{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b040a6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:01:43.163240Z",
     "iopub.status.busy": "2025-03-25T06:01:43.163052Z",
     "iopub.status.idle": "2025-03-25T06:01:43.325442Z",
     "shell.execute_reply": "2025-03-25T06:01:43.325111Z"
    }
   },
   "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 = \"Osteoporosis\"\n",
    "cohort = \"GSE56815\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Osteoporosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Osteoporosis/GSE56815\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Osteoporosis/GSE56815.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Osteoporosis/gene_data/GSE56815.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Osteoporosis/clinical_data/GSE56815.csv\"\n",
    "json_path = \"../../output/preprocess/Osteoporosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "941dd075",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82e3902e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:01:43.326775Z",
     "iopub.status.busy": "2025-03-25T06:01:43.326641Z",
     "iopub.status.idle": "2025-03-25T06:01:43.423155Z",
     "shell.execute_reply": "2025-03-25T06:01:43.422873Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression study of blood monocytes in pre- and postmenopausal females with low or high bone mineral density (HG-U133A)\"\n",
      "!Series_summary\t\"Comparison of circulating monocytes from pre- and postmanopausal females with low or high bone mineral density (BMD). Circulating monocytes are progenitors of osteoclasts, and produce factors important to bone metabolism. Results provide insight into the role of monocytes in osteoporosis.\"\n",
      "!Series_summary\t\"We identify osteoporosis genes by microarray analyses of monocytes in high vs. low hip BMD (bone mineral density) subjects.\"\n",
      "!Series_overall_design\t\"Microarray analyses of monocytes were performed using Affymetrix HG-133A arrays in 80 Caucasian females, including 40 high (20 pre- and 20 postmanopausal) and 40 low hip BMD (20 pre- and 20 postmanopausal) subjects\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['gender: Female'], 1: ['bone mineral density: high BMD', 'bone mineral density: low BMD'], 2: ['state: postmenopausal', 'state: premenopausal'], 3: ['cell type: monocytes']}\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": "68dbc6ce",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5dfcb15b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:01:43.424201Z",
     "iopub.status.busy": "2025-03-25T06:01:43.424101Z",
     "iopub.status.idle": "2025-03-25T06:01:43.451842Z",
     "shell.execute_reply": "2025-03-25T06:01:43.451550Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{'GSM1369756': [0.0, 1.0], 'GSM1369757': [0.0, 1.0], 'GSM1369758': [0.0, 1.0], 'GSM1369759': [0.0, 1.0], 'GSM1369760': [0.0, 1.0], 'GSM1369761': [0.0, 1.0], 'GSM1369762': [0.0, 1.0], 'GSM1369763': [0.0, 0.0], 'GSM1369764': [0.0, 0.0], 'GSM1369765': [0.0, 1.0], 'GSM1369766': [0.0, 0.0], 'GSM1369767': [0.0, 1.0], 'GSM1369768': [0.0, 0.0], 'GSM1369769': [0.0, 1.0], 'GSM1369770': [0.0, 0.0], 'GSM1369771': [0.0, 1.0], 'GSM1369772': [0.0, 0.0], 'GSM1369773': [0.0, 0.0], 'GSM1369774': [0.0, 0.0], 'GSM1369775': [0.0, 0.0], 'GSM1369776': [0.0, 0.0], 'GSM1369777': [0.0, 1.0], 'GSM1369778': [0.0, 1.0], 'GSM1369779': [0.0, 1.0], 'GSM1369780': [0.0, 1.0], 'GSM1369781': [0.0, 1.0], 'GSM1369782': [0.0, 1.0], 'GSM1369783': [0.0, 1.0], 'GSM1369784': [0.0, 1.0], 'GSM1369785': [0.0, 0.0], 'GSM1369786': [0.0, 0.0], 'GSM1369787': [0.0, 0.0], 'GSM1369788': [0.0, 0.0], 'GSM1369789': [0.0, 0.0], 'GSM1369790': [0.0, 0.0], 'GSM1369791': [0.0, 0.0], 'GSM1369792': [0.0, 1.0], 'GSM1369793': [0.0, 0.0], 'GSM1369794': [0.0, 0.0], 'GSM1369795': [0.0, 0.0], 'GSM1369796': [1.0, 0.0], 'GSM1369797': [1.0, 1.0], 'GSM1369798': [1.0, 1.0], 'GSM1369799': [1.0, 1.0], 'GSM1369800': [1.0, 1.0], 'GSM1369801': [1.0, 0.0], 'GSM1369802': [1.0, 1.0], 'GSM1369803': [1.0, 1.0], 'GSM1369804': [1.0, 0.0], 'GSM1369805': [1.0, 1.0], 'GSM1369806': [1.0, 0.0], 'GSM1369807': [1.0, 1.0], 'GSM1369808': [1.0, 0.0], 'GSM1369809': [1.0, 0.0], 'GSM1369810': [1.0, 1.0], 'GSM1369811': [1.0, 0.0], 'GSM1369812': [1.0, 1.0], 'GSM1369813': [1.0, 1.0], 'GSM1369814': [1.0, 1.0], 'GSM1369815': [1.0, 0.0], 'GSM1369816': [1.0, 1.0], 'GSM1369817': [1.0, 0.0], 'GSM1369818': [1.0, 0.0], 'GSM1369819': [1.0, 0.0], 'GSM1369820': [1.0, 0.0], 'GSM1369821': [1.0, 1.0], 'GSM1369822': [1.0, 1.0], 'GSM1369823': [1.0, 1.0], 'GSM1369824': [1.0, 1.0], 'GSM1369825': [1.0, 1.0], 'GSM1369826': [1.0, 1.0], 'GSM1369827': [1.0, 0.0], 'GSM1369828': [1.0, 1.0], 'GSM1369829': [1.0, 0.0], 'GSM1369830': [1.0, 0.0], 'GSM1369831': [1.0, 0.0], 'GSM1369832': [1.0, 0.0], 'GSM1369833': [1.0, 0.0], 'GSM1369834': [1.0, 0.0], 'GSM1369835': [1.0, 0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Osteoporosis/clinical_data/GSE56815.