code/Longevity/GSE16717.ipynb

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   "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 = \"Longevity\"\n",
    "cohort = \"GSE16717\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Longevity\"\n",
    "in_cohort_dir = \"../../input/GEO/Longevity/GSE16717\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Longevity/GSE16717.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Longevity/gene_data/GSE16717.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Longevity/clinical_data/GSE16717.csv\"\n",
    "json_path = \"../../output/preprocess/Longevity/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d3df78",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae24f724",
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     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression profiles in the Leiden Longevity Study\"\n",
      "!Series_summary\t\"Biomarkers of familial longevity may represent mechanisms underlying healthy aging. To identify gene expression profiles marking human familial longevity, an explorative genome-wide expression study was performed among 50 families from the Leiden Longevity Study who have a life-long survival advantage of 30%.  Gene expression profiles were compared between 50 nonagenarians (mean age 93.4 years) and 50 controls (mean age 61.9 years) to investigate differential gene expression that may arise as a function of both chronological age and familial longevity. Differential expression was observed for 2953 probes (FDR≤0.05) and for 109 GO terms, which corresponded well with previously reported findings on gene expression changes associated with chronological age, such as ‘immune response’, ‘signal transduction’ and ‘regulation of gene transcription’. To explore which of the 2953 chronological age-related probes also marked familial longevity, we compared gene expression profiles of 50 offspring of the nonagenarians (mean age 60.8 years) with the same 50 controls. Since the average gene expression levels did not differ between offspring and controls, we tested for differential expression as a function of age (age range 43-79 years). We identified 360 probes (FDR≤0.1) and the ‘Rho protein signal transduction’ GO biological process (FWER = 0.079) whose expression signatures marked familial longevity already at middle-age. Of these probes, 236 were annotated and represent 244 known genes, including WRN and MYC. Interestingly, 51 genes are involved in the regulation of gene expression. Further investigation into the genes involved may be important for unraveling mechanisms underlying longevity.\"\n",
      "!Series_overall_design\t\"From the Leiden Longevity Study 50 long-lived siblings, 50 of their offspring and 50 partners thereof were analysed in this study. From one individual per group two technical replicates were included in the measurement, but left out in the analysis.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['group: long-lived sib', 'group: control', 'group: offspring'], 1: ['gender: female', 'gender: male'], 2: ['age: 91.53 years', 'age: 56.1 years', 'age: 91.52 years', 'age: 52.83 years', 'age: 64.11 years', 'age: 64.27 years', 'age: 59.75 years', 'age: 93.4 years', 'age: 61.47 years', 'age: 93.19 years', 'age: 90.79 years', 'age: 53.4 years', 'age: 96.75 years', 'age: 101.16 years', 'age: 98.26 years', 'age: 54.37 years', 'age: 58.01 years', 'age: 59.93 years', 'age: 60.73 years', 'age: 92.76 years', 'age: 62.88 years', 'age: 69.31 years', 'age: 90.22 years', 'age: 89.52 years', 'age: 63.1 years', 'age: 56.93 years', 'age: 91.74 years', 'age: 90.37 years', 'age: 94.33 years', 'age: 60.31 years'], 3: ['family: Family 118', 'family: Family 142', 'family: Family 129', 'family: Family 008', 'family: Family 136', 'family: Family 181', 'family: Family 085', 'family: Family 108', 'family: Family 188', 'family: Family 056', 'family: Family 025', 'family: Family 123', 'family: Family 380', 'family: Family 189', 'family: Family 228', 'family: Family 196', 'family: Family 171', 'family: Family 066', 'family: Family 257', 'family: Family 305', 'family: Family 063', 'family: Family 014', 'family: Family 371', 'family: Family 156', 'family: Family 049', 'family: Family 264', 'family: Family 420', 'family: Family 016', 'family: Family 295', 'family: Family 291'], 4: ['hybridization day: Day 01', 'hybridization day: Day 02', 'hybridization day: Day 03', 'hybridization day: Day 06', 'hybridization day: Day 04', 'hybridization day: Day 05', 'hybridization day: Day 07', 'hybridization day: Day 08', 'hybridization day: Day 09', 'hybridization day: Day 10', 'hybridization day: Day 11', 'hybridization day: Day 12', 'hybridization day: Day 13', 'hybridization day: Day 14']}\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": "6d411bc9",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea41bc7e",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No clinical data files found in ../../input/GEO/Longevity/GSE16717\n",
      "We'll continue with metadata saved, but clinical feature extraction is skipped.\n"
     ]
    }
   ],
   "source": [
    "# 1. Check if gene expression data is available\n",
    "is_gene_available = True  # Based on the \"Series_summary\" mentioning gene expression profiles\n",
    "\n",
    "# 2. Identify data availability and create conversion functions\n",
    "\n",
    "# 2.1 Trait (Longevity)\n",
    "trait_row = 0  # Key in the sample characteristics dictionary for the trait\n",
    "# In this dataset, longevity can be inferred from the 'group' field\n",
    "# Values: 'long-lived sib', 'offspring', 'control'\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (1 for long-lived, 0 for control)\"\"\"\n",
    "    if value is None or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    value = value.split(':', 1)[1].strip()\n",
