{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d128d846",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:18.580808Z",
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     "shell.execute_reply": "2025-03-25T06:08:18.746100Z"
    }
   },
   "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 = \"Parkinsons_Disease\"\n",
    "cohort = \"GSE30335\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Parkinsons_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Parkinsons_Disease/GSE30335\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Parkinsons_Disease/GSE30335.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Parkinsons_Disease/gene_data/GSE30335.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Parkinsons_Disease/clinical_data/GSE30335.csv\"\n",
    "json_path = \"../../output/preprocess/Parkinsons_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4571a841",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8594df29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:18.747895Z",
     "iopub.status.busy": "2025-03-25T06:08:18.747747Z",
     "iopub.status.idle": "2025-03-25T06:08:18.924315Z",
     "shell.execute_reply": "2025-03-25T06:08:18.923979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Genomic Signatures for Pesticide Exposure in Latino Farmworkers\"\n",
      "!Series_summary\t\"We tested the hypothesis that differential  gene expression in whole blood will reveal candidate blood biomarkers for exposure to agricultural pesticides and herbicides.  Blood gene expression in male Latino farmworkers, where chronic pesticide exposure is occupational, was compared to blood gene expression in age and gender matched Latino manual workers. We identified an expression signature for farmwork, differential expression in genes that correlated with levels of urinary pesticide metabolites, alterations in axonal guidance pathways and statistical models that link farmworker differential expression to Parkinson's disease.\"\n",
      "!Series_overall_design\t\"Case control, 20 male Latino farmworkers compared to 20 age matched male Latino manual workers. \"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['occupation: Manual Worker', 'occupation: Farmworker'], 1: ['worker id: W001', 'worker id: W004', 'worker id: W005', 'worker id: W006', 'worker id: W007', 'worker id: W009', 'worker id: W010', 'worker id: W012', 'worker id: W013', 'worker id: W014', 'worker id: W015', 'worker id: W017', 'worker id: W018', 'worker id: W019', 'worker id: W020', 'worker id: W021', 'worker id: W022', 'worker id: W023', 'worker id: W024', 'worker id: W025', 'worker id: E001', 'worker id: E002', 'worker id: E003', 'worker id: E004', 'worker id: E006', 'worker id: E007', 'worker id: E008', 'worker id: E010', 'worker id: E011', 'worker id: E012']}\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": "4c4326b8",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4fb2c06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:18.925611Z",
     "iopub.status.busy": "2025-03-25T06:08:18.925498Z",
     "iopub.status.idle": "2025-03-25T06:08:18.932844Z",
     "shell.execute_reply": "2025-03-25T06:08:18.932549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of clinical features:\n",
      "{'GSM752043': [0.0], 'GSM752044': [0.0], 'GSM752045': [0.0], 'GSM752046': [0.0], 'GSM752047': [0.0], 'GSM752049': [0.0], 'GSM752050': [0.0], 'GSM752051': [0.0], 'GSM752052': [0.0], 'GSM752053': [0.0], 'GSM752054': [0.0], 'GSM752055': [0.0], 'GSM752056': [0.0], 'GSM752057': [0.0], 'GSM752058': [0.0], 'GSM752059': [0.0], 'GSM752060': [0.0], 'GSM752061': [0.0], 'GSM752062': [0.0], 'GSM752064': [0.0], 'GSM752065': [1.0], 'GSM752066': [1.0], 'GSM752068': [1.0], 'GSM752069': [1.0], 'GSM752070': [1.0], 'GSM752071': [1.0], 'GSM752072': [1.0], 'GSM752073': [1.0], 'GSM752075': [1.0], 'GSM752076': [1.0], 'GSM752077': [1.0], 'GSM752078': [1.0], 'GSM752080': [1.0], 'GSM752081': [1.0], 'GSM752082': [1.0], 'GSM752084': [1.0], 'GSM752085': [1.0], 'GSM752086': [1.0], 'GSM752087': [1.0], 'GSM752088': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Parkinsons_Disease/clinical_data/GSE30335.csv\n"
     ]
    }
   ],
   "source": [
    "# Analysis and answers to questions\n",
    "# 1. Gene Expression Data Availability\n",
    "# From the background information, this study compares blood gene expression between Latino farmworkers\n",
    "# and manual workers, indicating gene expression data is available.\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (Parkinson's Disease):\n",
    "# From background, farmworkers have increased exposure to pesticides which is linked to Parkinson's disease\n",
    "# We can use 'occupation' as a proxy for exposure risk for Parkinson's\n",
    "trait_row = 0  # The occupation field (farmworker vs manual worker)\n",
    "\n",
    "# For age:\n",
    "# There's no explicit age information in the sample characteristics dictionary\n",
    "age_row = None\n",
