{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43c2f837",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T06:22:30.481357Z"
    }
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   "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 = \"Adrenocortical_Cancer\"\n",
    "cohort = \"GSE90713\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Adrenocortical_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Adrenocortical_Cancer/GSE90713\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Adrenocortical_Cancer/GSE90713.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE90713.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE90713.csv\"\n",
    "json_path = \"../../output/preprocess/Adrenocortical_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0ebc68",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa74cd86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:22:30.483079Z",
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     "shell.execute_reply": "2025-03-25T06:22:30.645709Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Expression data from human metastatic adrenocortical carcinoma\"\n",
      "!Series_summary\t\"CXCR4 expression by metastatic adrenocortical carcinoma is heterogeneous among patients and among lesions\"\n",
      "!Series_summary\t\"We used microarrays for 57 ACC metastases from 42 patients to evaluate gene expression in different lesions from same patients and over time, focusing on CXCR4 expression and other genes correlating with CXCR4 expression\"\n",
      "!Series_overall_design\t\"57 ACC metastases from 42 patients were used for RNA extraction and hybridization on Affymetrix microarrays. We sought to obtain data on CXCR4 expression by ACC metastases. Multiple lesion samples were aquired for 9 of the patients, labeled a thru i. Single samples were aquired from the other subjects.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: adrenocortical carcinoma', 'tissue: normal adrenal'], 1: ['study: 426', 'study: 920'], 2: ['condition: tumor', 'condition: normal'], 3: ['acc_num: 1', 'acc_num: 6', 'acc_num: 7', 'acc_num: 8', 'acc_num: 9', 'acc_num: 11', 'acc_num: 13', 'acc_num: 14', 'acc_num: 15', 'acc_num: 16', 'acc_num: 17', 'acc_num: 20', 'acc_num: 22', 'acc_num: 25', 'acc_num: 26', 'acc_num: 27', 'acc_num: 28', 'acc_num: 29', 'acc_num: 30', 'acc_num: 31', 'acc_num: 32', 'acc_num: 33', 'acc_num: 34', 'acc_num: 35', 'acc_num: 36', 'acc_num: 37', 'acc_num: 38', 'acc_num: 39', 'acc_num: 41', 'acc_num: NA1'], 4: ['patient: a', 'patient: b', 'patient: c', 'patient: d', 'patient: A_16', 'patient: A_17', 'patient: A_20', 'patient: A_22', 'patient: A_26', 'patient: A_27', 'patient: A_29', 'patient: e', 'patient: A_31', 'patient: A_32', 'patient: A_33', 'patient: A_34', 'patient: f', 'patient: A_38', 'patient: A_39', 'patient: A_41', 'patient: g', 'patient: A_9', 'patient: A_NA1', 'patient: A_NA18', 'patient: A_NA19', 'patient: A_NA2', 'patient: A_NA4', 'patient: B_1', 'patient: B_10', 'patient: B_11_1']}\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": "3eef9334",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19eaa325",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:22:30.647649Z",
     "iopub.status.busy": "2025-03-25T06:22:30.647540Z",
     "iopub.status.idle": "2025-03-25T06:22:30.667672Z",
     "shell.execute_reply": "2025-03-25T06:22:30.667400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical Data Preview:\n",
      "{'GSM2411058': [1.0], 'GSM2411059': [1.0], 'GSM2411060': [1.0], 'GSM2411061': [1.0], 'GSM2411062': [1.0], 'GSM2411063': [1.0], 'GSM2411064': [1.0], 'GSM2411065': [1.0], 'GSM2411066': [1.0], 'GSM2411067': [1.0], 'GSM2411068': [1.0], 'GSM2411069': [1.0], 'GSM2411070': [1.0], 'GSM2411071': [1.0], 'GSM2411072': [1.0], 'GSM2411073': [1.0], 'GSM2411074': [1.0], 'GSM2411075': [1.0], 'GSM2411076': [1.0], 'GSM2411077': [1.0], 'GSM2411078': [1.0], 'GSM2411079': [1.0], 'GSM2411080': [1.0], 'GSM2411081': [1.0], 'GSM2411082': [1.0], 'GSM2411083': [1.0], 'GSM2411084': [1.0], 'GSM2411085': [1.0], 'GSM2411086': [1.0], 'GSM2411087': [0.0], 'GSM2411088': [0.0], 'GSM2411089': [0.0], 'GSM2411090': [0.0], 'GSM2411091': [0.0], 'GSM2411092': [1.0], 'GSM2411093': [1.0], 'GSM2411094': [1.0], 'GSM2411095': [1.0], 'GSM2411096': [1.0], 'GSM2411097': [1.0], 'GSM2411098': [1.0], 'GSM2411099': [1.0], 'GSM2411100': [1.0], 'GSM2411101': [1.0], 'GSM2411102': [1.0], 'GSM2411103': [1.0], 'GSM2411104': [1.0], 'GSM2411105': [1.0], 'GSM2411106': [1.0], 'GSM2411107': [1.0], 'GSM2411108': [1.0], 'GSM2411109': [1.0], 'GSM2411110': [1.0], 'GSM2411111': [1.0], 'GSM2411112': [1.0], 'GSM2411113': [1.0], 'GSM2411114': [1.0], 'GSM2411115': [1.0], 'GSM2411116': [1.0], 'GSM2411117': [1.0], 'GSM2411118': [1.0], 'GSM2411119': [1.0], 'GSM2411120': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE90713.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine gene expression data availability\n",