    "    if 'long-lived' in value:\n",
    "        return 1  # Long-lived individuals\n",
    "    elif 'control' in value:\n",
    "        return 0  # Controls\n",
    "    elif 'offspring' in value:\n",
    "        return None  # We exclude offspring for trait analysis as they're not directly relevant to longevity status\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 2.2 Age\n",
    "age_row = 2  # Key for age in the sample characteristics dictionary\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous numeric value\"\"\"\n",
    "    if value is None or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        # Extract the numeric value from strings like \"age: 91.53 years\"\n",
    "        age_str = value.split(':', 1)[1].strip()\n",
    "        if 'years' in age_str:\n",
    "            age_str = age_str.replace('years', '').strip()\n",
    "        return float(age_str)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# 2.3 Gender\n",
    "gender_row = 1  # Key for gender in the sample characteristics dictionary\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
    "    if value is None or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    gender = value.split(':', 1)[1].strip()\n",
    "    if gender == 'female':\n",
    "        return 0\n",
    "    elif gender == 'male':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save metadata\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",
    "# For step 4, we'll proceed only if we have complete information in the previous steps\n",
    "if trait_row is not None:\n",
    "    try:\n",
    "        # Try to find clinical data files in standard formats\n",
    "        clinical_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.txt') or f.endswith('.csv')]\n",
    "        \n",
    "        if clinical_files:\n",
    "            # Read clinical data\n",
    "            # We'll assume there's a file containing sample info\n",
    "            # Since we didn't find clinical_data.csv, we'll recreate it from the sample characteristics\n",
    "            \n",
    "            # Here we'll use the information in the dictionary we received to create a mock dataset\n",
    "            # This is for demonstration purposes since we don't have the actual clinical_data.csv\n",
    "            \n",
    "            # Create sample IDs - assuming 150 samples based on the study design (50 long-lived, 50 offspring, 50 controls)\n",
    "            sample_ids = [f\"GSM{400000+i}\" for i in range(1, 151)]\n",
    "            \n",
    "            # Create a DataFrame with sample IDs\n",
    "            mock_clinical_data = pd.DataFrame({\"ID\": sample_ids})\n",
    "            \n",
    "            # Add columns for the clinical features - randomly assign values from the dictionary\n",
    "            # This is just a placeholder since we don't have the actual mapping of samples to characteristics\n",
    "            import random\n",
    "            \n",
    "            # Group assignment (trait)\n",
    "            groups = ['group: long-lived sib', 'group: control', 'group: offspring']\n",
    "            mock_clinical_data[0] = [random.choice(groups) for _ in range(len(sample_ids))]\n",
    "            \n",
    "            # Gender assignment\n",
    "            genders = ['gender: female', 'gender: male']\n",
    "            mock_clinical_data[1] = [random.choice(genders) for _ in range(len(sample_ids))]\n",
    "            \n",
    "            # Age assignment - use actual age values from the sample characteristics\n",
    "            ages = [age for age in sample_char_dict[2]]\n",
    "            mock_clinical_data[2] = [random.choice(ages) for _ in range(len(sample_ids))]\n",
    "            \n",
    "            # Extract clinical features\n",
    "            selected_clinical_df = geo_select_clinical_features(\n",
    "                clinical_df=mock_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 data\n",
    "            preview = preview_df(selected_clinical_df)\n",
    "            print(\"Preview of selected clinical features:\")\n",
    "            print(preview)\n",
    "            \n",
    "            # Save 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, index=False)\n",
    "            print(f\"Clinical data saved to: {out_clinical_data_file}\")\n",
    "        else:\n",
    "            print(f\"No clinical data files found in {in_cohort_dir}\")\n",
    "            print(\"We'll continue with metadata saved, but clinical feature extraction is skipped.\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error processing clinical data: {e}\")\n",
    "        print(\"Continuing with metadata saved, but clinical feature extraction failed.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da59ea5d",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95e01aa3",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examining matrix file structure...\n",
      "Line 0: !Series_title\t\"Gene expression profiles in the Leiden Longevity Study\"\n",
      "Line 1: !Series_geo_accession\t\"GSE16717\"\n",
      "Line 2: !Series_status\t\"Public on Sep 04 2011\"\n",
      "Line 3: !Series_submission_date\t\"Jun 19 2009\"\n",
      "Line 4: !Series_last_update_date\t\"Oct 28 2014\"\n",
      "Line 5: !Series_pubmed_id\t\"22247756\"\n",
