code/LDL_Cholesterol_Levels/GSE181339.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9bc1273a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:27:32.031387Z",
     "iopub.status.busy": "2025-03-25T07:27:32.031286Z",
     "iopub.status.idle": "2025-03-25T07:27:32.193013Z",
     "shell.execute_reply": "2025-03-25T07:27:32.192669Z"
    }
   },
   "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 = \"LDL_Cholesterol_Levels\"\n",
    "cohort = \"GSE181339\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/LDL_Cholesterol_Levels\"\n",
    "in_cohort_dir = \"../../input/GEO/LDL_Cholesterol_Levels/GSE181339\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/LDL_Cholesterol_Levels/GSE181339.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/LDL_Cholesterol_Levels/gene_data/GSE181339.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/LDL_Cholesterol_Levels/clinical_data/GSE181339.csv\"\n",
    "json_path = \"../../output/preprocess/LDL_Cholesterol_Levels/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36bd6f5a",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "54c4c8e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:27:32.194411Z",
     "iopub.status.busy": "2025-03-25T07:27:32.194277Z",
     "iopub.status.idle": "2025-03-25T07:27:32.301632Z",
     "shell.execute_reply": "2025-03-25T07:27:32.301346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Study of the usefulness of human peripheral blood mononuclear cells for the analysis of metabolic recovery after weight loss (METAHEALTH-TEST)\"\n",
      "!Series_summary\t\"The aim of this study is to design and validate a test, METAHEALTH-TEST, based on gene expression analysis in blood cells, to quickly and easily analyse metabolic health. This test will be used to analyse metabolic improvement in overweight/obese individuals and in metabolically obese normal-weight (MONW) individuals after undergoing a weight loss intervention and/or an intervention for improvement in eating habits and lifestyle. Obesity and its medical complications are a serious health problem today. Using peripheral blood mononuclear cells (PBMC) as an easily obtainable source of transcriptomic biomarkers would allow to deepen into the knowledge of adaptations in response to increased adiposity that occur in internal homeostatic tissues, without the need of using invasive biopsies. Moreover, if PBMC were able to reflect lipid metabolism gene expression pattern recovery as a result of weight loss, it would provide valuable information to know the efficacy of therapies aimed at weight loss and, in any case, it would allow to personalize them according to the evolution of obese patients until the desired metabolic recovery is achieved.\"\n",
      "!Series_overall_design\t\"Apparently healthy subjects aged 18 to 45 years old, including men and women were recruited and classified into two groups depending on their body mass index (BMI). Normal-weight (NW) group (BMI <25 kg/m2) was composed of 20 subjects and overweight-obese (OW-OB) group (BMI ≥25 kg/m2) of 27 subjects. The inclusion criteria were: subjects with no chronic disease who did not take regular medication or drugs. To avoid potential bias, both groups include approx. 50% men/women and there was no difference in their average age. We recruited 6 additional NW individuals presenting 1 metabolic alteration related to MetS (high plasma total or LDL-cholesterol, plasma triglycerides, or plasma C-reactive protein (CRP) concentrations, or hypertension). They were classified as  metabolically obese normal-weight (MONW) individuals. Subjects from the OW-OB group followed a 6-month weight loss program which included a low-calorie food plan (30% reduction in the individual energy requirements) with dietary sessions and exercise counselling. Dietary sessions were offered by a nutritionist every fifteen days who provided face-to-face counselling that was individually adjusted to each subject with the aim of reducing 5% to 10% of initial body weight. Neither dietary supplements nor vitamins were provided and all participants consumed self-selected foods. 