{
 "cells": [
  {
   "cell_type": "code",
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   "id": "f82d79eb",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T08:08:43.320781Z"
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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 = \"Metabolic_Rate\"\n",
    "cohort = \"GSE89231\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Metabolic_Rate\"\n",
    "in_cohort_dir = \"../../input/GEO/Metabolic_Rate/GSE89231\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Metabolic_Rate/GSE89231.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Metabolic_Rate/gene_data/GSE89231.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Metabolic_Rate/clinical_data/GSE89231.csv\"\n",
    "json_path = \"../../output/preprocess/Metabolic_Rate/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc0e8f6a",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ae94b63",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T08:08:43.507746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files in the directory:\n",
      "['GSE89231_family.soft.gz', 'GSE89231_series_matrix.txt.gz']\n",
      "SOFT file: ../../input/GEO/Metabolic_Rate/GSE89231/GSE89231_family.soft.gz\n",
      "Matrix file: ../../input/GEO/Metabolic_Rate/GSE89231/GSE89231_series_matrix.txt.gz\n",
      "Background Information:\n",
      "!Series_title\t\"Doxorubicin response in diffuse large B-cell lymphoma cell lines varies with concentration, exposure duration, and level of intrinsic sensitivity\"\n",
      "!Series_summary\t\"Although diffuse large B-cell lymphoma (DLBCL) is a very heterogeneous disease, patients are as standard treated with a combination of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP). Since approximately 40% of patients die due to refractory disease or relapse, enhanced knowledge about drug response mechanisms is required to improve treatment outcome. Therefore, this study assesses parameters that possibly influence doxorubicin response. Doxorubicin-induced impact on the number of living cells was evaluated for four human DLBCL cell lines, illustrating differences in intrinsic sensitivity levels. Six cell lines were subjected to gene expression profiling upon exposure to two distinct drug concentrations (0.00061 μg/mL and 2.5 μg/mL) for 2, 12, and 48 hours. Variation in gene expression compared to baseline was determined with a mixed-effects model, and gene ontology enrichment analysis was performed using the webtools GOrilla and REVIGO. Only few genes were differentially expressed after short exposure and/or exposure to the low concentration, suggesting lack of drug efficacy under these conditions. In contrast, 12-hour exposure to the high concentration induced several changes. In sensitive cell lines, doxorubicin affected the expression of genes involved in ncRNA metabolism, DNA repair, and cell cycle process mechanisms. In resistant cell lines, the expression of genes implicated in metabolic processes were altered. Thus, we observed a differential response rate to doxorubicin in distinct DLBCL cell lines and demonstrated that doxorubicin-induced alterations in gene expression and resulting ontologies vary with drug concentration, exposure duration, and intrinsic sensitivity level.\"\n",
      "!Series_overall_design\t\"Global gene expression data of DLBCL cell lines untreated or after 2, 12, and 48 hours of exposure to two distinct concentrations (0.00061 μg/mL and 2.5 μg/mL) of doxorubicin.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['cell line: FARAGE', 'cell line: NU-DHL-1', 'cell line: OCI-Ly7', 'cell line: RIVA', 'cell line: SU-DHL-5', 'cell line: U2932']}\n"
     ]
    }
   ],
   "source": [
    "# 1. Check what files are actually in the directory\n",
    "import os\n",
    "print(\"Files in the directory:\")\n",
    "files = os.listdir(in_cohort_dir)\n",
    "print(files)\n",
    "\n",
    "# 2. Find appropriate files with more flexible pattern matching\n",
    "soft_file = None\n",
    "matrix_file = None\n",
    "\n",
    "for file in files:\n",
    "    file_path = os.path.join(in_cohort_dir, file)\n",
    "    # Look for files that might contain SOFT or matrix data with various possible extensions\n",
    "    if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n",
    "        soft_file = file_path\n",
    "    if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n",
    "        matrix_file = file_path\n",
    "\n",
    "if not soft_file:\n",
    "    print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if gz_files:\n",
    "        soft_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "if not matrix_file:\n",
    "    print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n",
    "    elif len(gz_files) == 1 and not soft_file:\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# 3. Read files if found\n",
    "if soft_file and matrix_file:\n",
    "    # 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",
    "    \n",
    "    try:\n",
    "        background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "        \n",
    "        # Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "        sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "        \n",
    "        # 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",
    "    except Exception as e:\n",
    "        print(f\"Error processing files: {e}\")\n",
    "        # Try swapping files if first attempt fails\n",
