{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf9f03c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:16:04.535185Z",
     "iopub.status.busy": "2025-03-25T05:16:04.534990Z",
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     "shell.execute_reply": "2025-03-25T05:16:04.703678Z"
    }
   },
   "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 = \"GSE61629\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Essential_Thrombocythemia\"\n",
    "in_cohort_dir = \"../../input/GEO/Essential_Thrombocythemia/GSE61629\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Essential_Thrombocythemia/GSE61629.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE61629.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE61629.csv\"\n",
    "json_path = \"../../output/preprocess/Essential_Thrombocythemia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff79374",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa07891e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:16:04.705313Z",
     "iopub.status.busy": "2025-03-25T05:16:04.705163Z",
     "iopub.status.idle": "2025-03-25T05:16:04.924253Z",
     "shell.execute_reply": "2025-03-25T05:16:04.923666Z"
    }
   },
   "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) (untreated)\"\n",
      "!Series_summary\t\"Microarrays were used to assess gene expression in patients with ET, PV, and PMF before treatment with IFNalpha2.\"\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 was used to calculate differences in gene expression between patients and control subjects.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['disease state: ET', 'disease state: PMF', 'disease state: PV', 'disease state: control'], 1: ['treatment: untreated', 'tissue: blood'], 2: ['tissue: Whole blood', nan]}\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": "f8710bcc",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e8c96d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:16:04.926275Z",
     "iopub.status.busy": "2025-03-25T05:16:04.926127Z",
     "iopub.status.idle": "2025-03-25T05:16:04.935300Z",
     "shell.execute_reply": "2025-03-25T05:16:04.934761Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of clinical data:\n",
      "{'GSM1388566': [1.0], 'GSM1388567': [1.0], 'GSM1388568': [1.0], 'GSM1388569': [1.0], 'GSM1388570': [1.0], 'GSM1388571': [1.0], 'GSM1388577': [1.0], 'GSM1388579': [1.0], 'GSM1388582': [0.0], 'GSM1388584': [0.0], 'GSM1388585': [0.0], 'GSM1388587': [0.0], 'GSM1388590': [0.0], 'GSM1388591': [0.0], 'GSM1388592': [0.0], 'GSM1388593': [0.0], 'GSM1388594': [0.0], 'GSM1388595': [0.0], 'GSM1388596': [0.0], 'GSM1388598': [0.0], 'GSM1388599': [0.0], 'GSM1388600': [0.0], 'GSM1388601': [0.0], 'GSM1388603': [0.0], 'GSM1388604': [0.0], 'GSM1388605': [0.0], 'GSM1388606': [0.0], 'GSM1388607': [0.0], 'GSM1388608': [0.0], 'GSM1388614': [0.0], 'GSM1388616': [0.0], 'GSM1388623': [0.0], 'GSM1388624': [0.0], 'GSM1509517': [0.0], 'GSM1509518': [0.0], 'GSM1509519': [0.0], 'GSM1509520': [0.0], 'GSM1509521': [0.0], 'GSM1509522': [0.0], 'GSM1509523': [0.0], 'GSM1509524': [0.0], 'GSM1509525': [0.0], 'GSM1509526': [0.0], 'GSM1509527': [0.0], 'GSM1509528': [0.0], 'GSM1509529': [0.0], 'GSM1509530': [0.0], 'GSM1509531': [0.0], 'GSM1509532': [0.0], 'GSM1509533': [0.0], 'GSM1509534': [0.0], 'GSM1509535': [0.0], 'GSM1509536': [0.0], 'GSM1509537': [0.0]}\n",
      "Clinical data saved to: ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE61629.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on background information, the data contains gene expression from microarrays\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# Trait (Essential_Thrombocythemia) data is available in row 0 (disease state)\n",
    "trait_row = 0\n",
    "\n",
    "# Age is not provided in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender is not provided in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert the trait value to binary (0 or 1) where 1 represents having Essential_Thrombocythemia\n",
    "    \"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Check if the subject has Essential Thrombocythemia (ET)\n",
    "    if value.upper() == \"ET\":\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def convert_age(value):\n",
    "    # Not applicable as age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Not applicable as gender data is not available\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Check if trait data is available by checking if trait_row is not None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Conduct initial filtering and save metadata\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\n",
    "    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",
    "    # Preview the clinical dataframe\n",
    "    preview_result = preview_df(clinical_df)\n",
    "    print(\"Preview of clinical data:\")\n",
    "    print(preview_result)\n",
    "    \n",
    "    # Save the clinical dataframe to CSV\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\"Clinical data saved to: {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3fb3807",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b4f0752",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:16:04.937026Z",