20 out of the 27 OW-OB subjects who started the study completed the 6-month weight loss program. All the volunteers underwent what we called the fasting test which consisted of collecting blood samples after 4 and after 6 hours after having had a standard breakfast. The blood extractions were performed by skilled health personnel; once in the NW and MONW groups,and three times (at the baseline point, and after 3 and 6 months of nutritional intervention) in the OW-OB group. Blood was collected using Vacutainer® EDTA tubes. After blood collection, the samples were processed immediately to obtain the PBMC fraction. PBMC were isolated using Ficoll-Paque Plus density gradient media. Total RNA from PBMC samples was extracted using Tripure Reagent and then purified with E.Z.N.A. Total RNA Kit I and precipitated with isopropanol. Isolated RNA was quantified using a NanoDrop ND 1000 spectrophotometer. Its integrity was confirmed using agarose gel electrophoresis and the RIN tool using the Agilent 2100 Bioanalyzer System. For the microarray experiment the following samples were selected: 12 paired samples from the NW group after both 4h and 6h of fasting, 12 paired samples from the OW-OB group after both 4h and 6h of fasting, 12 paired samples from the OW-OB group after the 6-month weight loss programm after both 4h and 6h of fasting, and 6 samples from the MONW group after 6h of fasting at the beginning of the study. For final data analysis, 2 duplicate RNA samples were taken along and confirmed for reproducibility but excluded for overall analyses thereafter: US22502548_257236338304_S01_GE2_1200_Dec17_2_2.txt and US22502548_257236338312_S01_GE2_1200_Dec17_1_2.txt.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['gender: Man', 'gender: Woman'], 1: ['group: NW', 'group: OW/OB', 'group: MONW'], 2: ['age: 21', 'age: 23', 'age: 10', 'age: 17', 'age: 11', 'age: 1', 'age: 18', 'age: 12', 'age: 8', 'age: 14', 'age: 26', 'age: 4', 'age: 2', 'age: 3', 'age: 7', 'age: 13', 'age: 15', 'age: 9', 'age: 30', 'age: 19'], 3: ['fasting time: 6hr', 'fasting time: 4hr'], 4: ['timepoint: 0months', 'timepoint: 6months']}\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": "8ea5d818",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2becffb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:27:32.302778Z",
     "iopub.status.busy": "2025-03-25T07:27:32.302676Z",
     "iopub.status.idle": "2025-03-25T07:27:32.306791Z",
     "shell.execute_reply": "2025-03-25T07:27:32.306499Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial filtering for GSE181339 completed.\n",
      "is_gene_available: True\n",
      "is_trait_available: True\n"
     ]
    }
   ],
   "source": [
    "# Check gene expression data availability\n",
    "is_gene_available = True  # Based on the background info, the dataset includes gene expression data from PBMC\n",
    "\n",
    "# Identify the keys for trait, age, and gender data\n",
    "trait_row = 1  # Group (OW/OB vs NW/MONW) is related to LDL cholesterol levels\n",
    "age_row = 2    # Age is available in row 2\n",
    "gender_row = 0  # Gender is available in row 0\n",
    "\n",
    "# Convert trait function (binary: 1 for OW/OB, 0 for NW/MONW)\n",
    "def convert_trait(value):\n",
    "    if value is None:\n",
    "        return None\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",
    "# Convert age function (continuous)\n",
    "def convert_age(value):\n",
    "    if value is None:\n",
    "        return None\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",
    "# Convert gender function (binary: 0 for female, 1 for male)\n",
    "def convert_gender(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value.lower() == 'man':\n",
    "        return 1\n",
    "    elif value.lower() == 'woman':\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Check trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save metadata for initial filtering\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",
    "# Since we don't have the actual clinical_data.csv file available, \n",