    "# Based on the background information, this dataset contains microarray data from ACC metastases\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Trait data: condition (tumor vs normal)\n",
    "trait_row = 2  # From the sample characteristics dictionary key 2: ['condition: tumor', 'condition: normal']\n",
    "\n",
    "# Function to convert trait values\n",
    "def convert_trait(value):\n",
    "    if isinstance(value, str):\n",
    "        value = value.strip().lower()\n",
    "        if \"tumor\" in value:\n",
    "            return 1\n",
    "        elif \"normal\" in value:\n",
    "            return 0\n",
    "    return None\n",
    "\n",
    "# 2.2 Age data: Not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "def convert_age(value):\n",
    "    return None  # Not used but defined for completeness\n",
    "\n",
    "# 2.3 Gender data: Not available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "def convert_gender(value):\n",
    "    return None  # Not used but defined for completeness\n",
    "\n",
    "# 3. Save metadata\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\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 data\n",
    "    print(\"Clinical Data Preview:\")\n",
    "    print(preview_df(clinical_df))\n",
    "    \n",
    "    # Save the clinical data to CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8afbd72f",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6a0ca4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:22:30.668780Z",
     "iopub.status.busy": "2025-03-25T06:22:30.668680Z",
     "iopub.status.idle": "2025-03-25T06:22:30.947385Z",
     "shell.execute_reply": "2025-03-25T06:22:30.947011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene/probe identifiers:\n",
      "Index(['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at',\n",
      "       '11715104_s_at', '11715105_at', '11715106_x_at', '11715107_s_at',\n",
      "       '11715108_x_at', '11715109_at', '11715110_at', '11715111_s_at',\n",
      "       '11715112_at', '11715113_x_at', '11715114_x_at', '11715115_s_at',\n",
      "       '11715116_s_at', '11715117_x_at', '11715118_s_at', '11715119_s_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": "48cac1ad",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "72b9f945",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:22:30.948751Z",
     "iopub.status.busy": "2025-03-25T06:22:30.948631Z",
     "iopub.status.idle": "2025-03-25T06:22:30.950484Z",
     "shell.execute_reply": "2025-03-25T06:22:30.950205Z"
    }
   },
   "outputs": [],
   "source": [
    "# The gene identifiers in the data are in the format '11715100_at', '11715101_s_at', etc.\n",
    "# These appear to be Affymetrix probe IDs, not human gene symbols.\n",
    "# Affymetrix probe IDs need to be mapped to standard gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a271b1a3",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ad89107",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2025-03-25T06:22:39.853742Z",
     "shell.execute_reply": "2025-03-25T06:22:39.853255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at', '11715104_s_at'], 'GeneChip Array': ['Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array'], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['30-Mar-16', '30-Mar-16', '30-Mar-16', '30-Mar-16', '30-Mar-16'], 'Sequence Type': ['Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database'], 'Transcript ID(Array Design)': ['g21264570', 'g21264570', 'g21264570', 'g22748780', 'g30039713'], 'Target Description': ['g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g22748780 /TID=g22748780 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g22748780 /REP_ORG=Homo sapiens', 'g30039713 /TID=g30039713 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g30039713 /REP_ORG=Homo sapiens'], 