidney| lung| mammary gland| mouth| placenta| prostate| testis| thymus| thyroid| uterus| breast (mammary gland) tumor| colorectal tumor| esophageal tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| adult /// AK123446 // Hs.520589 // bladder| blood| bone| brain| embryonic tissue| intestine| kidney| liver| lung| lymph node| ovary| pancreas| parathyroid| placenta| testis| thyroid| uterus| colorectal tumor| glioma| head and neck tumor| kidney tumor| leukemia| liver tumor| normal| ovarian tumor| uterine tumor|embryoid body| fetus| adult /// ENST00000425496 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000425496 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000456623 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000456623 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000534867 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000534867 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000419160 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000419160 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000432964 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000432964 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000423728 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000423728 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult'], 'GO_biological_process': ['---', '---', '---', '---', '---'], 'GO_cellular_component': ['---', '---', 'NM_001005484 // GO:0005886 // plasma membrane // traceable author statement  /// NM_001005484 // GO:0016021 // integral to membrane // inferred from electronic annotation  /// ENST00000335137 // GO:0005886 // plasma membrane // traceable author statement  /// ENST00000335137 // GO:0016021 // integral to membrane // inferred from electronic annotation', '---', '---'], 'GO_molecular_function': ['---', '---', 'NM_001005484 // GO:0004930 // G-protein coupled receptor activity // inferred from electronic annotation  /// NM_001005484 // GO:0004984 // olfactory receptor activity // inferred from electronic annotation  /// ENST00000335137 // GO:0004930 // G-protein coupled receptor activity // inferred from electronic annotation  /// ENST00000335137 // GO:0004984 // olfactory receptor activity // inferred from electronic annotation', '---', '---'], 'pathway': ['---', '---', '---', '---', '---'], 'protein_domains': ['---', '---', 'ENST00000335137 // Pfam // IPR000276 // GPCR, rhodopsin-like, 7TM /// ENST00000335137 // Pfam // IPR019424 // 7TM GPCR, olfactory receptor/chemoreceptor Srsx', '---', '---'], 'crosshyb_type': ['3', '3', '3', '3', '3'], 'category': ['main', 'main', 'main', 'main', 'main'], 'GB_ACC': ['NR_046018', nan, 'NM_001005484', nan, 'AK302511'], 'SPOT_ID': [nan, 'ENST00000473358', nan, 'TCONS_00000119-XLOC_000001', nan]}\n",
      "\n",
      "Column names in gene annotation data:\n",
      "['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop', 'total_probes', 'gene_assignment', 'mrna_assignment', 'swissprot', 'unigene', 'GO_biological_process', 'GO_cellular_component', 'GO_molecular_function', 'pathway', 'protein_domains', 'crosshyb_type', 'category', 'GB_ACC', 'SPOT_ID']\n",
      "\n",
      "The dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\n",
      "Number of rows with GenBank accessions: 20942 out of 2305573\n",
      "\n",
      "The dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\n",
      "Example SPOT_ID format: nan\n"
     ]
    }
   ],
   "source": [
    "# 1. Extract gene annotation data from the SOFT file\n",
    "print(\"Extracting gene annotation data from SOFT file...\")\n",
    "try:\n",
    "    # Use the library function to extract gene annotation\n",
    "    gene_annotation = get_gene_annotation(soft_file)\n",
    "    print(f\"Successfully extracted gene annotation data with {len(gene_annotation.index)} rows\")\n",
    "    \n",
    "    # Preview the annotation DataFrame\n",
    "    print(\"\\nGene annotation preview (first few rows):\")\n",
    "    print(preview_df(gene_annotation))\n",
    "    \n",
    "    # Show column names to help identify which columns we need for mapping\n",
    "    print(\"\\nColumn names in gene annotation data:\")\n",
    "    print(gene_annotation.columns.tolist())\n",
    "    \n",
    "    # Check for relevant mapping columns\n",
    "    if 'GB_ACC' in gene_annotation.columns:\n",
    "        print(\"\\nThe dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\")\n",
    "        # Count non-null values in GB_ACC column\n",
    "        non_null_count = gene_annotation['GB_ACC'].count()\n",
    "        print(f\"Number of rows with GenBank accessions: {non_null_count} out of {len(gene_annotation)}\")\n",
    "    \n",
    "    if 'SPOT_ID' in gene_annotation.columns:\n",
    "        print(\"\\nThe dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\")\n",
    "        print(\"Example SPOT_ID format:\", gene_annotation['SPOT_ID'].iloc[0])\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error processing gene annotation data: {e}\")\n",
    "    is_gene_available = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834b2630",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6feaa00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:25:11.540768Z",