    "        print(\"Trying to swap SOFT and matrix files...\")\n",
    "        temp = soft_file\n",
    "        soft_file = matrix_file\n",
    "        matrix_file = temp\n",
    "        try:\n",
    "            background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "            sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "            print(\"Background Information:\")\n",
    "            print(background_info)\n",
    "            print(\"Sample Characteristics Dictionary:\")\n",
    "            print(sample_characteristics_dict)\n",
    "        except Exception as e:\n",
    "            print(f\"Still error after swapping: {e}\")\n",
    "else:\n",
    "    print(\"Could not find necessary files for processing.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2dacfe",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d8f8de3",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T08:08:43.514807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Analyze if the dataset contains gene expression data\n",
    "# Based on the background information, this dataset includes gene expression data from DLBCL cell lines\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability Analysis\n",
    "# Trait - Metabolic Rate - Not directly available in this dataset\n",
    "# This dataset appears to be about doxorubicin sensitivity in DLBCL cell lines, which doesn't directly measure metabolic rate\n",
    "trait_row = None  # No direct measurement of metabolic rate\n",
    "\n",
    "# Age - Not applicable for cell lines\n",
    "age_row = None\n",
    "\n",
    "# Gender - Not applicable for cell lines\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions (though not used in this dataset)\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait values to appropriate data type.\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    if ':' in str(value):\n",
    "        value = str(value).split(':', 1)[1].strip()\n",
    "    # Additional conversion logic would go here if trait data were available\n",
    "    return value\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age values to continuous data type.\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    if ':' in str(value):\n",
    "        value = str(value).split(':', 1)[1].strip()\n",
    "    # Additional conversion logic would go here if age data were available\n",
    "    return value\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender values to binary data type (0 for female, 1 for male).\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    if ':' in str(value):\n",
    "        value = str(value).split(':', 1)[1].strip().lower()\n",
    "    # Additional conversion logic would go here if gender data were available\n",
    "    return value\n",
    "\n",
    "# 3. Save Metadata - Initial Filtering\n",
    "# Trait data is not available (trait_row is None)\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "# Since trait_row is None, we skip the clinical feature extraction step\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1393fa6a",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b258107",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T08:08:43.814339Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No subseries references found in the first 1000 lines of the SOFT file.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene data extraction result:\n",
      "Number of rows: 54675\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n",
      "       '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n",
      "       '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n",
      "       '1552263_at', '1552264_a_at', '1552266_at'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the path to the soft and matrix files\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Looking more carefully at the background information\n",
    "# This is a SuperSeries which doesn't contain direct gene expression data\n",
    "# Need to investigate the soft file to find the subseries\n",
    "print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
    "\n",
    "# Open the SOFT file to try to identify subseries\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    subseries_lines = []\n",
    "    for i, line in enumerate(f):\n",
    "        if 'Series_relation' in line and 'SuperSeries of' in line:\n",
    "            subseries_lines.append(line.strip())\n",
    "        if i > 1000:  # Limit search to first 1000 lines\n",
    "            break\n",
    "\n",
    "# Display the subseries found\n",
    "if subseries_lines:\n",
    "    print(\"Found potential subseries references:\")\n",
    "    for line in subseries_lines:\n",
    "        print(line)\n",
    "else:\n",
    "    print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
    "\n",
    "# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(\"\\nGene data extraction result:\")\n",
    "    print(\"Number of rows:\", len(gene_data))\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n",
    "    print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2134c972",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfe36608",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:08:43.816289Z",
     "iopub.status.busy": "2025-03-25T08:08:43.816168Z",
     "iopub.status.idle": "2025-03-25T08:08:43.818067Z",
     "shell.execute_reply": "2025-03-25T08:08:43.817788Z"
    }
   },