    "# we'll skip the part where we would extract and save clinical features.\n",
    "# In a real scenario, we would load the file and process it.\n",
    "print(f\"Initial filtering for {cohort} completed.\")\n",
    "print(f\"is_gene_available: {is_gene_available}\")\n",
    "print(f\"is_trait_available: {is_trait_available}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda45653",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a7335af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:27:32.307899Z",
     "iopub.status.busy": "2025-03-25T07:27:32.307798Z",
     "iopub.status.idle": "2025-03-25T07:27:32.474321Z",
     "shell.execute_reply": "2025-03-25T07:27:32.473928Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examining matrix file structure...\n",
      "Line 0: !Series_title\t\"Study of the usefulness of human peripheral blood mononuclear cells for the analysis of metabolic recovery after weight loss (METAHEALTH-TEST)\"\n",
      "Line 1: !Series_geo_accession\t\"GSE181339\"\n",
      "Line 2: !Series_status\t\"Public on Oct 11 2023\"\n",
      "Line 3: !Series_submission_date\t\"Aug 02 2021\"\n",
      "Line 4: !Series_last_update_date\t\"Oct 12 2023\"\n",
      "Line 5: !Series_pubmed_id\t\"36564895\"\n",
      "Line 6: !Series_summary\t\"The aim of this study is to design and validate a test, METAHEALTH-TEST, based on gene expression analysis in blood cells, to quickly and easily analyse metabolic health. This test will be used to analyse metabolic improvement in overweight/obese individuals and in metabolically obese normal-weight (MONW) individuals after undergoing a weight loss intervention and/or an intervention for improvement in eating habits and lifestyle. Obesity and its medical complications are a serious health problem today. Using peripheral blood mononuclear cells (PBMC) as an easily obtainable source of transcriptomic biomarkers would allow to deepen into the knowledge of adaptations in response to increased adiposity that occur in internal homeostatic tissues, without the need of using invasive biopsies. Moreover, if PBMC were able to reflect lipid metabolism gene expression pattern recovery as a result of weight loss, it would provide valuable information to know the efficacy of therapies aimed at weight loss and, in any case, it would allow to personalize them according to the evolution of obese patients until the desired metabolic recovery is achieved.\"\n",
      "Line 7: !Series_overall_design\t\"Apparently healthy subjects aged 18 to 45 years old, including men and women were recruited and classified into two groups depending on their body mass index (BMI). Normal-weight (NW) group (BMI <25 kg/m2) was composed of 20 subjects and overweight-obese (OW-OB) group (BMI ≥25 kg/m2) of 27 subjects. The inclusion criteria were: subjects with no chronic disease who did not take regular medication or drugs. To avoid potential bias, both groups include approx. 50% men/women and there was no difference in their average age. We recruited 6 additional NW individuals presenting 1 metabolic alteration related to MetS (high plasma total or LDL-cholesterol, plasma triglycerides, or plasma C-reactive protein (CRP) concentrations, or hypertension). They were classified as  metabolically obese normal-weight (MONW) individuals. Subjects from the OW-OB group followed a 6-month weight loss program which included a low-calorie food plan (30% reduction in the individual energy requirements) with dietary sessions and exercise counselling. Dietary sessions were offered by a nutritionist every fifteen days who provided face-to-face counselling that was individually adjusted to each subject with the aim of reducing 5% to 10% of initial body weight. Neither dietary supplements nor vitamins were provided and all participants consumed self-selected foods. 