    "\n",
    "# For gender:\n",
    "# The background states all participants are male, so gender is constant\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert occupation to binary trait (exposure risk for Parkinson's)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    value = value.lower().split(\": \")[-1].strip()\n",
    "    \n",
    "    if \"farmworker\" in value:\n",
    "        return 1  # Higher pesticide exposure (risk factor for Parkinson's)\n",
    "    elif \"manual worker\" in value:\n",
    "        return 0  # Lower pesticide exposure \n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value - Not applicable for this dataset\"\"\"\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary - Not applicable for this dataset\"\"\"\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Trait data is available (trait_row is not 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",
    "if trait_row is not None:\n",
    "    # Extract clinical features from the clinical data 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",
    "    print(\"Preview of clinical features:\")\n",
    "    print(preview_df(clinical_features))\n",
    "    \n",
    "    # Save to CSV\n",
    "    clinical_features.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac285234",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a729b55",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:18.933978Z",
     "iopub.status.busy": "2025-03-25T06:08:18.933872Z",
     "iopub.status.idle": "2025-03-25T06:08:19.179561Z",
     "shell.execute_reply": "2025-03-25T06:08:19.179175Z"
    }
   },
   "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": "658d0eb7",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f951348",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:19.180903Z",
     "iopub.status.busy": "2025-03-25T06:08:19.180787Z",
     "iopub.status.idle": "2025-03-25T06:08:19.182645Z",
     "shell.execute_reply": "2025-03-25T06:08:19.182370Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers are not human gene symbols but rather Affymetrix probe IDs.\n",
    "# Affymetrix probe IDs typically have formats like \"1007_s_at\", \"1053_at\", etc.\n",
    "# They need to be mapped to official gene symbols for more interpretable analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf2bbc7",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00a75a97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:19.183814Z",
     "iopub.status.busy": "2025-03-25T06:08:19.183707Z",
     "iopub.status.idle": "2025-03-25T06:08:23.362609Z",
     "shell.execute_reply": "2025-03-25T06:08:23.362205Z"
    }
   },
   "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. First get the file paths using geo_get_relevant_filepaths function\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. 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",
    "# 3. 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": "8a59a69c",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cec1609",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:23.364075Z",
     "iopub.status.busy": "2025-03-25T06:08:23.363935Z",
     "iopub.status.idle": "2025-03-25T06:08:23.593618Z",
     "shell.execute_reply": "2025-03-25T06:08:23.593218Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted expression data from probes to genes\n",
      "Number of genes: 21278\n",
      "First 10 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. Observe gene annotation data to determine identifiers and gene symbols\n",
    "# Based on the preview, the identifier in gene_data is in the 'ID' column\n",
    "# The gene symbols are in the 'Gene Symbol' column\n",
    "\n",
    "# 2. Get gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n",
    "\n",
    "# 3. Apply gene mapping to convert from probe-level measurements to gene expression\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print some information about the resulting gene expression data\n",
    "print(f\"Converted expression data from probes to genes\")\n",
    "print(f\"Number of genes: {len(gene_data)}\")\n",
    "print(\"First 10 gene symbols:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a1d93b",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cdcc9bc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:23.595140Z",
     "iopub.status.busy": "2025-03-25T06:08:23.595016Z",
     "iopub.status.idle": "2025-03-25T06:08:32.411246Z",
     "shell.execute_reply": "2025-03-25T06:08:32.410648Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data shape after normalization: (19845, 40)\n",
      "First 5 normalized gene symbols:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1'], dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Parkinsons_Disease/gene_data/GSE30335.csv\n",
      "Loaded clinical data from file\n",
      "Clinical data shape: (1, 40)\n",
      "Clinical data preview:\n",