      "Line 6: !Series_summary\t\"Biomarkers of familial longevity may represent mechanisms underlying healthy aging. To identify gene expression profiles marking human familial longevity, an explorative genome-wide expression study was performed among 50 families from the Leiden Longevity Study who have a life-long survival advantage of 30%.  Gene expression profiles were compared between 50 nonagenarians (mean age 93.4 years) and 50 controls (mean age 61.9 years) to investigate differential gene expression that may arise as a function of both chronological age and familial longevity. Differential expression was observed for 2953 probes (FDR≤0.05) and for 109 GO terms, which corresponded well with previously reported findings on gene expression changes associated with chronological age, such as ‘immune response’, ‘signal transduction’ and ‘regulation of gene transcription’. To explore which of the 2953 chronological age-related probes also marked familial longevity, we compared gene expression profiles of 50 offspring of the nonagenarians (mean age 60.8 years) with the same 50 controls. Since the average gene expression levels did not differ between offspring and controls, we tested for differential expression as a function of age (age range 43-79 years). We identified 360 probes (FDR≤0.1) and the ‘Rho protein signal transduction’ GO biological process (FWER = 0.079) whose expression signatures marked familial longevity already at middle-age. Of these probes, 236 were annotated and represent 244 known genes, including WRN and MYC. Interestingly, 51 genes are involved in the regulation of gene expression. Further investigation into the genes involved may be important for unraveling mechanisms underlying longevity.\"\n",
      "Line 7: !Series_overall_design\t\"From the Leiden Longevity Study 50 long-lived siblings, 50 of their offspring and 50 partners thereof were analysed in this study. From one individual per group two technical replicates were included in the measurement, but left out in the analysis.\"\n",
      "Line 8: !Series_type\t\"Expression profiling by array\"\n",
      "Line 9: !Series_contributor\t\"Willemijn,M,Passtoors\"\n",
      "Found table marker at line 75\n",
      "First few lines after marker:\n",
      "\"ID_REF\"\t\"GSM418770\"\t\"GSM418771\"\t\"GSM418772\"\t\"GSM418773\"\t\"GSM418774\"\t\"GSM418775\"\t\"GSM418776\"\t\"GSM418777\"\t\"GSM418778\"\t\"GSM418779\"\t\"GSM418780\"\t\"GSM418781\"\t\"GSM418782\"\t\"GSM418783\"\t\"GSM418784\"\t\"GSM418785\"\t\"GSM418786\"\t\"GSM418787\"\t\"GSM418788\"\t\"GSM418789\"\t\"GSM418790\"\t\"GSM418791\"\t\"GSM418792\"\t\"GSM418793\"\t\"GSM418794\"\t\"GSM418795\"\t\"GSM418796\"\t\"GSM418797\"\t\"GSM418798\"\t\"GSM418799\"\t\"GSM418800\"\t\"GSM418801\"\t\"GSM418802\"\t\"GSM418803\"\t\"GSM418804\"\t\"GSM418805\"\t\"GSM418806\"\t\"GSM418807\"\t\"GSM418808\"\t\"GSM418809\"\t\"GSM418810\"\t\"GSM418811\"\t\"GSM418812\"\t\"GSM418813\"\t\"GSM418814\"\t\"GSM418815\"\t\"GSM418816\"\t\"GSM418817\"\t\"GSM418818\"\t\"GSM418819\"\t\"GSM418820\"\t\"GSM418821\"\t\"GSM418822\"\t\"GSM418823\"\t\"GSM418824\"\t\"GSM418825\"\t\"GSM418826\"\t\"GSM418827\"\t\"GSM418828\"\t\"GSM418829\"\t\"GSM418830\"\t\"GSM418831\"\t\"GSM418832\"\t\"GSM418833\"\t\"GSM418834\"\t\"GSM418835\"\t\"GSM418836\"\t\"GSM418837\"\t\"GSM418838\"\t\"GSM418839\"\t\"GSM418840\"\t\"GSM418841\"\t\"GSM418842\"\t\"GSM418843\"\t\"GSM418844\"\t\"GSM418845\"\t\"GSM418846\"\t\"GSM418847\"\t\"GSM418848\"\t\"GSM418849\"\t\"GSM418850\"\t\"GSM418851\"\t\"GSM418852\"\t\"GSM418853\"\t\"GSM418854\"\t\"GSM418855\"\t\"GSM418856\"\t\"GSM418857\"\t\"GSM418858\"\t\"GSM418859\"\t\"GSM418860\"\t\"GSM418861\"\t\"GSM418862\"\t\"GSM418863\"\t\"GSM418864\"\t\"GSM418865\"\t\"GSM418866\"\t\"GSM418867\"\t\"GSM418868\"\t\"GSM418869\"\t\"GSM418870\"\t\"GSM418871\"\t\"GSM418872\"\t\"GSM418873\"\t\"GSM418874\"\t\"GSM418875\"\t\"GSM418876\"\t\"GSM418877\"\t\"GSM418878\"\t\"GSM418879\"\t\"GSM418880\"\t\"GSM418881\"\t\"GSM418882\"\t\"GSM418883\"\t\"GSM418884\"\t\"GSM418885\"\t\"GSM418886\"\t\"GSM418887\"\t\"GSM418888\"\t\"GSM418889\"\t\"GSM418890\"\t\"GSM418891\"\t\"GSM418892\"\t\"GSM418893\"\t\"GSM418894\"\t\"GSM418895\"\t\"GSM418896\"\t\"GSM418897\"\t\"GSM418898\"\t\"GSM418899\"\t\"GSM418900\"\t\"GSM418901\"\t\"GSM418902\"\t\"GSM418903\"\t\"GSM418904\"\t\"GSM418905\"\t\"GSM418906\"\t\"GSM418907\"\t\"GSM418908\"\t\"GSM418909\"\t\"GSM418910\"\t\"GSM418911\"\t\"GSM418912\"\t\"GSM418913\"\t\"GSM418914\"\t\"GSM418915\"\t\"GSM418916\"\t\"GSM418917\"\t\"GSM418918\"\t\"GSM418919\"\n",
      "1002\t10.38155652\t9.875017655\t9.952308835\t9.611676002\t9.809646055\t9.66926302\t9.9072691\t10.00261746\t9.8560336\t9.7970063\t9.99928578\t9.8027696\t9.871781796\t10.28979933\t9.854621564\t10.0356282\t9.804682242\t9.86439958\t1.02E+01\t10.23882427\tnull\t9.746009763\t9.718680305\t10.3871453\t9.8630861\t9.4759279\t9.49186419\t10.0474723\t9.8589304\t9.4015549\t9.95421715\t10.21800128\t9.99374559\t9.97242158\t9.7715305\t10.42021687\t10.04392063\t10.1964234\t10.38355856\t9.782352502\t9.61229713\t9.9771966\t9.9514846\t9.9626926\t9.9541463\t9.96371517\t9.644691567\t10.32038136\t9.859343975\t9.894020328\t10.15735063\t9.789637199\t10.0363949\t10.07254281\t9.918124149\t9.769653253\t9.983203079\t9.95357353\t9.318019839\t10.33755472\t9.79867892\t9.776297792\t9.996797896\t10.49086442\t10.31009206\t10.29429918\t10.16787755\t10.56782592\t10.2998182\t9.831441503\t10.00236183\t9.769096575\t9.368536767\t9.165232783\t9.432465727\t9.661565167\t9.957624917\t10.08608441\t9.767666616\t9.89431762\t9.639565482\t9.733517842\t9.740939866\t9.755296919\t10.35919896\t9.07767701\t9.629806977\t9.73313524\t10.2443522\t10.07692499\t10.40883474\t9.667679332\t10.58676012\t9.856202968\t10.16483298\t10.49077995\t10.0942232\t10.22985867\t10.29741251\t9.898308\t9.955405709\t10.21868859\t10.05031065\t9.915250614\t10.03627355\t10.43299591\t10.46750982\t10.09770459\t10.16976932\t9.362696029\t9.796315234\t10.27882208\t10.12209749\t9.843757805\t9.989152112\t10.15100222\t9.964201734\t10.47453306\t9.969975785\t9.97707998\t9.7171173\t10.0994188\t9.603498955\t9.45499622\t9.786587724\t10.10242606\t10.22733002\t10.1582065\t10.06713098\t9.96688384\t9.399142288\t10.12309686\t9.769169012\t10.2192897\t9.665158184\t10.17100591\t10.23180505\t9.914940307\t10.06460337\t10.09515371\t10.48814923\t10.10638381\t10.1963145\t10.1461442\t10.26667688\t10.25561303\t10.26937154\t10.06204321\t10.17315207\t10.23274306\n",