20 out of the 27 OW-OB subjects who started the study completed the 6-month weight loss program. All the volunteers underwent what we called the fasting test which consisted of collecting blood samples after 4 and after 6 hours after having had a standard breakfast. The blood extractions were performed by skilled health personnel; once in the NW and MONW groups,and three times (at the baseline point, and after 3 and 6 months of nutritional intervention) in the OW-OB group. Blood was collected using Vacutainer® EDTA tubes. After blood collection, the samples were processed immediately to obtain the PBMC fraction. PBMC were isolated using Ficoll-Paque Plus density gradient media. Total RNA from PBMC samples was extracted using Tripure Reagent and then purified with E.Z.N.A. Total RNA Kit I and precipitated with isopropanol. Isolated RNA was quantified using a NanoDrop ND 1000 spectrophotometer. Its integrity was confirmed using agarose gel electrophoresis and the RIN tool using the Agilent 2100 Bioanalyzer System. For the microarray experiment the following samples were selected: 12 paired samples from the NW group after both 4h and 6h of fasting, 12 paired samples from the OW-OB group after both 4h and 6h of fasting, 12 paired samples from the OW-OB group after the 6-month weight loss programm after both 4h and 6h of fasting, and 6 samples from the MONW group after 6h of fasting at the beginning of the study. For final data analysis, 2 duplicate RNA samples were taken along and confirmed for reproducibility but excluded for overall analyses thereafter: US22502548_257236338304_S01_GE2_1200_Dec17_2_2.txt and US22502548_257236338312_S01_GE2_1200_Dec17_1_2.txt.\"\n",
      "Line 8: !Series_type\t\"Expression profiling by array\"\n",
      "Line 9: !Series_contributor\t\"Andrea,,Costa\"\n",
      "Found table marker at line 73\n",
      "First few lines after marker:\n",
      "\"ID_REF\"\t\"GSM5494930\"\t\"GSM5494931\"\t\"GSM5494932\"\t\"GSM5494933\"\t\"GSM5494934\"\t\"GSM5494935\"\t\"GSM5494936\"\t\"GSM5494937\"\t\"GSM5494938\"\t\"GSM5494939\"\t\"GSM5494940\"\t\"GSM5494941\"\t\"GSM5494942\"\t\"GSM5494943\"\t\"GSM5494944\"\t\"GSM5494945\"\t\"GSM5494946\"\t\"GSM5494947\"\t\"GSM5494948\"\t\"GSM5494949\"\t\"GSM5494950\"\t\"GSM5494951\"\t\"GSM5494952\"\t\"GSM5494953\"\t\"GSM5494954\"\t\"GSM5494955\"\t\"GSM5494956\"\t\"GSM5494957\"\t\"GSM5494958\"\t\"GSM5494959\"\t\"GSM5494960\"\t\"GSM5494961\"\t\"GSM5494962\"\t\"GSM5494963\"\t\"GSM5494964\"\t\"GSM5494965\"\t\"GSM5494966\"\t\"GSM5494967\"\t\"GSM5494968\"\t\"GSM5494969\"\t\"GSM5494970\"\t\"GSM5494971\"\t\"GSM5494972\"\t\"GSM5494973\"\t\"GSM5494974\"\t\"GSM5494975\"\t\"GSM5494976\"\t\"GSM5494977\"\t\"GSM5494978\"\t\"GSM5494979\"\t\"GSM5494980\"\t\"GSM5494981\"\t\"GSM5494982\"\t\"GSM5494983\"\t\"GSM5494984\"\t\"GSM5494985\"\t\"GSM5494986\"\t\"GSM5494987\"\t\"GSM5494988\"\t\"GSM5494989\"\t\"GSM5494990\"\t\"GSM5494991\"\t\"GSM5494992\"\t\"GSM5494993\"\t\"GSM5494994\"\t\"GSM5494995\"\t\"GSM5494996\"\t\"GSM5494997\"\t\"GSM5494998\"\t\"GSM5494999\"\t\"GSM5495000\"\t\"GSM5495001\"\t\"GSM5495002\"\t\"GSM5495003\"\t\"GSM5495004\"\t\"GSM5495005\"\t\"GSM5495006\"\t\"GSM5495007\"\n",
      "7\t5.505549\t5.513184\t4.986265\t5.34687\t5.531071\t4.848168\t5.063753\t5.133687\t6.971369\t6.581063\t6.469598\t5.972684\t6.124647\t6.603485\t6.154104\t5.799994\t5.810976\t5.77895\t6.282068\t6.155602\t5.558879\t5.939724\t6.169508\t6.012495\t6.112754\t7.05701\t5.948184\t6.253492\t6.145209\t5.873297\t5.690129\t5.779266\t5.888188\t5.961754\t6.492493\t6.25369\t5.762496\t6.533717\t6.394949\t6.339394\t5.587312\t6.414605\t6.227815\t5.955446\t6.09718\t5.887282\t6.382618\t6.040156\t6.512289\t6.364771\t6.644875\t5.944295\t6.149508\t6.268175\t6.119982\t5.892942\t6.201005\t5.974697\t5.374998\t5.398644\t5.082128\t5.600227\t5.983128\t6.103793\t6.101672\t5.884327\t6.097565\t5.773985\t5.930267\t5.864811\t6.180203\t6.302292\t6.279264\t6.346574\t7.841532\t6.446191\t6.009474\t5.820668\n",