     "iopub.status.busy": "2025-03-25T05:16:04.936909Z",
     "iopub.status.idle": "2025-03-25T05:16:05.266957Z",
     "shell.execute_reply": "2025-03-25T05:16:05.266350Z"
    }
   },
   "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": "89b68b27",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b687fa91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:16:05.268940Z",
     "iopub.status.busy": "2025-03-25T05:16:05.268794Z",
     "iopub.status.idle": "2025-03-25T05:16:05.271492Z",
     "shell.execute_reply": "2025-03-25T05:16:05.270943Z"
    }
   },
   "outputs": [],
   "source": [
    "# Reviewing the gene identifiers\n",
    "\n",
    "# The identifiers shown are Affymetrix probe IDs (like '1007_s_at', '1053_at', etc.)\n",
    "# These are not human gene symbols but microarray probe identifiers that need to be \n",
    "# mapped to gene symbols for biological interpretation\n",
    "\n",
    "# Affymetrix probe IDs are in the format of numbers followed by \"_at\", \"_s_at\", \"_x_at\", etc.\n",
    "# They need to be mapped to actual gene symbols using annotation databases\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e0c362",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "882db335",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:16:05.273050Z",
     "iopub.status.busy": "2025-03-25T05:16:05.272933Z",
     "iopub.status.idle": "2025-03-25T05:16:10.449101Z",
     "shell.execute_reply": "2025-03-25T05:16:10.448484Z"
    }
   },
   "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": "dc460d11",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d86f6ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:16:10.450955Z",
     "iopub.status.busy": "2025-03-25T05:16:10.450825Z",
     "iopub.status.idle": "2025-03-25T05:16:10.731527Z",
     "shell.execute_reply": "2025-03-25T05:16:10.730859Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted gene expression data preview (first 10 genes):\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
      "       'A4GALT', 'A4GNT', 'AA06'],\n",
      "      dtype='object', name='Gene')\n",
      "Total number of genes after mapping: 21278\n"
     ]
    }
   ],
   "source": [
    "# 1. Observe which columns store gene identifiers and gene symbols\n",
    "# Based on previewing gene_annotation, we can see:\n",
    "# - 'ID' contains the probe identifiers (matches gene_data.index format)\n",
    "# - 'Gene Symbol' contains the gene symbols\n",
    "\n",
    "# 2. Get the gene mapping dataframe by extracting these two columns\n",
    "probe_col = 'ID'\n",
    "gene_col = 'Gene Symbol'\n",
    "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
    "\n",
    "# 3. Apply the gene mapping to convert from probe-level to gene-level expression\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print sample of the gene-mapped data\n",
    "print(\"Converted gene expression data preview (first 10 genes):\")\n",
    "print(gene_data.index[:10])\n",
    "print(f\"Total number of genes after mapping: {len(gene_data)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61989fc",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a3ee5ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:16:10.733522Z",
     "iopub.status.busy": "2025-03-25T05:16:10.733402Z",
     "iopub.status.idle": "2025-03-25T05:16:21.918532Z",
     "shell.execute_reply": "2025-03-25T05:16:21.917861Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n",
      "Gene data shape after normalization: (19845, 54)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE61629.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], 'GSM1388577': [1.0], 'GSM1388579': [1.0], 'GSM1388582': [0.0], 'GSM1388584': [0.0], 'GSM1388585': [0.0], 'GSM1388587': [0.0], 'GSM1388590': [0.0], 'GSM1388591': [0.0], 'GSM1388592': [0.0], 'GSM1388593': [0.0], 'GSM1388594': [0.0], 'GSM1388595': [0.0], 'GSM1388596': [0.0], 'GSM1388598': [0.0], 'GSM1388599': [0.0], 'GSM1388600': [0.0], 'GSM1388601': [0.0], 'GSM1388603': [0.0], 'GSM1388604': [0.0], 'GSM1388605': [0.0], 'GSM1388606': [0.0], 'GSM1388607': [0.0], 'GSM1388608': [0.0], 'GSM1388614': [0.0], 'GSM1388616': [0.0], 'GSM1388623': [0.0], 'GSM1388624': [0.0], 'GSM1509517': [0.0], 'GSM1509518': [0.0], 'GSM1509519': [0.0], 'GSM1509520': [0.0], 'GSM1509521': [0.0], 'GSM1509522': [0.0], 'GSM1509523': [0.0], 'GSM1509524': [0.0], 'GSM1509525': [0.0], 'GSM1509526': [0.0], 'GSM1509527': [0.0], 'GSM1509528': [0.0], 'GSM1509529': [0.0], 'GSM1509530': [0.0], 'GSM1509531': [0.0], 'GSM1509532': [0.0], 'GSM1509533': [0.0], 'GSM1509534': [0.0], 'GSM1509535': [0.0], 'GSM1509536': [0.0], 'GSM1509537': [0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE61629.csv\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (54, 19846)\n",
      "Handling missing values...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (54, 19846)\n",
      "Checking for bias in trait distribution...\n",
      "For the feature 'Essential_Thrombocythemia', the least common label is '1.0' with 8 occurrences. This represents 14.81% 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/GSE61629.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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