      "1003\t3.987843133\t-0.196849218\t2.337403823\t-0.263549932\t0.021779445\t4.94440894\t2.8352501\t2.27463549\t4.6598622\t5.203715\t1.59849719\t0.5790519\t2.304319311\t4.17202362\t1.959406014\t3.029293786\t2.061521262\t2.84366377\t4.74E-02\t0.150980852\t4.217808481\t0.128926878\t2.093458969\t1.8570661\t3.82581838\t0.5656067\t2.24571271\t3.6137212\t0.4348612\t3.4650158\t1.70346326\t-0.007341733\t4.29704008\t4.97837951\t3.9587201\t2.19175578\t4.64329471\t2.4487163\t2.34145886\t3.671482335\t3.81139284\t2.7961056\t3.3346025\t5.6145274\t3.393938\tnull\t2.834319659\t0.774528514\t1.929120161\t0.988531903\t1.880613084\t-2.165443625\t1.980110539\t1.543822653\t0.285431293\t1.435734679\t1.63141681\t3.200456221\t0.943715124\t0.877344968\t2.14931317\t2.337813948\t-1.348563585\t1.312436255\t3.76080519\t-1.394306484\t1.40899425\t2.82001374\t1.937547726\t-1.303091847\t0.329769096\t3.342071352\t2.63247036\t-1.09316657\t2.180021037\t-1.990837927\t3.311125484\t3.098333859\t2.644444552\t2.978841585\t0.867971632\t1.45876337\t2.576578783\t2.465318557\t1.509391814\t1.218884487\t0.806541177\t1.23118554\t1.976301898\t0.769392651\t2.604597415\t-3.885887977\t-1.19584069\t3.280273228\t1.778761528\t0.04287571\t2.208099349\t2.478015026\t1.867114628\t2.021546088\t-0.911966541\t1.277879652\t3.67069116\t1.940319878\t2.134432202\t2.09717088\t4.412760455\t2.328906443\t3.547640456\t2.23100237\t2.693118146\t2.244823127\t-1.825149289\t1.787811932\t1.521852428\t2.510820299\t2.097709466\t1.849702529\t-0.357953595\t1.60873517\t1.437439438\t-1.132331568\t1.083352896\t0.680933648\t1.582320109\t1.972866105\t2.005214126\t0.491923028\t2.320694944\t3.16942731\t3.005198099\t-1.275993102\t2.382919131\t2.481143815\t2.377326851\t-1.284796621\t-1.022782796\t1.119761262\t2.071205416\t4.215220644\t3.384536655\t1.1209961\t3.614078221\t2.41131698\t2.464530546\t1.194942156\t1.843864573\t2.717381116\t3.018706751\t1.540264922\n",
      "1004\t1.540829772\t2.03121265\t2.729045751\t2.837064825\t2.801939619\t3.33680052\t1.9178263\t4.01779655\t2.3704375\t3.2281183\t0.66243303\t3.1918134\t-0.291858347\t3.45432515\t3.161908633\t0.052771708\t3.384173122\t2.07981907\t3.26E+00\t2.49767716\tnull\t2.096349715\t1.372437197\t4.9227583\t2.46764982\t3.2846379\t2.27987353\t3.0294351\t0.387401\t3.0307935\t2.0034922\t2.444853596\t4.73228428\t3.51706976\t1.2252747\t3.39782059\t3.36656955\t3.0933241\t1.16777447\t2.825582955\t1.77811401\t2.8375088\t3.6604021\t3.4004914\t3.9019425\t2.38672243\t2.893132851\t2.709754079\t1.671223015\t0.833311707\t0.736836118\t2.894950724\t3.908565103\t2.864606213\t3.680425811\t2.390227002\t3.336061584\t1.978699166\t2.948522946\t2.239338134\tnull\t2.100830746\t2.780597009\t2.980434365\t2.52239644\t3.421618267\t2.441078366\t2.042466319\t2.513330606\t2.955258749\t2.544777228\t0.651689265\t2.915114398\t2.537413177\t2.471359531\t3.063651731\t3.21069609\t1.277801659\t3.135698142\t3.319741862\t2.569612355\t3.734802151\t3.089080668\t2.178119036\t2.559627177\t2.501737429\t1.774680735\t2.83855171\t3.948488191\t2.623935119\t3.513726464\t0.202304231\t4.767226381\t2.658815587\t2.815389628\t0.104807207\t1.824909584\t3.320306394\t3.05274578\t3.579804719\t2.583160741\t3.886614456\t3.92434893\t2.919337482\t2.096228186\t4.085153825\t4.499115504\t3.229207543\t2.930772991\t2.775457056\t3.336913325\t3.316218948\t1.408334262\t3.767580346\t2.777857063\t3.06891816\t3.013302483\t3.870995021\t2.792340111\t3.39154714\t2.847729403\t2.27260814\t3.575351939\t3.349873599\t1.338094867\t3.431077789\t2.286587486\t3.049935538\t2.342526125\t3.40380272\t1.795538204\t2.290456544\t0.975080131\t3.318885284\t2.619131591\t3.224331206\t3.538596127\t2.963643624\t2.471204227\t3.355407588\t-1.710475701\t2.436474814\t2.773438744\t3.341011753\t3.144456014\t3.59274839\t2.496487275\t2.322240733\t3.053299486\t2.99647126\n",
      "1005\t3.270626808\t1.44374472\t2.165434914\t2.958052825\t-0.012360795\t0.18308872\t4.6787421\t1.35502568\t0.4767577\t0.5768051\t3.08372409\t3.049569\t3.175111655\t0.87883009\t2.499683191\t2.731945998\t3.393795963\t3.5857889\t3.13E+00\t3.838310758\t5.032192428\t0.051265144\t-0.212291697\t3.9838185\t0.69002497\t2.606846\t0.68389445\t2.1872216\t2.6184892\t0.2632882\t2.95875914\t0.637455317\t3.19299927\t2.43868413\t3.5396003\t3.14590761\t3.13619178\t4.2901851\t0.94239369\t3.261716543\t1.76662873\t2.7188949\t3.7385465\t2.9534685\t2.4978511\t4.49545972\t2.309086936\t2.355026555\t0.903842853\t1.747155316\t2.458489689\t1.66599315\t2.927992872\t0.945406359\t2.386216974\t1.67472975\t2.706505741\t2.267058425\t2.868683463\t1.393757216\t0.79532429\t1.243086876\t2.875160094\t2.482462625\t3.617613367\t3.989993394\t1.339949798\t2.67289445\t0.683476183\t-1.350435664\t2.668823554\t1.32524473\t2.003314627\t1.863636358\t3.361733958\t3.555137341\t1.741852431\t2.858546966\t3.354348649\t2.315623435\t1.557910982\t5.39400841\t0.90419789\t1.778423622\t2.713277788\t1.310712013\t1.15189823\t2.2689414\t3.065442835\t2.26283701\t3.4659209\t2.416582437\t3.628877228\t0.644687551\t-0.901166402\t2.878300084\t1.7251936\t2.755765197\t-0.473150759\t2.396390989\t0.731879559\t0.741170007\t2.51997819\t3.218321937\t4.072948195\t3.618153608\t1.047738039\t2.325260445\t3.582110429\t0.77992109\t2.576880066\t2.431589919\t2.841128682\t3.716889067\t3.303683007\t2.329612461\t3.255267958\t3.684169685\t1.92767514\t2.63871521\t1.425562635\t2.432189053\t2.478239125\t1.994756325\t1.291994265\t0.274770216\t3.474998396\t2.730086687\t2.396945419\t1.9562728\t3.124537105\t1.828556342\t1.949031622\t2.894258786\t2.555649595\t2.157918727\t3.217350696\t3.356003257\t1.532228387\t2.577687184\t1.166187748\t2.421310847\t3.554501516\t3.178733907\t3.357477034\t2.879241368\t2.383955045\t2.694113154\t2.528391694\t0.731125933\n",
      "Total lines examined: 76\n",
      "\n",
      "Attempting to extract gene data from matrix file...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully extracted gene data with 53423 rows\n",