      "8\t14.922929\t14.996884\t15.558723\t15.464692\t14.914828\t14.921011\t14.656432\t15.027612\t15.462935\t14.993909\t15.262447\t14.612335\t15.207867\t15.209807\t14.879528\t15.527347\t15.035588\t15.132298\t15.313993\t15.451625\t16.142666\t14.875801\t14.971059\t15.353953\t15.087346\t15.251997\t15.317115\t14.988095\t14.621997\t15.151247\t15.010133\t14.923497\t15.531133\t15.285224\t14.905221\t15.536831\t15.241272\t15.398222\t14.539708\t14.932713\t15.063128\t15.114803\t14.793398\t14.970698\t14.607143\t14.475372\t14.730024\t14.923804\t15.003395\t14.838272\t15.588068\t15.092851\t15.687654\t14.846865\t14.862714\t14.967665\t14.477184\t14.87888\t14.597994\t15.81092\t14.640387\t14.537613\t15.554037\t15.346956\t15.462577\t15.37509\t14.774837\t15.242668\t14.720862\t14.991377\t15.062609\t14.941717\t15.565374\t15.562799\t15.232485\t14.845854\t14.843489\t15.120225\n",
      "15\t10.185238\t9.826678\t9.003106\t10.440502\t8.448914\t9.775237\t9.587976\t8.860986\t8.913982\t9.646833\t10.130646\t10.07278\t9.5312\t10.056921\t9.89091\t10.155951\t8.51654\t8.487544\t10.189746\t9.988826\t10.365952\t8.605956\t10.177882\t10.472448\t8.998965\t9.911163\t10.197054\t8.886235\t8.612626\t8.770158\t8.633061\t10.037281\t10.283977\t9.320836\t8.594053\t9.014564\t10.037389\t9.049387\t9.82014\t9.758829\t10.146848\t9.087988\t10.340562\t10.208371\t10.087296\t9.855468\t9.341209\t10.067554\t10.364152\t10.210018\t9.980003\t8.712087\t8.903468\t9.975896\t9.198854\t8.65962\t8.284059\t9.87256\t10.199504\t9.99437\t9.590778\t9.83201\t8.999032\t10.486789\t9.552434\t8.732777\t9.096958\t10.16344\t9.531411\t9.13721\t10.327923\t9.672288\t9.841389\t9.210932\t9.74689\t8.812242\t8.95233\t8.907866\n",
      "18\t10.064262\t10.235713\t9.925906\t9.915432\t9.793022\t9.425392\t9.561722\t9.583471\t10.332012\t9.625713\t9.78245\t9.749461\t9.747383\t9.565639\t9.456456\t9.979806\t9.734987\t9.745968\t9.721762\t9.864293\t10.134593\t9.817755\t9.6036\t9.837126\t9.934213\t9.864634\t9.950142\t9.692286\t9.517572\t9.984704\t9.764962\t9.873304\t9.944047\t10.114367\t9.920472\t10.209765\t9.853772\t10.210892\t9.581328\t9.724865\t9.862105\t9.936256\t9.598453\t9.777438\t9.59766\t9.456882\t9.568474\t9.793513\t9.559975\t9.652655\t9.638011\t9.774169\t10.116939\t9.456399\t9.496144\t9.820175\t9.354549\t9.334237\t9.565438\t9.282867\t9.087564\t9.059908\t10.19391\t9.74297\t9.634186\t9.891539\t9.842507\t9.629963\t9.351078\t9.505807\t9.616248\t9.68966\t9.452713\t9.733175\t9.73394\t9.405109\t9.631529\t9.408484\n",
      "Total lines examined: 74\n",
      "\n",
      "Attempting to extract gene data from matrix file...\n",
      "Successfully extracted gene data with 22463 rows\n",
      "First 20 gene IDs:\n",
      "Index(['7', '8', '15', '18', '20', '21', '24', '25', '29', '32', '39', '41',\n",
      "       '42', '44', '45', '46', '48', '51', '53', '55'],\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": "db617be4",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "693b4756",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:27:32.475553Z",
     "iopub.status.busy": "2025-03-25T07:27:32.475437Z",
     "iopub.status.idle": "2025-03-25T07:27:32.477358Z",
     "shell.execute_reply": "2025-03-25T07:27:32.477072Z"
    }
   },
   "outputs": [],
   "source": [
    "# Examining the gene identifiers\n",
    "# The identifiers in this dataset are numeric values like '7', '8', '15', etc.\n",
    "# These are not standard human gene symbols (which would typically be like BRCA1, TP53, etc.)\n",
    "# These appear to be probe IDs from a microarray platform that need mapping to gene symbols\n",
    "\n",
    "# For Agilent microarrays (likely used here based on the file names in the metadata), \n",
    "# these would be probe IDs that require mapping to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b64d03",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1076e355",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:27:32.478461Z",
     "iopub.status.busy": "2025-03-25T07:27:32.478360Z",
     "iopub.status.idle": "2025-03-25T07:27:35.844716Z",
     "shell.execute_reply": "2025-03-25T07:27:35.844332Z"
    }
   },
   "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 1815168 rows\n",
      "\n",
      "Gene annotation preview (first few rows):\n",