      "{'GSM752043': [0.0], 'GSM752044': [0.0], 'GSM752045': [0.0], 'GSM752046': [0.0], 'GSM752047': [0.0], 'GSM752049': [0.0], 'GSM752050': [0.0], 'GSM752051': [0.0], 'GSM752052': [0.0], 'GSM752053': [0.0], 'GSM752054': [0.0], 'GSM752055': [0.0], 'GSM752056': [0.0], 'GSM752057': [0.0], 'GSM752058': [0.0], 'GSM752059': [0.0], 'GSM752060': [0.0], 'GSM752061': [0.0], 'GSM752062': [0.0], 'GSM752064': [0.0], 'GSM752065': [1.0], 'GSM752066': [1.0], 'GSM752068': [1.0], 'GSM752069': [1.0], 'GSM752070': [1.0], 'GSM752071': [1.0], 'GSM752072': [1.0], 'GSM752073': [1.0], 'GSM752075': [1.0], 'GSM752076': [1.0], 'GSM752077': [1.0], 'GSM752078': [1.0], 'GSM752080': [1.0], 'GSM752081': [1.0], 'GSM752082': [1.0], 'GSM752084': [1.0], 'GSM752085': [1.0], 'GSM752086': [1.0], 'GSM752087': [1.0], 'GSM752088': [1.0]}\n",
      "Linked data shape: (40, 19846)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (40, 19846)\n",
      "For the feature 'Parkinsons_Disease', the least common label is '0.0' with 20 occurrences. This represents 50.00% of the dataset.\n",
      "The distribution of the feature 'Parkinsons_Disease' in this dataset is fine.\n",
      "\n",
      "Data shape after removing biased features: (40, 19846)\n",
      "Is the trait biased: False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Parkinsons_Disease/GSE30335.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols from the already mapped gene expression data from Step 6\n",
    "# Apply normalization to standardize gene symbols\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene expression data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(\"First 5 normalized gene symbols:\")\n",
    "print(normalized_gene_data.index[:5])\n",
    "\n",
    "# Save the normalized gene data\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. Load the clinical data that was already processed in Step 2\n",
    "# We need to load the clinical data from the file that was saved in Step 2\n",
    "if os.path.exists(out_clinical_data_file):\n",
    "    clinical_data_processed = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(\"Loaded clinical data from file\")\n",
    "else:\n",
    "    # If for some reason the file wasn't saved, recreate the clinical features using the same parameters as in Step 2\n",
    "    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "    background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "    \n",
    "    # Use the same conversion function and trait_row from Step 2\n",
    "    def convert_trait(value):\n",
    "        \"\"\"Convert occupation to binary trait (exposure risk for Parkinson's)\"\"\"\n",
    "        if not isinstance(value, str):\n",
    "            return None\n",
    "        \n",
    "        value = value.lower().split(\": \")[-1].strip()\n",
    "        \n",
    "        if \"farmworker\" in value:\n",
    "            return 1  # Higher pesticide exposure (risk factor for Parkinson's)\n",
    "        elif \"manual worker\" in value:\n",
    "            return 0  # Lower pesticide exposure \n",
    "        else:\n",
    "            return None\n",
    "    \n",
    "    # Use the exact same parameters as we determined in Step 2\n",
    "    clinical_data_processed = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=0,  # From Step 2\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=None,  # From Step 2\n",
    "        convert_age=None,\n",
    "        gender_row=None,  # From Step 2\n",
    "        convert_gender=None\n",
    "    )\n",
    "    \n",
    "    # Save it again just to be sure\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_data_processed.to_csv(out_clinical_data_file)\n",
    "\n",
    "print(\"Clinical data shape:\", clinical_data_processed.shape)\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_data_processed))\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "# The clinical data is oriented with genes/traits as rows and samples as columns\n",
    "# Transpose the normalized gene data to match this orientation (samples as columns)\n",
    "genetic_data_t = normalized_gene_data\n",
    "\n",
    "# Link clinical and genetic data vertically\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data_processed, genetic_data_t)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Determine if trait and demographic features are biased\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
    "print(f\"Is the trait biased: {is_biased}\")\n",
    "\n",
    "# 6. Validate and save cohort info\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=True,\n",
    "    is_trait_available=True,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from blood samples comparing farmworkers (with higher pesticide exposure, a risk factor for Parkinson's) to manual workers.\"\n",
    ")\n",
    "\n",
    "# 7. 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 deemed not usable. Linked data was not saved.\")"
   ]
  }
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