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Assessment\n",
    "# Based on the background information, this dataset appears to contain gene expression data \n",
    "# from Affymetrix HG-133A arrays\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait data: osteoporosis can be inferred from BMD (bone mineral density) data\n",
    "trait_row = 1  # 'bone mineral density' is in the row index 1\n",
    "\n",
    "# Age-related data not available directly, but menopausal state can be used as a proxy for age ranges\n",
    "# However, menopausal state is categorical, not continuous age\n",
    "age_row = 2  # 'state' (menopausal status) is in row index 2\n",
    "\n",
    "# Gender data is available but seems constant (all Female)\n",
    "gender_row = None  # All subjects are female so this is a constant feature\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert bone mineral density data to binary trait values for osteoporosis.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part after the colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Low BMD indicates osteoporosis, high BMD indicates normal/healthy\n",
    "    if 'low' in value.lower():\n",
    "        return 1  # Osteoporosis (condition present)\n",
    "    elif 'high' in value.lower():\n",
    "        return 0  # Normal/healthy (condition absent)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert menopausal state to a binary age-related variable.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part after the colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Postmenopausal women are typically older (>50 years), premenopausal are younger\n",
    "    if 'post' in value.lower():\n",
    "        return 1  # Postmenopausal (older)\n",
    "    elif 'pre' in value.lower():\n",
    "        return 0  # Premenopausal (younger)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender data to binary, though not needed as all subjects are female.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part after the colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Standard conversion: female=0, male=1\n",
    "    if value.lower() == 'female':\n",
    "        return 0\n",
    "    elif value.lower() == 'male':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata - Initial filtering based on data availability\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 using the library function\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=None,  # All subjects are female, so gender is constant\n",
    "        convert_gender=None\n",
    "    )\n",
    "    \n",
    "    # Preview the extracted clinical data\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    \n",
    "    # Save the clinical data to CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=True)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ee8729",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92c94b33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:01:43.452910Z",
     "iopub.status.busy": "2025-03-25T06:01:43.452805Z",
     "iopub.status.idle": "2025-03-25T06:01:43.593049Z",
     "shell.execute_reply": "2025-03-25T06:01:43.592678Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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', '1598_g_at', '160020_at', '1729_at', '1773_at', '177_at',\n",
      "       '179_at', '1861_at'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5733c073",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f72bd4e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:01:43.594380Z",
     "iopub.status.busy": "2025-03-25T06:01:43.594275Z",
     "iopub.status.idle": "2025-03-25T06:01:43.596163Z",
