{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22b6a117",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:44.497617Z",
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     "shell.execute_reply": "2025-03-25T05:15:44.690595Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
    "\n",
    "# Path Configuration\n",
    "from tools.preprocess import *\n",
    "\n",
    "# Processing context\n",
    "trait = \"Essential_Thrombocythemia\"\n",
    "cohort = \"GSE57793\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Essential_Thrombocythemia\"\n",
    "in_cohort_dir = \"../../input/GEO/Essential_Thrombocythemia/GSE57793\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Essential_Thrombocythemia/GSE57793.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE57793.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE57793.csv\"\n",
    "json_path = \"../../output/preprocess/Essential_Thrombocythemia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105b0abf",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "084f6b0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:44.692506Z",
     "iopub.status.busy": "2025-03-25T05:15:44.692353Z",
     "iopub.status.idle": "2025-03-25T05:15:44.932685Z",
     "shell.execute_reply": "2025-03-25T05:15:44.932356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Expression data from patients with Essential Thrombocythemia (ET), Polycythemia Vera (PV), Primary Myelofibrosis (PMF)\"\n",
      "!Series_summary\t\"Microarrays were used to assess gene expression in patients with ET, PV, and PMF before and after treatment with IFNalpha2 in a paired design.\"\n",
      "!Series_overall_design\t\"Total RNA was purified from whole blood and amplified to biotin-labeled aRNA and hybridized to microarray chips. Background correction, normalization, and gene expression index calculation were performed with the robust multi-array (rma) method. The regularized t-test limma for pairwise data was used to calculate differences in gene expression between patients before and after treatment with IFN-alpha2.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['disease state: ET', 'disease state: PMF', 'disease state: PV'], 1: ['treatment: untreated', 'treatment: IFN-alpha2'], 2: ['tissue: Whole blood']}\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": "dc95214a",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46b6d3ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:44.933930Z",
     "iopub.status.busy": "2025-03-25T05:15:44.933811Z",
     "iopub.status.idle": "2025-03-25T05:15:44.942915Z",
     "shell.execute_reply": "2025-03-25T05:15:44.942592Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of clinical features: {'GSM1388566': [1.0], 'GSM1388567': [1.0], 'GSM1388568': [1.0], 'GSM1388569': [1.0], 'GSM1388570': [1.0], 'GSM1388571': [1.0], 'GSM1388572': [1.0], 'GSM1388573': [1.0], 'GSM1388574': [1.0], 'GSM1388575': [1.0], 'GSM1388576': [1.0], 'GSM1388577': [1.0], 'GSM1388578': [1.0], 'GSM1388579': [1.0], 'GSM1388580': [1.0], 'GSM1388581': [1.0], 'GSM1388582': [0.0], 'GSM1388583': [0.0], 'GSM1388584': [0.0], 'GSM1388585': [0.0], 'GSM1388586': [0.0], 'GSM1388587': [0.0], 'GSM1388588': [0.0], 'GSM1388589': [0.0], 'GSM1388590': [0.0], 'GSM1388591': [0.0], 'GSM1388592': [0.0], 'GSM1388593': [0.0], 'GSM1388594': [0.0], 'GSM1388595': [0.0], 'GSM1388596': [0.0], 'GSM1388597': [0.0], 'GSM1388598': [0.0], 'GSM1388599': [0.0], 'GSM1388600': [0.0], 'GSM1388601': [0.0], 'GSM1388602': [0.0], 'GSM1388603': [0.0], 'GSM1388604': [0.0], 'GSM1388605': [0.0], 'GSM1388606': [0.0], 'GSM1388607': [0.0], 'GSM1388608': [0.0], 'GSM1388609': [0.0], 'GSM1388610': [0.0], 'GSM1388611': [0.0], 'GSM1388612': [0.0], 'GSM1388613': [0.0], 'GSM1388614': [0.0], 'GSM1388615': [0.0], 'GSM1388616': [0.0], 'GSM1388617': [0.0], 'GSM1388618': [0.0], 'GSM1388619': [0.0], 'GSM1388620': [0.0], 'GSM1388621': [0.0], 'GSM1388622': [0.0], 'GSM1388623': [0.0], 'GSM1388624': [0.0], 'GSM1388625': [0.0], 'GSM1388626': [0.0], 'GSM1388627': [0.0], 'GSM1388628': [0.0], 'GSM1388629': [0.0], 'GSM1388630': [0.0], 'GSM1388631': [0.0]}\n",
      "Clinical features saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE57793.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression data from microarrays\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait: In the sample characteristics dictionary, key 0 contains disease state information including ET\n",
    "trait_row = 0\n",
    "# Age and gender information are not available in the sample characteristics dictionary\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert the trait value to binary format:\n",