     "iopub.status.busy": "2025-03-25T07:25:11.540634Z",
     "iopub.status.idle": "2025-03-25T07:25:14.816605Z",
     "shell.execute_reply": "2025-03-25T07:25:14.816220Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating gene mapping from probes to gene symbols...\n",
      "Generated mapping with 53617 rows\n",
      "\n",
      "Preview of gene mapping:\n",
      "{'ID': ['16657436', '16657440', '16657445', '16657447', '16657450'], 'Gene': ['NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// NR_051985 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// NR_045117 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // 2q13 // 84771 /// NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // 2q13 // 84771 /// NR_051986 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // 9p24.3 // 100287596 /// ENST00000456328 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000559159 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000562189 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000513886 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000515242 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000518655 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000515173 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000545636 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000450305 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000560040 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000430178 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000538648 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000535848 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000457993 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000437401 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000426146 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // --- // --- /// ENST00000445777 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000421620 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // --- // ---', 'ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000473358 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000473358 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000473358 // MIR1302-2 // microRNA 1302-2 // --- // 100302278', 'NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501', '---', 'AK302511 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK294489 // LOC729737 // uncharacterized LOC729737 // 1p36.33 // 729737 /// AK303380 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK316554 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK316556 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK302573 // LOC729737 // uncharacterized LOC729737 // 1p36.33 // 729737 /// AK123446 // LOC441124 // uncharacterized LOC441124 // 1q42.11 // 441124 /// ENST00000425496 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000425496 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000425496 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000425496 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000456623 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000456623 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000456623 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000456623 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000418377 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000418377 // LOC100288102 // uncharacterized LOC100288102 // 1q42.11 // 100288102 /// ENST00000418377 // LOC731275 // uncharacterized LOC731275 // 1q43 // 731275 /// ENST00000534867 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000534867 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000534867 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000534867 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000544678 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000544678 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000544678 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000544678 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000544678 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000544678 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326 /// ENST00000419160 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000419160 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000419160 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000419160 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000432964 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000432964 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000432964 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000432964 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000423728 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000423728 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000423728 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000423728 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000457364 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000457364 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000457364 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000457364 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000457364 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000457364 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326 /// ENST00000438516 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000438516 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000438516 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000438516 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000438516 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000438516 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326']}\n",