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers, they appear to be Affymetrix probe IDs (e.g., '1007_s_at', '1053_at'),\n",
    "# not standard human gene symbols. These will need to be mapped to proper gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74baff7f",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8148f42",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T08:08:48.346014Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e45a693",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11a001db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:08:48.347702Z",
     "iopub.status.busy": "2025-03-25T08:08:48.347578Z",
     "iopub.status.idle": "2025-03-25T08:08:48.673141Z",
     "shell.execute_reply": "2025-03-25T08:08:48.672760Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping preview:\n",
      "          ID              Gene\n",
      "0  1007_s_at  DDR1 /// MIR4640\n",
      "1    1053_at              RFC2\n",
      "2     117_at             HSPA6\n",
      "3     121_at              PAX8\n",
      "4  1255_g_at            GUCA1A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene expression data after mapping:\n",
      "Number of genes: 21278\n",
      "Number of samples: 49\n",
      "First few gene symbols:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
      "       'A4GALT', 'A4GNT', 'AA06'],\n",
      "      dtype='object', name='Gene')\n",
      "\n",
      "After normalizing gene symbols:\n",
      "Number of genes after normalization: 19845\n",
      "First few normalized gene symbols:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
      "       'A4GALT', 'A4GNT', 'AA06'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the appropriate columns for gene identifiers and gene symbols\n",
    "# From the previews, we can see:\n",
    "# - Gene expression data uses identifiers like '1007_s_at' which are in the 'ID' column of the gene annotation\n",
    "# - Gene symbols are stored in the 'Gene Symbol' column of the gene annotation\n",
    "\n",
    "# 2. Get a gene mapping dataframe by extracting the ID and Gene Symbol columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
    "\n",
    "# Print a preview of the mapping dataframe\n",
    "print(\"Gene mapping preview:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print information about the resulting gene expression data\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(f\"Number of genes: {len(gene_data)}\")\n",
    "print(f\"Number of samples: {gene_data.shape[1]}\")\n",
    "print(\"First few gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Normalize gene symbols to handle synonyms and duplicates\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(\"\\nAfter normalizing gene symbols:\")\n",
    "print(f\"Number of genes after normalization: {len(gene_data)}\")\n",
    "print(\"First few normalized gene symbols:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d3f9bc",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "472f0903",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:08:48.674490Z",
     "iopub.status.busy": "2025-03-25T08:08:48.674381Z",
     "iopub.status.idle": "2025-03-25T08:08:49.354062Z",
     "shell.execute_reply": "2025-03-25T08:08:49.353690Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of gene data after normalization: (19845, 49)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved normalized gene data to ../../output/preprocess/Metabolic_Rate/gene_data/GSE89231.csv\n",
      "No metabolic rate data is available in this dataset.\n",
      "Number of samples: 49\n",
      "Saved clinical data to ../../output/preprocess/Metabolic_Rate/clinical_data/GSE89231.csv\n",
      "Skipping data linking as trait data (Metabolic_Rate) is not available in this dataset.\n",
      "Dataset is not usable for Metabolic_Rate analysis. No linked data saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Shape of gene data after normalization: {gene_data_normalized.shape}\")\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data_normalized.to_csv(out_gene_data_file)\n",
    "print(f\"Saved normalized gene data to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Acknowledge that trait data is not available\n",
    "print(\"No metabolic rate data is available in this dataset.\")\n",
    "\n",
    "# Extract sample IDs for potential clinical data\n",
    "sample_ids = gene_data.columns.tolist()\n",
    "print(f\"Number of samples: {len(sample_ids)}\")\n",
    "\n",
    "# Create a minimal clinical dataframe with only sample IDs\n",
    "clinical_df = pd.DataFrame(index=sample_ids)\n",
    "\n",
    "# Save the clinical data (contains only sample IDs)\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Saved clinical data to {out_clinical_data_file}\")\n",
    "\n",
    "# 3. Skip linking since trait data is not available\n",
    "print(\"Skipping data linking as trait data (Metabolic_Rate) is not available in this dataset.\")\n",
    "\n",
    "# 4. Validate the dataset as unusable due to missing trait data\n",
    "# Use is_final=False since we don't have valid trait data to evaluate bias\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=True,\n",
    "    is_trait_available=False  # No trait data available\n",
    ")\n",
    "\n",
    "# 5. Since the dataset is not usable for our trait, we don't save the linked data\n",
    "print(\"Dataset is not usable for Metabolic_Rate analysis. No linked data saved.\")"
   ]
  }
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