    "    - 1 for Essential Thrombocythemia (ET)\n",
    "    - 0 for other conditions (PMF, PV)\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on ET status\n",
    "    if value.upper() == 'ET':\n",
    "        return 1\n",
    "    elif value.upper() in ['PMF', 'PV']:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Placeholder function for age conversion (not used in this dataset)\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"\n",
    "    Placeholder function for gender conversion (not used in this dataset)\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    value = value.lower()\n",
    "    if value in ['female', 'f']:\n",
    "        return 0\n",
    "    elif value in ['male', 'm']:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata - Initial filtering validation\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features from the dataframe\n",
    "    clinical_features = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the extracted clinical features\n",
    "    preview = preview_df(clinical_features)\n",
    "    print(f\"Preview of clinical features: {preview}\")\n",
    "    \n",
    "    # Save the clinical features to CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5ea261",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "544a17cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:44.943979Z",
     "iopub.status.busy": "2025-03-25T05:15:44.943871Z",
     "iopub.status.idle": "2025-03-25T05:15:45.321824Z",
     "shell.execute_reply": "2025-03-25T05:15:45.321468Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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 file paths again to access the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data from the matrix_file\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
    "print(\"First 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ed29a4",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d8f6946",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:45.322993Z",
     "iopub.status.busy": "2025-03-25T05:15:45.322872Z",
     "iopub.status.idle": "2025-03-25T05:15:45.324806Z",
     "shell.execute_reply": "2025-03-25T05:15:45.324496Z"
    }
   },
   "outputs": [],
   "source": [
    "# Examine the identifiers from the previous step\n",
    "# These identifiers (like '1007_s_at', '1053_at') are typical Affymetrix probe IDs\n",
    "# used on microarray platforms. They are not human gene symbols and need to be mapped.\n",
    "# Affymetrix probe IDs typically follow this format with numbers and \"_at\" suffix.\n",
    "\n",
    "# Based on my domain knowledge in biomedical research, these are clearly Affymetrix\n",
    "# microarray probe IDs, not human gene symbols like BRCA1, TP53, etc.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43696c40",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e9d12ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:45.325789Z",
     "iopub.status.busy": "2025-03-25T05:15:45.325682Z",
     "iopub.status.idle": "2025-03-25T05:15:51.607312Z",
     "shell.execute_reply": "2025-03-25T05:15:51.606660Z"
    }
   },
   "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": "a735b960",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63c73386",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:51.609250Z",
     "iopub.status.busy": "2025-03-25T05:15:51.609086Z",
     "iopub.status.idle": "2025-03-25T05:15:52.007409Z",
     "shell.execute_reply": "2025-03-25T05:15:52.006762Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping sample (first 5 rows):\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": [
      "Gene expression data shape after mapping: (19845, 66)\n",
      "First 5 gene symbols:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1'], dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Based on observation:\n",
    "# - The gene expression data uses 'ID' as the key for identifiers (like '1007_s_at')\n",
    "# - The gene annotation dataframe has both 'ID' column for probe IDs and 'Gene Symbol' column for gene symbols\n",
    "\n",
    "# 2. Get gene mapping dataframe using the two columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n",
    "\n",
    "# Print sample of the mapping to verify\n",
    "print(\"Gene mapping sample (first 5 rows):\")\n",
    "print(gene_mapping.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Normalize gene symbols\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "\n",