      "\n",
      "Gene data shape before mapping: (53617, 42)\n",
      "\n",
      "Applying gene mapping to convert probe IDs to gene symbols...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully mapped gene data with 81076 genes\n",
      "\n",
      "First 20 gene symbols in mapped gene expression data:\n",
      "Index(['A-', 'A-2', 'A-52', 'A-E', 'A-I', 'A-II', 'A-IV', 'A-V', 'A0', 'A1',\n",
      "       'A1-', 'A10', 'A11', 'A12', 'A13', 'A14', 'A15', 'A16', 'A17', 'A18'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene expression data saved to ../../output/preprocess/Lactose_Intolerance/gene_data/GSE136395.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the relevant columns for gene mapping\n",
    "# From examining the gene_annotation and gene_data, we need to map:\n",
    "#   - The 'ID' column in gene_annotation (which contains probes like '16650001')\n",
    "#   - To gene symbols found in the 'gene_assignment' column\n",
    "\n",
    "# Extract IDs and gene_assignment for mapping\n",
    "print(\"Creating gene mapping from probes to gene symbols...\")\n",
    "prob_col = 'ID'  # Column containing probe IDs\n",
    "gene_col = 'gene_assignment'  # Column containing gene symbols/names\n",
    "\n",
    "# 2. Get gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "print(f\"Generated mapping with {len(gene_mapping)} rows\")\n",
    "\n",
    "# Preview the mapping to verify\n",
    "print(\"\\nPreview of gene mapping:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# Verify the gene data before mapping\n",
    "print(\"\\nGene data shape before mapping:\", gene_data.shape)\n",
    "\n",
    "# 3. Convert probe-level measurements to gene expression data\n",
    "print(\"\\nApplying gene mapping to convert probe IDs to gene symbols...\")\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(f\"Successfully mapped gene data with {len(gene_data.index)} genes\")\n",
    "\n",
    "# Show example of gene symbols in the resulting dataframe\n",
    "print(\"\\nFirst 20 gene symbols in mapped gene expression data:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# Save gene expression data to 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\"\\nGene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd805df",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f28814fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:25:14.818013Z",
     "iopub.status.busy": "2025-03-25T07:25:14.817892Z",
     "iopub.status.idle": "2025-03-25T07:25:17.398224Z",
     "shell.execute_reply": "2025-03-25T07:25:17.397861Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saving normalized gene data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Lactose_Intolerance/gene_data/GSE136395.csv\n",
      "\n",
      "Extracting clinical data...\n",
      "Clinical data saved to ../../output/preprocess/Lactose_Intolerance/clinical_data/GSE136395.csv\n",
      "Clinical data shape: (3, 42)\n",
      "Trait information available: False\n",
      "\n",
      "Linking clinical and genetic data...\n",
      "Cannot link data: clinical data is not available\n",
      "\n",
      "Skipping missing value handling and bias evaluation as linked data is not available\n",
      "\n",
      "Performing final validation...\n",
      "Abnormality detected in the cohort: GSE136395. Preprocessing failed.\n",
      "A new JSON file was created at: ../../output/preprocess/Lactose_Intolerance/cohort_info.json\n",
      "\n",
      "Dataset usability for Lactose_Intolerance association studies: False\n",
      "Reason: Dataset does not contain required trait information\n"
     ]
    }
   ],
   "source": [
    "# 1. Save the normalized gene expression data from the previous step\n",
    "print(\"\\nSaving normalized gene 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\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Extract clinical data from the matrix file\n",
    "print(\"\\nExtracting clinical data...\")\n",
    "try:\n",
    "    # Get the file paths again to make sure we have them\n",
    "    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "    \n",
    "    # Extract background information and clinical data\n",
    "    background_info, clinical_data = get_background_and_clinical_data(\n",
    "        matrix_file, \n",
    "        prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],\n",
    "        prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "    )\n",
    "    \n",
    "    # Process clinical data using trait information from Step 2\n",
    "    trait_row = 1  # Based on analysis in step 2 - group (OW/OB vs NW/MONW)\n",
    "    gender_row = 0  # Gender data\n",
    "    age_row = 2     # Age data\n",
    "    \n",