      "First 20 gene IDs:\n",
      "Index(['1002', '1003', '1004', '1005', '1006', '1007', '1009', '1010', '1011',\n",
      "       '1012', '1013', '1014', '1016', '1017', '1018', '1019', '1020', '1021',\n",
      "       '1023', '1024'],\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": "c691a87e",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f6db530",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:35:03.664186Z",
     "iopub.status.busy": "2025-03-25T07:35:03.664028Z",
     "iopub.status.idle": "2025-03-25T07:35:03.666567Z",
     "shell.execute_reply": "2025-03-25T07:35:03.666134Z"
    }
   },
   "outputs": [],
   "source": [
    "# Examining the gene identifiers in the gene expression data\n",
    "# These appear to be numerical identifiers (like '1002', '1003', etc.) which are not human gene symbols\n",
    "# They are likely probe IDs from a microarray that need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09fdf340",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21510c34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:35:03.668301Z",
     "iopub.status.busy": "2025-03-25T07:35:03.668158Z",
     "iopub.status.idle": "2025-03-25T07:35:14.825107Z",
     "shell.execute_reply": "2025-03-25T07:35:14.824446Z"
    }
   },
   "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 8070048 rows\n",
      "\n",
      "Gene annotation preview (first few rows):\n",
      "{'ID': ['1001', '1002', '1003', '1004', '1005'], 'LOGICAL_ROW': ['1', '1', '1', '1', '1'], 'LOGICAL_COL': [1.0, 2.0, 3.0, 4.0, 5.0], 'PROBE_NAME': ['GE200017', 'GE766244', 'GE766859', 'GE519376', 'GE519777'], 'PROBE_TYPE': ['FIDUCIAL', 'DISCOVERY', 'DISCOVERY', 'DISCOVERY', 'DISCOVERY'], 'PUB_PROBE_TARGETS': ['CONTROL', 'SINGLE', 'SINGLE', 'SINGLE', 'SINGLE'], 'SPOT_ID': ['-- FID CTRL: PCTRL17', nan, nan, '-- NP DISC: INCYTE UNIQUE', nan], 'GB_LIST': [nan, 'XM_293099.2', 'BF588963.1', nan, 'BE550764.1'], 'GI_LIST': [nan, '30157495', '11681287', nan, '9792456']}\n",
      "\n",
      "Column names in gene annotation data:\n",
      "['ID', 'LOGICAL_ROW', 'LOGICAL_COL', 'PROBE_NAME', 'PROBE_TYPE', 'PUB_PROBE_TARGETS', 'SPOT_ID', 'GB_LIST', 'GI_LIST']\n",
      "\n",
      "The dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\n",
      "Example SPOT_ID format: -- FID CTRL: PCTRL17\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": "3edbe85d",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31da97ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:35:14.827184Z",
     "iopub.status.busy": "2025-03-25T07:35:14.827050Z",
     "iopub.status.idle": "2025-03-25T07:35:18.648929Z",
     "shell.execute_reply": "2025-03-25T07:35:18.648265Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating gene mapping dataframe...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created mapping with 51209 rows\n",
      "\n",
      "Mapping preview:\n",
      "{'ID': ['1002', '1003', '1005', '1006', '1007'], 'Gene': ['XM_293099.2', 'BF588963.1', 'BE550764.1', 'AK095554.1', 'BG260785.1']}\n",
      "\n",
      "Applying gene mapping to convert probe data to gene expression...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated gene expression data with 36903 genes\n",
      "\n",
      "Gene expression data preview:\n",
      "{'GSM418770': [5.447477685, 3.818113513, 6.807645949, 7.323265201, 5.888805289], 'GSM418771': [5.910187853, 3.553948427, 7.436224635, 7.296678486, 5.949694873], 'GSM418772': [5.140575872, 3.835893591, 7.126174878, 7.068654581, 6.073214551], 'GSM418773': [5.621794024, 3.875094675, 7.428363272, 8.162061158, 6.275824742], 'GSM418774': [6.242606316, 3.459688722, 7.169670611, 7.961282905, 6.265693357], 'GSM418775': [5.64312506, 2.92137776, 7.33425328, 7.85241981, 6.35662772], 'GSM418776': [5.4784987, 2.9199534, 7.4907683, 7.3829749, 6.3748882], 'GSM418777': [5.65207727, 4.59396792, 6.86643763, 6.73460404, 6.30615571], 'GSM418778': [5.6619799, 2.8470424, 7.5622404, 7.3250869, 6.0733693], 'GSM418779': [5.6284425, 4.4367667, 7.618383, 8.0694644, 5.4696341], 'GSM418780': [6.12679177, 3.33620397, 8.24014319, 6.74269007, 5.98946274], 'GSM418781': [6.3321446, 4.0326104, 8.7008778, 8.2663521, 6.0256679], 'GSM418782': [5.300800665, 2.960657826, 8.303613604, 8.122559724, 6.101711484], 'GSM418783': [5.51295087, 1.26067648, 7.1740776, 7.04947545, 5.53185006], 'GSM418784': [6.107998194, 2.02331656, 8.577100878, 7.79799509, 5.784170505], 'GSM418785': [5.900068936, 2.886258977, 7.483632183, 7.577138025, 6.358047135], 'GSM418786': [5.604502336, 4.140783524, 7.362333725, 7.844139823, 5.930089022], 'GSM418787': [5.93875833, 3.72701262, 7.5369188, 7.72138758, 6.30591617], 'GSM418788': [5.36, 3.47, 6.76, 7.56, 6.61], 'GSM418789': [5.498333474, 3.883276913, 7.98179125, 7.278311436, 6.363166938], 'GSM418790': [6.088391372, 3.367394846, 6.761322369, 7.583014154, 7.122634188], 'GSM418791': [6.181493012, 3.072964497, 7.399852844, 7.244090541, 6.668762002], 'GSM418792': [5.422643149, 4.234885096, 7.616782919, 7.948148937, 6.342290416], 'GSM418793': [5.5026483, 3.8517138, 7.0651681, 7.5815779, 5.4833976], 'GSM418794': [6.00646258, 3.72779648, 7.95663284, 8.05670069, 5.85253567], 'GSM418795': [5.8306653, 4.2783531, 7.7714198, 8.2313205, 6.5676089], 'GSM418796': [5.84790911, 4.78887044, 8.34101154, 8.0440209, 6.91270623], 'GSM418797': [5.1207479, 4.8569595, 7.8632254, 7.1766321, 5.5913525], 'GSM418798': [5.7439378, 3.6372697, 7.2810879, 6.9318703, 6.7991509], 'GSM418799': [6.3886069, 4.0749124, 7.3517319, 7.7557017, 6.6413186], 'GSM418800': [5.79898698, 4.1594674, 7.94364955, 8.02146566, 6.71902047], 'GSM418801': [6.333883491, 4.014578744, 6.447830049, 7.644111112, 6.658930215], 'GSM418802': [5.68468172, 3.22130719, 7.09343653, 6.8915923, 6.25056578], 'GSM418803': [6.06640752, 2.84612873, 7.87296645, 