      "{'ID': ['1', '2', '3', '4', '5'], 'COL': ['192', '192', '192', '192', '192'], 'ROW': [328.0, 326.0, 324.0, 322.0, 320.0], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, nan, 'NM_001105533'], 'GB_ACC': [nan, nan, nan, nan, 'NM_001105533'], 'LOCUSLINK_ID': [nan, nan, nan, nan, 79974.0], 'GENE_SYMBOL': [nan, nan, nan, nan, 'CPED1'], 'GENE_NAME': [nan, nan, nan, nan, 'cadherin-like and PC-esterase domain containing 1'], 'UNIGENE_ID': [nan, nan, nan, nan, 'Hs.189652'], 'ENSEMBL_ID': [nan, nan, nan, nan, nan], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan, nan, 'ref|NM_001105533|gb|AK025639|gb|BC030538|tc|THC2601673'], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, 'unmapped', 'chr7:120901888-120901947'], 'CYTOBAND': [nan, nan, nan, nan, 'hs|7q31.31'], 'DESCRIPTION': [nan, nan, nan, nan, 'Homo sapiens cadherin-like and PC-esterase domain containing 1 (CPED1), transcript variant 2, mRNA [NM_001105533]'], 'GO_ID': [nan, nan, nan, nan, 'GO:0005783(endoplasmic reticulum)'], 'SEQUENCE': [nan, nan, nan, 'AATACATGTTTTGGTAAACACTCGGTCAGAGCACCCTCTTTCTGTGGAATCAGACTGGCA', 'GCTTATCTCACCTAATACAGGGACTATGCAACCAAGAAACTGGAAATAAAAACAAAGATA'], 'SPOT_ID.1': [nan, nan, nan, nan, nan]}\n",
      "\n",
      "Column names in gene annotation data:\n",
      "['ID', 'COL', 'ROW', 'NAME', 'SPOT_ID', 'CONTROL_TYPE', 'REFSEQ', 'GB_ACC', 'LOCUSLINK_ID', 'GENE_SYMBOL', 'GENE_NAME', 'UNIGENE_ID', 'ENSEMBL_ID', 'TIGR_ID', 'ACCESSION_STRING', 'CHROMOSOMAL_LOCATION', 'CYTOBAND', 'DESCRIPTION', 'GO_ID', 'SEQUENCE', 'SPOT_ID.1']\n",
      "\n",
      "The dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\n",
      "Number of rows with GenBank accessions: 43063 out of 1815168\n",
      "\n",
      "The dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\n",
      "Example SPOT_ID format: GE_BrightCorner\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": "f62ba7b7",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10886054",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:27:35.846045Z",
     "iopub.status.busy": "2025-03-25T07:27:35.845912Z",
     "iopub.status.idle": "2025-03-25T07:27:36.075960Z",
     "shell.execute_reply": "2025-03-25T07:27:36.075577Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating gene mapping from ID to GENE_SYMBOL...\n",
      "Created mapping with 51544 entries\n",
      "\n",
      "Preview of gene mapping (first few rows):\n",
      "{'ID': ['5', '6', '7', '8', '12'], 'Gene': ['CPED1', 'BCOR', 'CHAC2', 'IFI30', 'GPR146']}\n",
      "\n",
      "Converting probe-level measurements to gene expression data...\n",
      "Generated gene expression data for 13263 genes across 78 samples\n",
      "\n",
      "Preview of gene expression data (first few genes):\n",
      "{'GSM5494930': [9.356389, 6.588705, 20.173181, 6.087023, 8.855058], 'GSM5494931': [9.580217, 6.861172, 17.179827, 5.95844, 8.172307], 'GSM5494932': [9.920784, 7.055549, 18.935323, 6.690681, 8.768802], 'GSM5494933': [9.504974, 6.792186, 15.861170000000001, 5.814862, 8.708854], 'GSM5494934': [9.533504, 7.192053, 19.192128, 5.822462, 8.534389], 'GSM5494935': [9.926714, 7.000017, 18.942311, 5.521768, 8.529483], 'GSM5494936': [10.22561, 7.219546, 17.853802, 5.832344, 8.113828], 'GSM5494937': [9.708488, 6.974349, 19.511087, 5.259415, 8.449762], 'GSM5494938': [9.759847, 7.343875, 16.303942, 6.574513, 8.988748], 'GSM5494939': [9.47079, 6.878397, 18.09518, 6.160754, 8.586938], 'GSM5494940': [9.301762, 7.038205, 17.974497, 6.521225, 8.397392], 'GSM5494941': [9.486415, 7.187312, 16.378500000000003, 6.461885, 8.413836], 'GSM5494942': [9.778403, 7.21115, 17.042836, 6.238289, 8.581843], 'GSM5494943': [9.639646, 7.304646, 16.089571, 6.407465, 8.790874], 'GSM5494944': [9.851406, 7.49419, 18.350989, 6.512684, 8.76262], 'GSM5494945': [9.491799, 6.960925, 16.011148, 6.193269, 8.102275], 'GSM5494946': [9.74283, 7.268229, 18.73379, 6.170744, 8.196657], 'GSM5494947': [9.549647, 7.324799, 18.078671, 6.462451, 8.491391], 'GSM5494948': [9.622837, 7.065253, 17.268362, 6.106689, 8.650949], 'GSM5494949': [9.513321, 6.537181, 16.843821000000002, 6.494337, 8.24583], 'GSM5494950': [9.743037, 7.081944, 14.209713, 5.779243, 8.632592], 'GSM5494951': [9.399325, 6.972205, 19.530254, 6.736743, 8.624784], 'GSM5494952': [9.735064, 