    "    # Define conversion functions based on Step 2\n",
    "    def convert_trait(value):\n",
    "        \"\"\"Convert trait value (binary: 1 for OW/OB, 0 for NW/MONW)\"\"\"\n",
    "        if pd.isna(value):\n",
    "            return None\n",
    "        \n",
    "        # Extract value after colon if present\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        if 'OW/OB' in value:\n",
    "            return 1  # Overweight/Obese is associated with higher LDL cholesterol\n",
    "        elif 'NW' in value or 'MONW' in value:\n",
    "            return 0  # Normal weight (includes metabolically obese normal weight)\n",
    "        else:\n",
    "            return None\n",
    "\n",
    "    def convert_gender(value):\n",
    "        \"\"\"Convert gender value to binary (0: female, 1: male)\"\"\"\n",
    "        if pd.isna(value):\n",
    "            return None\n",
    "        \n",
    "        # Extract value after colon if present\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        # Convert gender\n",
    "        if value.lower() == 'woman':\n",
    "            return 0\n",
    "        elif value.lower() == 'man':\n",
    "            return 1\n",
    "        else:\n",
    "            return None\n",
    "    \n",
    "    def convert_age(value):\n",
    "        \"\"\"Convert age value to float\"\"\"\n",
    "        if pd.isna(value):\n",
    "            return None\n",
    "        \n",
    "        # Extract value after colon if present\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        try:\n",
    "            return float(value)  # Convert to float for continuous variable\n",
    "        except:\n",
    "            return None\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "    \n",
    "    # Check if we have valid trait information\n",
    "    is_trait_available = trait_row is not None and not selected_clinical_df.loc[trait].isnull().all()\n",
    "    print(f\"Trait information available: {is_trait_available}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error extracting clinical data: {e}\")\n",
    "    is_trait_available = False\n",
    "    selected_clinical_df = pd.DataFrame()\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "print(\"\\nLinking clinical and genetic data...\")\n",
    "try:\n",
    "    if is_trait_available and not selected_clinical_df.empty:\n",
    "        # Link clinical and genetic data\n",
    "        linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
    "        print(f\"Created linked data with {linked_data.shape[0]} samples and {linked_data.shape[1]} features\")\n",
    "    else:\n",
    "        print(\"Cannot link data: clinical data is not available\")\n",
    "        linked_data = pd.DataFrame()\n",
    "        is_trait_available = False\n",
    "except Exception as e:\n",
    "    print(f\"Error linking clinical and genetic data: {e}\")\n",
    "    is_trait_available = False\n",
    "    linked_data = pd.DataFrame()\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "if is_trait_available and not linked_data.empty:\n",
    "    print(\"\\nHandling missing values...\")\n",
    "    try:\n",
    "        # Rename the first column to the trait name for consistency\n",
    "        if linked_data.columns[0] != trait:\n",
    "            linked_data = linked_data.rename(columns={linked_data.columns[0]: trait})\n",
    "        \n",
    "        linked_data = handle_missing_values(linked_data, trait)\n",
    "        print(f\"After handling missing values: {linked_data.shape[0]} samples and {linked_data.shape[1]} features\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error handling missing values: {e}\")\n",
    "        \n",
    "    # 5. Determine whether the trait and demographic features are biased\n",
    "    print(\"\\nEvaluating feature bias...\")\n",
    "    try:\n",
    "        is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "        print(f\"Trait bias determination: {is_biased}\")\n",
    "        print(f\"Final linked data shape: {linked_data.shape[0]} samples and {linked_data.shape[1]} features\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error evaluating feature bias: {e}\")\n",
    "        is_biased = True\n",
    "else:\n",
    "    print(\"\\nSkipping missing value handling and bias evaluation as linked data is not available\")\n",
    "    is_biased = True\n",
    "\n",
    "# 6. Validate and save cohort information\n",
    "print(\"\\nPerforming final validation...\")\n",
    "note = \"\"\n",
    "if not is_trait_available:\n",
    "    note = \"Dataset does not contain required trait information\"\n",
    "elif is_biased:\n",
    "    note = \"Dataset has severe bias in the trait distribution\"\n",
    "\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 7. Save the linked data if usable\n",
    "print(f\"\\nDataset usability for {trait} association studies: {is_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\"Final linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    if note:\n",
    "        print(f\"Reason: {note}\")\n",
    "    else:\n",
    "        print(\"Dataset does not meet quality criteria for the specified trait\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}