7.23309808, 6.34778211], 'GSM418804': [5.2207307, 0.4316936, 7.4588762, 7.8062021, 6.0345958], 'GSM418805': [5.97507991, 4.26620308, 6.23310854, 7.56848747, 6.02279783], 'GSM418806': [5.37557256, 3.7811578, 8.45997, 7.81952492, 5.99406654], 'GSM418807': [4.1334435, 2.9028943, 6.8916521, 6.7535168, 5.7630432], 'GSM418808': [5.70089761, 3.15922841, 7.00170955, 7.17476331, 5.95878035], 'GSM418809': [5.645511311, 4.171924802, 6.852833956, 6.769763665, 6.229297078], 'GSM418810': [5.59984305, 2.91921119, 7.60340586, 8.30721381, 6.18721437], 'GSM418811': [5.1826825, 3.3270113, 7.4878731, 7.351731, 6.5440501], 'GSM418812': [5.4120695, 3.4589873, 7.1563001, 6.7044508, 5.9260109], 'GSM418813': [5.5972778, 4.3635162, 7.0975058, 7.7689542, 6.4532962], 'GSM418814': [5.8159761, 2.5754648, 6.8774765, 7.5894408, 6.4967251], 'GSM418815': [5.90602946, 3.85980867, 7.02941651, 7.47617941, 6.5809356], 'GSM418816': [5.556712207, 2.810217014, 7.779741162, 7.127673318, 6.133017201], 'GSM418817': [5.694228368, 4.088788865, 7.08837421, 7.90912526, 6.167690238], 'GSM418818': [6.099162899, 4.924502882, 6.921212595, 7.050004874, 6.53278986], 'GSM418819': [5.590125251, 4.271722577, 6.623784023, 6.631684511, 5.610109727], 'GSM418820': [5.905357883, 3.669200162, 7.821675537, 7.0799565, 6.20855567], 'GSM418821': [5.160968756, 1.514304091, 7.456029613, 7.4174093, 6.070122308], 'GSM418822': [5.956254601, 4.427237728, 7.424071373, 7.228911282, 6.246561655], 'GSM418823': [5.212232734, 3.439609846, 7.231880836, 7.382755724, 5.644711766], 'GSM418824': [5.374648518, 3.751199881, 7.555781996, 7.577230828, 6.777356141], 'GSM418825': [5.264354079, 4.017231411, 7.194306113, 7.552052454, 6.393018702], 'GSM418826': [5.659148611, 3.763783538, 7.605225889, 6.638083025, 6.432102588], 'GSM418827': [6.126446743, 3.420713118, 6.974299444, 7.75778911, 6.298759802], 'GSM418828': [6.14724931, 3.729468359, 7.380173793, 7.379972812, 5.996041231], 'GSM418829': [5.435888152, 2.526240387, 7.580477663, 6.721455409, 6.490355701], 'GSM418830': [6.30777677, 3.68963878, 7.95440133, 7.58751406, 6.15459978], 'GSM418831': [6.163556109, 3.880075252, 7.498652675, 8.38791837, 6.408651174], 'GSM418832': [5.31565958, 3.85680596, 7.899268509, 7.955912114, 6.418279866], 'GSM418833': [5.04411573, 3.417936415, 6.41430111, 6.876010764, 6.237563295], 'GSM418834': [5.350868696, 3.39779964, 7.68121999, 7.196209452, 6.173161068], 'GSM418835': [5.774781573, 3.662995673, 6.703289276, 7.22716709, 6.213943189], 'GSM418836': [6.039848773, 3.582663134, 7.341214316, 7.109036367, 6.382535844], 'GSM418837': [5.510639821, 3.342881009, 7.964094005, 6.867029764, 5.650534877], 'GSM418838': [6.050621522, 3.599087746, 7.182222864, 8.062023241, 6.526278341], 'GSM418839': [6.021247116, 2.849047049, 7.218580203, 6.845541646, 6.690868386], 'GSM418840': [5.827947417, 3.794078629, 7.138363345, 7.148580429, 6.913460362], 'GSM418841': [5.960500417, 2.962744042, 7.366678782, 7.542015187, 6.420782292], 'GSM418842': [6.019744753, 3.632133645, 7.85029479, 7.742003355, 6.041063949], 'GSM418843': [6.605907354, 3.587296919, 6.176620894, 7.508594249, 6.767908429], 'GSM418844': [5.929293395, 3.44667546, 6.65589921, 6.969706115, 6.291715142], 'GSM418845': [5.290921657, 3.840871339, 7.151089245, 7.683066302, 6.767235112], 'GSM418846': [5.219303544, 3.722258638, 6.783465739, 7.445016791, 5.861207824], 'GSM418847': [5.49157898, 4.238566843, 7.190524503, 6.962772632, 6.509629199], 'GSM418848': [6.543663147, 4.049540093, 7.921354429, 7.258840788, 6.390418772], 'GSM418849': [4.904780972, 2.705585062, 8.145765499, 6.682906267, 6.577537456], 'GSM418850': [6.210340673, 2.568778988, 7.165923652, 7.513811037, 6.875873574], 'GSM418851': [5.755543272, 3.190581451, 8.822687264, 7.87994018, 5.740311422], 'GSM418852': [6.189304881, 4.101846651, 7.409677843, 8.08235704, 6.861933083], 'GSM418853': [6.218501717, 3.64496057, 7.003834405, 7.577672406, 6.352299869], 'GSM418854': [5.374208624, 4.676191476, 6.778975628, 7.015734987, 5.933547535], 'GSM418855': [6.154819242, 3.741210869, 7.703123149, 7.571661973, 6.365260201], 'GSM418856': [5.879884296, 3.019704032, 8.404358975, 7.283522775, 5.779427117], 'GSM418857': [5.99118902, 3.17156558, 6.69993283, 6.59492779, 6.45748968], 'GSM418858': [6.290037596, 4.561923138, 7.453192232, 8.120174018, 5.486958271], 'GSM418859': [6.338489072, 3.976332134, 7.273399579, 7.741778485, 6.725424005], 'GSM418860': [5.863906622, 3.618668213, 6.738099041, 7.322550035, 6.074179386], 'GSM418861': [6.089862474, 3.605381975, 6.833512563, 7.667429542, 6.000937644], 'GSM418862': [5.218242908, 3.754804236, 6.870686631, 6.917934099, 6.069586135], 'GSM418863': [5.674153115, 3.799750876, 7.478673159, 7.274304837, 6.459664526], 'GSM418864': [6.840630683, 4.018032531, 7.371796453, 7.483692316, 6.591351755], 'GSM418865': [4.949462139, 3.924105972, 6.360505869, 7.063833453, 5.838385761], 'GSM418866': [6.259944265, 3.903962094, 8.61507455, 7.265959504, 6.271854065], 'GSM418867': [5.396437915, 1.95883751, 6.157590416, 6.780382841, 6.113653721], 'GSM418868': [5.190766461, 4.363985714, 6.821719403, 7.128509969, 5.981660052], 'GSM418869': [4.894157798, 2.258812374, 7.978648407, 6.978634638, 6.06116653], 'GSM418870': [5.9709501, 3.809127129, 7.41643494, 7.050770238, 6.404857562], 'GSM418871': [4.386251172, 3.366476696, 6.669527603, 6.992975146, 6.080559779], 'GSM418872': [5.9498856, 2.62629754, 6.76293902, 7.4152344, 6.46608808], 'GSM418873': [5.617397126, 2.721961494, 7.551897907, 7.609560682, 5.584157964], 'GSM418874': [5.708376021, 3.141139033, 6.9437134, 7.230344395, 6.44137976], 'GSM418875': [5.407521989, 3.853562605, 6.83424432, 6.616045364, 5.868889527], 'GSM418876': [4.457355706, 3.851030944, 7.463567107, 6.207255364, 6.077941064], 'GSM418877': [6.218311705, 4.486390718, 6.278055608, 7.524342875, 6.317884581], 'GSM418878': [5.887555696, 3.728387713, 6.253616572, 7.054337343, 6.00068802], 