     "shell.execute_reply": "2025-03-25T06:01:43.595844Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers appear to be Affymetrix microarray probe IDs, not standard human gene symbols.\n",
    "# For example, \"1007_s_at\", \"1053_at\" are in a format typical of Affymetrix arrays.\n",
    "# They will need to be mapped to human gene symbols for biological interpretation.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163886a1",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e916fa6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:01:43.597290Z",
     "iopub.status.busy": "2025-03-25T06:01:43.597190Z",
     "iopub.status.idle": "2025-03-25T06:01:46.195734Z",
     "shell.execute_reply": "2025-03-25T06:01:46.195287Z"
    }
   },
   "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": "0e2eb613",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00d9b43c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:01:46.197267Z",
     "iopub.status.busy": "2025-03-25T06:01:46.197150Z",
     "iopub.status.idle": "2025-03-25T06:01:46.351318Z",
     "shell.execute_reply": "2025-03-25T06:01:46.350937Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of gene data after mapping:\n",
      "Index(['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC', 'AAGAB',\n",
      "       'AAK1', 'AAMDC'],\n",
      "      dtype='object', name='Gene')\n",
      "Shape of gene expression data: (13830, 80)\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the keys for gene identifiers and gene symbols\n",
    "# From the gene expression data, the identifiers are formatted like \"1007_s_at\", which appear to match\n",
    "# the 'ID' column in the gene annotation dataframe\n",
    "\n",
    "# From the gene annotation preview, we see the 'Gene Symbol' column contains standard human gene symbols\n",
    "# like \"DDR1 /// MIR4640\", \"RFC2\", etc.\n",
    "\n",
    "# 2. Create gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Preview the first few rows of the mapped gene expression data\n",
    "print(\"Preview of gene data after mapping:\")\n",
    "print(gene_data.index[:10])\n",
    "print(f\"Shape of gene expression data: {gene_data.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc8a607",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3acffdba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:01:46.352663Z",
     "iopub.status.busy": "2025-03-25T06:01:46.352553Z",
     "iopub.status.idle": "2025-03-25T06:01:52.722954Z",
     "shell.execute_reply": "2025-03-25T06:01:52.722498Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Osteoporosis/gene_data/GSE56815.csv\n",
      "Clinical data saved to ../../output/preprocess/Osteoporosis/clinical_data/GSE56815.csv\n",
      "Linked data shape: (80, 13544)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the feature 'Osteoporosis', the least common label is '0.0' with 40 occurrences. This represents 50.00% of the dataset.\n",
      "The distribution of the feature 'Osteoporosis' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 0.0\n",
      "  50% (Median): 0.5\n",
      "  75%: 1.0\n",
      "Min: 0.0\n",
      "Max: 1.0\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Osteoporosis/GSE56815.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(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",
    "# Create clinical features directly from clinical_data using the conversion functions defined earlier\n",
    "clinical_features_df = geo_select_clinical_features(\n",
    "    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",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Now link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
    "print(\"Linked data shape:\", linked_data.shape)\n",
    "\n",
    "# Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "\n",
    "# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct quality check and save the cohort information.\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_trait_biased, \n",
    "    df=unbiased_linked_data,\n",
    "    note=\"This is an HPV-transformed keratinocyte cell line study focusing on transformation stages: 1 for anchorage independent (more advanced cancer stage), 0 for earlier stages.\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Data was determined to be unusable and was not saved\")"
   ]
  }
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