    "# Print the shape of the resulting gene expression data\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First 5 gene symbols:\")\n",
    "print(gene_data.index[:5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac80ecd4",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11679720",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:52.009405Z",
     "iopub.status.busy": "2025-03-25T05:15:52.009257Z",
     "iopub.status.idle": "2025-03-25T05:16:03.480826Z",
     "shell.execute_reply": "2025-03-25T05:16:03.480457Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n",
      "Gene data shape after normalization: (19845, 66)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE57793.csv\n",
      "Extracting clinical features...\n",
      "Clinical data preview:\n",
      "{'GSM1388566': [1.0], 'GSM1388567': [1.0], 'GSM1388568': [1.0], 'GSM1388569': [1.0], 'GSM1388570': [1.0], 'GSM1388571': [1.0], 'GSM1388572': [1.0], 'GSM1388573': [1.0], 'GSM1388574': [1.0], 'GSM1388575': [1.0], 'GSM1388576': [1.0], 'GSM1388577': [1.0], 'GSM1388578': [1.0], 'GSM1388579': [1.0], 'GSM1388580': [1.0], 'GSM1388581': [1.0], 'GSM1388582': [0.0], 'GSM1388583': [0.0], 'GSM1388584': [0.0], 'GSM1388585': [0.0], 'GSM1388586': [0.0], 'GSM1388587': [0.0], 'GSM1388588': [0.0], 'GSM1388589': [0.0], 'GSM1388590': [0.0], 'GSM1388591': [0.0], 'GSM1388592': [0.0], 'GSM1388593': [0.0], 'GSM1388594': [0.0], 'GSM1388595': [0.0], 'GSM1388596': [0.0], 'GSM1388597': [0.0], 'GSM1388598': [0.0], 'GSM1388599': [0.0], 'GSM1388600': [0.0], 'GSM1388601': [0.0], 'GSM1388602': [0.0], 'GSM1388603': [0.0], 'GSM1388604': [0.0], 'GSM1388605': [0.0], 'GSM1388606': [0.0], 'GSM1388607': [0.0], 'GSM1388608': [0.0], 'GSM1388609': [0.0], 'GSM1388610': [0.0], 'GSM1388611': [0.0], 'GSM1388612': [0.0], 'GSM1388613': [0.0], 'GSM1388614': [0.0], 'GSM1388615': [0.0], 'GSM1388616': [0.0], 'GSM1388617': [0.0], 'GSM1388618': [0.0], 'GSM1388619': [0.0], 'GSM1388620': [0.0], 'GSM1388621': [0.0], 'GSM1388622': [0.0], 'GSM1388623': [0.0], 'GSM1388624': [0.0], 'GSM1388625': [0.0], 'GSM1388626': [0.0], 'GSM1388627': [0.0], 'GSM1388628': [0.0], 'GSM1388629': [0.0], 'GSM1388630': [0.0], 'GSM1388631': [0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE57793.csv\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (66, 19846)\n",
      "Handling missing values...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (66, 19846)\n",
      "Checking for bias in trait distribution...\n",
      "For the feature 'Essential_Thrombocythemia', the least common label is '1.0' with 16 occurrences. This represents 24.24% of the dataset.\n",
      "The distribution of the feature 'Essential_Thrombocythemia' in this dataset is fine.\n",
      "\n",
      "Dataset usability: True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Essential_Thrombocythemia/GSE57793.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols...\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to a CSV file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "print(\"Extracting clinical features...\")\n",
    "# Create the clinical features using the trait row identified in Step 2\n",
    "selected_clinical_df = geo_select_clinical_features(\n",
    "    clinical_df=clinical_data,\n",
    "    trait=trait,\n",
    "    trait_row=trait_row,\n",
    "    convert_trait=convert_trait,\n",
    "    age_row=age_row,\n",
    "    convert_age=convert_age,\n",
    "    gender_row=gender_row,\n",
    "    convert_gender=convert_gender\n",
    ")\n",
    "\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Save the clinical data to a CSV file\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",
    "# Link clinical and genetic data using the normalized gene data\n",
    "print(\"Linking clinical and genetic data...\")\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "print(\"Handling missing values...\")\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check if trait is biased\n",
    "print(\"Checking for bias in trait distribution...\")\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Final validation\n",
    "note = \"Dataset contains gene expression data from patients with Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (PMF).\"\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "print(f\"Dataset usability: {is_usable}\")\n",
    "\n",
    "# 6. Save linked data if usable\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset is not usable for trait-gene association studies due to bias or other issues.\")"
   ]
  }
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