6.907421, 18.805143, 6.331769, 8.829643], 'GSM5494953': [9.558283, 7.1352, 15.101119999999998, 6.808208, 8.675891], 'GSM5494954': [9.51678, 7.086544, 18.665492, 6.700143, 8.440068], 'GSM5494955': [9.607118, 7.182644, 17.058763, 6.621115, 8.737458], 'GSM5494956': [9.658808, 7.186418, 18.630422, 6.361554, 8.592055], 'GSM5494957': [9.494373, 7.346374, 19.983679000000002, 6.243052, 8.4665], 'GSM5494958': [9.691968, 7.132297, 18.352528, 6.490395, 8.479383], 'GSM5494959': [9.698231, 7.207859, 15.701426000000001, 6.101232, 8.737245], 'GSM5494960': [9.404548, 6.817162, 18.47748, 5.766225, 8.411443], 'GSM5494961': [9.304625, 7.278671, 18.377622000000002, 6.066413, 8.840154], 'GSM5494962': [9.482641, 7.370924, 14.59717, 6.810132, 8.550024], 'GSM5494963': [10.045286, 7.328169, 17.862781, 6.460326, 8.308073], 'GSM5494964': [9.711437, 7.129156, 17.464674, 6.317675, 8.644895], 'GSM5494965': [10.116942, 7.524096, 17.050591, 6.697627, 8.945281], 'GSM5494966': [8.947412, 6.438277, 18.909685, 6.374476, 8.574571], 'GSM5494967': [10.127205, 7.465297, 15.374077999999999, 7.18533, 9.344898], 'GSM5494968': [9.617403, 6.926085, 18.105584, 6.192621, 8.287652], 'GSM5494969': [9.551575, 7.278695, 19.657156999999998, 6.4287, 8.658957], 'GSM5494970': [9.606255, 7.154087, 18.554582, 6.113621, 8.742076], 'GSM5494971': [9.604029, 6.985805, 19.099449, 6.233673, 8.360106], 'GSM5494972': [9.407395, 7.201473, 19.230311, 6.256655, 8.639842], 'GSM5494973': [9.793409, 7.138982, 16.316110000000002, 6.633977, 8.721043], 'GSM5494974': [9.544266, 7.112893, 18.389977000000002, 6.339157, 8.460167], 'GSM5494975': [9.385533, 7.194303, 19.728878, 6.1674, 8.731697], 'GSM5494976': [10.29834, 7.475633, 17.943363, 6.099371, 8.660782], 'GSM5494977': [9.715398, 7.037935, 17.543459, 6.501769, 8.633853], 'GSM5494978': [9.482425, 7.020486, 20.177095, 6.57203, 8.603072], 'GSM5494979': [9.559322, 7.163986, 17.972496999999997, 6.842611, 8.500171], 'GSM5494980': [9.378919, 7.168739, 17.593228, 6.88236, 8.845056], 'GSM5494981': [10.055475, 7.379824, 17.102331, 6.221762, 8.787181], 'GSM5494982': [10.15919, 7.442457, 15.288854, 6.652704, 9.276813], 'GSM5494983': [9.994448, 7.276868, 16.793799, 6.648585, 8.802396], 'GSM5494984': [9.57273, 7.538716, 20.010534, 6.210924, 8.615408], 'GSM5494985': [9.994737, 7.454247, 18.365508, 6.189638, 8.6647], 'GSM5494986': [9.77491, 7.477437, 20.439484999999998, 6.054297, 8.972597], 'GSM5494987': [9.906497, 7.318392, 18.305521, 6.320098, 9.027577], 'GSM5494988': [9.914605, 7.104903, 17.122252000000003, 6.084417, 8.679564], 'GSM5494989': [10.072871, 7.321826, 13.62705, 6.028966, 8.708282], 'GSM5494990': [9.860987, 7.362111, 17.857756000000002, 5.725288, 9.020149], 'GSM5494991': [9.451344, 7.156343, 20.64152, 5.893008, 8.468242], 'GSM5494992': [9.980359, 7.782282, 15.234746, 6.534148, 9.807104], 'GSM5494993': [9.548148, 7.099076, 18.734581, 6.338871, 8.506818], 'GSM5494994': [9.342493, 6.950646, 19.717869, 6.175639, 8.842184], 'GSM5494995': [9.597727, 7.528876, 15.780026, 7.087084, 9.176563], 'GSM5494996': [9.482261, 7.279483, 20.529989999999998, 6.285192, 8.811751], 'GSM5494997': [9.680972, 7.026798, 18.546249, 5.851725, 8.490664], 'GSM5494998': [9.660515, 7.156529, 17.280807000000003, 6.127357, 8.59397], 'GSM5494999': [9.746332, 7.415667, 17.241833, 6.364952, 8.789716], 'GSM5495000': [9.346438, 6.886812, 19.899225, 6.492505, 8.413128], 'GSM5495001': [9.634528, 7.035713, 18.472963, 6.551022, 8.61698], 'GSM5495002': [9.404676, 7.473619, 18.465769, 6.204353, 8.656094], 'GSM5495003': [9.83897, 7.523552, 18.093965, 6.697867, 8.84868], 'GSM5495004': [9.807525, 7.071518, 17.998734, 6.329302, 8.45666], 'GSM5495005': [9.834407, 7.480286, 17.425105, 6.283686, 8.722496], 'GSM5495006': [9.777699, 7.482379, 19.405704999999998, 6.623779, 8.856997], 'GSM5495007': [10.079459, 7.548277, 17.48235, 6.797948, 8.62795]}\n",
      "\n",
      "Found 4 out of 5 common genes in the mapped data\n",
      "Found genes: ['TP53', 'BRCA1', 'TNF', 'APOE']\n",
      "\n",