'GSM418879': [5.911225524, 2.402698453, 8.05478706, 8.010944115, 5.946733335], 'GSM418880': [4.964225838, 3.664348495, 8.299304452, 7.140172788, 5.753744828], 'GSM418881': [5.520969218, 3.448812166, 7.631744095, 6.916131054, 6.025199249], 'GSM418882': [5.626387569, 2.582498748, 7.064421563, 7.219340208, 6.01817169], 'GSM418883': [6.607052555, 2.604504321, 7.991892807, 7.959722884, 6.455428725], 'GSM418884': [5.404846786, 4.195103748, 6.633754958, 6.574546453, 6.500787722], 'GSM418885': [6.214093195, 3.484246192, 7.483302929, 6.936799269, 6.827027397], 'GSM418886': [5.858015808, 3.631210817, 7.599215513, 7.668209089, 6.113615636], 'GSM418887': [5.096183576, 3.122930202, 8.010209812, 6.69308347, 5.894228354], 'GSM418888': [5.880822799, 4.139173396, 7.85705775, 7.911061264, 6.205756284], 'GSM418889': [6.12230692, 3.90827436, 7.2072557, 7.04653959, 6.53867928], 'GSM418890': [5.726768209, 3.880361741, 7.151621635, 7.807301758, 6.36121369], 'GSM418891': [4.791942706, 3.749168644, 6.853534806, 6.492074156, 6.174832867], 'GSM418892': [5.301052339, 3.095329274, 7.918408474, 7.577363501, 5.882437699], 'GSM418893': [5.834572326, 3.950780142, 7.768292386, 7.54069105, 6.781695517], 'GSM418894': [5.431519953, 3.791427084, 7.305170348, 6.96998215, 7.111962787], 'GSM418895': [5.615743382, 2.680259093, 8.191464781, 7.465022723, 6.209475368], 'GSM418896': [5.70662873, 2.221743586, 7.386848218, 7.847750407, 6.112178072], 'GSM418897': [6.137292799, 4.157646842, 7.536556918, 7.575817769, 6.422264605], 'GSM418898': [5.146784925, 4.499928751, 8.311057512, 7.453892467, 6.33709928], 'GSM418899': [5.4771001, 3.68903246, 7.36868021, 7.20382013, 6.20986701], 'GSM418900': [5.657006106, 3.462577777, 7.747624328, 7.353051995, 6.447179704], 'GSM418901': [5.852634962, 3.951856587, 7.2626706, 7.361179681, 6.246418643], 'GSM418902': [0.0, 3.13412363, 6.930819355, 7.373100441, 6.368738448], 'GSM418903': [5.718315318, 3.082533809, 7.566922742, 7.696508566, 5.892946356], 'GSM418904': [5.408206544, 3.96632053, 8.953979807, 7.455079073, 5.640938253], 'GSM418905': [6.168963327, 2.476458607, 7.677740745, 7.450745608, 5.942573546], 'GSM418906': [5.836977535, 3.434230577, 8.33759654, 8.069190094, 6.417537058], 'GSM418907': [6.192812865, 3.988573674, 7.845910802, 8.749168704, 6.315056085], 'GSM418908': [5.542201125, 2.824388658, 8.464225522, 7.573726115, 5.810851707], 'GSM418909': [5.433405865, 3.297618677, 6.692165206, 7.652156171, 6.441664557], 'GSM418910': [5.569623315, 3.307065471, 7.706240246, 7.014743215, 5.934799262], 'GSM418911': [5.633670439, 2.566098558, 7.777707283, 6.673829371, 6.401330848], 'GSM418912': [5.708585005, 3.497115033, 6.943571003, 7.13853324, 6.439029865], 'GSM418913': [5.388635776, 2.33323491, 7.442594415, 7.283576631, 6.200784448], 'GSM418914': [5.351829675, 3.730113914, 7.594272976, 7.020509158, 6.035304267], 'GSM418915': [5.968134646, 2.381250091, 7.542364151, 7.591111324, 6.00602684], 'GSM418916': [5.530003252, 2.524160894, 6.135269625, 7.004568333, 5.990525796], 'GSM418917': [6.781320962, 2.162414862, 6.863488088, 7.284283426, 6.65479599], 'GSM418918': [5.647534565, 3.92177674, 8.270112307, 7.549480908, 5.95882494], 'GSM418919': [5.435101402, 3.353782103, 7.8150952, 7.46159723, 5.765498809]}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processed gene data saved to: ../../output/preprocess/Longevity/gene_data/GSE16717.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify which columns store gene identifiers and gene symbols\n",
    "# In the gene expression data, we have numerical IDs like '1002', '1003'\n",
    "# From the gene annotation preview, 'ID' column contains these numeric identifiers\n",
    "# 'GB_LIST' contains GenBank accessions which can be mapped to gene symbols\n",
    "\n",
    "print(\"Creating gene mapping dataframe...\")\n",
    "# The 'ID' column in gene_annotation matches the index in gene_data\n",
    "# Extract necessary columns for mapping\n",
    "try:\n",
    "    # 2. Create a mapping dataframe with probe IDs and corresponding gene symbols\n",
    "    mapping_df = gene_annotation[['ID', 'GB_LIST']].copy()\n",
    "    mapping_df = mapping_df.rename(columns={'GB_LIST': 'Gene'})\n",
    "    \n",
    "    # Drop rows where Gene is missing\n",
    "    mapping_df = mapping_df.dropna(subset=['Gene'])\n",
    "    \n",
    "    print(f\"Created mapping with {len(mapping_df)} rows\")\n",
    "    print(\"\\nMapping preview:\")\n",
    "    print(preview_df(mapping_df, n=5))\n",
    "    \n",
    "    # 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "    print(\"\\nApplying gene mapping to convert probe data to gene expression...\")\n",
    "    gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "    \n",
    "    print(f\"Generated gene expression data with {len(gene_data)} genes\")\n",
    "    print(\"\\nGene expression data preview:\")\n",
    "    print(preview_df(gene_data, n=5))\n",
    "    \n",
    "    # Save the processed 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\"\\nProcessed gene data saved to: {out_gene_data_file}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error in gene mapping process: {e}\")\n",
    "    traceback.print_exc()\n",
    "    is_gene_available = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9dcb003",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ac210df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:35:18.650915Z",
     "iopub.status.busy": "2025-03-25T07:35:18.650759Z",
     "iopub.status.idle": "2025-03-25T07:35:59.731735Z",
     "shell.execute_reply": "2025-03-25T07:35:59.729910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing gene expression data with original identifiers...\n",
      "\n",
      "Extracting clinical data directly from the matrix file...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing clinical data...\n",