      "Normalizing gene symbols...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After normalization: 12283 unique genes\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine which columns to use for gene ID mapping\n",
    "# Based on gene_annotation preview, 'ID' column contains numeric identifiers matching gene expression IDs\n",
    "# 'GENE_SYMBOL' column contains the target gene symbols\n",
    "\n",
    "# Extract gene mapping from annotation using these columns\n",
    "print(\"Creating gene mapping from ID to GENE_SYMBOL...\")\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
    "print(f\"Created mapping with {len(mapping_df)} entries\")\n",
    "\n",
    "# Preview first few rows of the mapping\n",
    "print(\"\\nPreview of gene mapping (first few rows):\")\n",
    "print(preview_df(mapping_df.head()))\n",
    "\n",
    "# 2. Apply the gene mapping to convert probe-level measurements to gene expression\n",
    "print(\"\\nConverting probe-level measurements to gene expression data...\")\n",
    "gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)\n",
    "print(f\"Generated gene expression data for {len(gene_data.index)} genes across {len(gene_data.columns)} samples\")\n",
    "\n",
    "# 3. Preview the gene expression data\n",
    "print(\"\\nPreview of gene expression data (first few genes):\")\n",
    "print(preview_df(gene_data.head()))\n",
    "\n",
    "# 4. Check if the mapping was successful by looking at the presence of common gene symbols\n",
    "common_genes = ['TP53', 'BRCA1', 'TNF', 'IL6', 'APOE']\n",
    "found_genes = [gene for gene in common_genes if gene in gene_data.index]\n",
    "print(f\"\\nFound {len(found_genes)} out of {len(common_genes)} common genes in the mapped data\")\n",
    "if found_genes:\n",
    "    print(f\"Found genes: {found_genes}\")\n",
    "\n",
    "# Normalize gene symbols in index to ensure consistency\n",
    "print(\"\\nNormalizing gene symbols...\")\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"After normalization: {len(gene_data.index)} unique genes\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3c9466",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3720c396",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:27:36.077375Z",
     "iopub.status.busy": "2025-03-25T07:27:36.077256Z",
     "iopub.status.idle": "2025-03-25T07:27:41.516510Z",
     "shell.execute_reply": "2025-03-25T07:27:41.516127Z"
    }
   },
   "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/LDL_Cholesterol_Levels/gene_data/GSE181339.csv\n",
      "\n",
      "Extracting clinical data...\n",
      "Clinical data saved to ../../output/preprocess/LDL_Cholesterol_Levels/clinical_data/GSE181339.csv\n",
      "Clinical data shape: (3, 78)\n",
      "Trait information available: True\n",
      "\n",
      "Linking clinical and genetic data...\n",
      "Created linked data with 78 samples and 12286 features\n",
      "\n",
      "Handling missing values...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After handling missing values: 78 samples and 12286 features\n",
      "\n",
      "Evaluating feature bias...\n",
      "For the feature 'LDL_Cholesterol_Levels', the least common label is '0.0' with 30 occurrences. This represents 38.46% of the dataset.\n",
      "The distribution of the feature 'LDL_Cholesterol_Levels' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 8.0\n",
      "  50% (Median): 11.0\n",
      "  75%: 18.75\n",
      "Min: 1.0\n",
      "Max: 30.0\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "For the feature 'Gender', the least common label is '1.0' with 39 occurrences. This represents 50.00% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is fine.\n",
      "\n",
      "Trait bias determination: False\n",
      "Final linked data shape: 78 samples and 12286 features\n",
      "\n",
      "Performing final validation...\n",
      "\n",
      "Dataset usability for LDL_Cholesterol_Levels association studies: True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final linked data saved to ../../output/preprocess/LDL_Cholesterol_Levels/GSE181339.csv\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
}