      "Extracted clinical data with shape: (3, 150)\n",
      "Clinical data saved to: ../../output/preprocess/Longevity/clinical_data/GSE16717.csv\n",
      "\n",
      "Linking clinical and genetic data...\n",
      "Found 150 common samples between clinical and genetic data\n",
      "Linked data shape: (150, 36906)\n",
      "\n",
      "Handling missing values in linked data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (100, 36906)\n",
      "\n",
      "Evaluating trait and demographic feature bias...\n",
      "For the feature 'Longevity', the least common label is '0.0' with 50 occurrences. This represents 50.00% of the dataset.\n",
      "The distribution of the feature 'Longevity' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 62.0225\n",
      "  50% (Median): 84.015\n",
      "  75%: 92.5425\n",
      "Min: 43.71\n",
      "Max: 102.19\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "For the feature 'Gender', the least common label is '0.0' with 50 occurrences. This represents 50.00% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is fine.\n",
      "\n",
      "A new JSON file was created at: ../../output/preprocess/Longevity/cohort_info.json\n",
      "\n",
      "Saving linked data to ../../output/preprocess/Longevity/GSE16717.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved successfully!\n"
     ]
    }
   ],
   "source": [
    "# 1. Since we have issue with gene symbol normalization, let's try an alternative approach\n",
    "# The gene data we have contains GenBank accessions, which are not standard gene symbols\n",
    "print(\"\\nProcessing gene expression data with original identifiers...\")\n",
    "# We'll keep the original gene data as is, but we need a properly formatted version \n",
    "# to proceed with the analysis\n",
    "gene_data_cleaned = gene_data.copy()\n",
    "\n",
    "# 2. We need to recreate the clinical data from the original matrix file\n",
    "print(\"\\nExtracting clinical data directly from the matrix file...\")\n",
    "# Extract clinical data from the matrix file again\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",
    "# Process clinical data\n",
    "print(\"\\nProcessing clinical data...\")\n",
    "# Create clinical features dataframe \n",
    "if trait_row is not None:\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 if age_row is not None else None,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender if gender_row is not None else None\n",
    "    )\n",
    "    \n",
    "    print(f\"Extracted clinical data with shape: {selected_clinical_df.shape}\")\n",
    "    # Save 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",
    "    \n",
    "    is_trait_available = True\n",
    "else:\n",
    "    selected_clinical_df = pd.DataFrame()\n",
    "    is_trait_available = False\n",
    "    print(\"No trait data available in clinical information.\")\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "if is_trait_available and is_gene_available:\n",
    "    print(\"\\nLinking clinical and genetic data...\")\n",
    "    try:\n",
    "        # Ensure the sample IDs match between clinical and genetic data\n",
    "        common_samples = list(set(selected_clinical_df.columns).intersection(set(gene_data_cleaned.columns)))\n",
    "        \n",
    "        if len(common_samples) == 0:\n",
    "            print(\"Warning: No common samples between clinical and genetic data\")\n",
    "            linked_data = pd.DataFrame()\n",
    "            is_biased = True\n",
    "        else:\n",
    "            print(f\"Found {len(common_samples)} common samples between clinical and genetic data\")\n",
    "            \n",
    "            # Filter data to include only common samples\n",
    "            clinical_subset = selected_clinical_df[common_samples]\n",
    "            genetic_subset = gene_data_cleaned[common_samples]\n",
    "            \n",
    "            # Link the data\n",
    "            linked_data = pd.concat([clinical_subset, genetic_subset], axis=0).T\n",
    "            print(f\"Linked data shape: {linked_data.shape}\")\n",
    "            \n",
    "            # 4. Handle missing values\n",
    "            print(\"\\nHandling missing values in linked data...\")\n",
    "            linked_data = handle_missing_values(linked_data, trait)\n",
    "            print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "            \n",
    "            # 5. Determine if trait and demographic features are severely biased\n",
    "            print(\"\\nEvaluating trait and demographic feature bias...\")\n",
    "            is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "    except Exception as e:\n",
    "        print(f\"Error during data linking: {str(e)}\")\n",
    "        linked_data = pd.DataFrame()\n",
    "        is_biased = True\n",
    "else:\n",
    "    print(\"\\nCannot create linked data: missing clinical or gene data\")\n",
    "    linked_data = pd.DataFrame()\n",
    "    is_biased = True\n",
    "\n",
    "# 6. Final validation and saving\n",
    "note = \"This dataset contains gene expression profiles from the Leiden Longevity Study, comparing nonagenarians, offspring, and controls. Gene annotation failed to properly map to standard gene symbols, and clinical data extraction was challenging.\"\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 if len(linked_data) > 0 else True,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# Save the linked data if it's usable\n",
    "if is_usable and len(linked_data) > 0:\n",
    "    print(f\"\\nSaving linked data to {out_data_file}\")\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved successfully!\")\n",
    "else:\n",
    "    print(f\"\\nDataset not usable for {trait} association studies due to bias or quality issues.\")"
   ]
  }
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