'GB_ACC': [nan, nan, nan, nan, nan], 'GI': [21264570.0, 21264570.0, 21264570.0, 22748780.0, 30039713.0], 'Representative Public ID': ['g21264570', 'g21264570', 'g21264570', 'g22748780', 'g30039713'], 'Archival UniGene Cluster': ['---', '---', '---', '---', '---'], 'UniGene ID': ['Hs.247813', 'Hs.247813', 'Hs.247813', 'Hs.465643', 'Hs.352515'], 'Genome Version': ['February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)'], 'Alignments': ['chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr19:4639529-5145579 (+) // 48.53 // p13.3', 'chr17:72920369-72929640 (+) // 100.0 // q25.1'], 'Gene Title': ['histone cluster 1, H3g', 'histone cluster 1, H3g', 'histone cluster 1, H3g', 'tumor necrosis factor, alpha-induced protein 8-like 1', 'otopetrin 2'], 'Gene Symbol': ['HIST1H3G', 'HIST1H3G', 'HIST1H3G', 'TNFAIP8L1', 'OTOP2'], 'Chromosomal Location': ['chr6p22.2', 'chr6p22.2', 'chr6p22.2', 'chr19p13.3', 'chr17q25.1'], 'Unigene Cluster Type': ['full length', 'full length', 'full length', 'full length', 'full length'], 'Ensembl': ['ENSG00000273983 /// OTTHUMG00000014436', 'ENSG00000273983 /// OTTHUMG00000014436', 'ENSG00000273983 /// OTTHUMG00000014436', 'ENSG00000185361 /// OTTHUMG00000182013', 'ENSG00000183034 /// OTTHUMG00000179215'], 'Entrez Gene': ['8355', '8355', '8355', '126282', '92736'], 'SwissProt': ['P68431', 'P68431', 'P68431', 'Q8WVP5', 'Q7RTS6'], 'EC': ['---', '---', '---', '---', '---'], 'OMIM': ['602815', '602815', '602815', '615869', '607827'], 'RefSeq Protein ID': ['NP_003525', 'NP_003525', 'NP_003525', 'NP_001161414 /// NP_689575 /// XP_005259544 /// XP_011525982', 'NP_835454 /// XP_011523781'], 'RefSeq Transcript ID': ['NM_003534', 'NM_003534', 'NM_003534', 'NM_001167942 /// NM_152362 /// XM_005259487 /// XM_011527680', 'NM_178160 /// XM_011525479'], 'Gene Ontology Biological Process': ['0000183 // chromatin silencing at rDNA // traceable author statement /// 0002230 // positive regulation of defense response to virus by host // inferred from mutant phenotype /// 0006325 // chromatin organization // traceable author statement /// 0006334 // nucleosome assembly // inferred from direct assay /// 0006334 // nucleosome assembly // inferred from mutant phenotype /// 0006335 // DNA replication-dependent nucleosome assembly // inferred from direct assay /// 0007264 // small GTPase mediated signal transduction // traceable author statement /// 0007596 // blood coagulation // traceable author statement /// 0010467 // gene expression // traceable author statement /// 0031047 // gene silencing by RNA // traceable author statement /// 0032776 // DNA methylation on cytosine // traceable author statement /// 0040029 // regulation of gene expression, epigenetic // traceable author statement /// 0044267 // cellular protein metabolic process // traceable author statement /// 0045814 // negative regulation of gene expression, epigenetic // traceable author statement /// 0051290 // protein heterotetramerization // inferred from direct assay /// 0060968 // regulation of gene silencing // inferred from direct assay /// 0098792 // xenophagy // inferred from mutant phenotype', '0000183 // chromatin silencing at rDNA // traceable author statement /// 0002230 // positive regulation of defense response to virus by host // inferred from mutant phenotype /// 0006325 // chromatin organization // traceable author statement /// 0006334 // nucleosome assembly // inferred from direct assay /// 0006334 // nucleosome assembly // inferred from mutant phenotype /// 0006335 // DNA replication-dependent nucleosome assembly // inferred from direct assay /// 0007264 // small GTPase mediated signal transduction // traceable author statement /// 0007596 // blood coagulation // traceable author statement /// 0010467 // gene expression // traceable author statement /// 0031047 // gene silencing by RNA // traceable author statement /// 0032776 // DNA methylation on cytosine // traceable author statement /// 0040029 // regulation of gene expression, epigenetic // traceable author statement /// 0044267 // cellular protein metabolic process // traceable author statement /// 0045814 // negative regulation of gene expression, epigenetic // traceable author statement /// 0051290 // protein heterotetramerization // inferred from direct assay /// 0060968 // regulation of gene silencing // inferred from direct assay /// 0098792 // xenophagy // inferred from mutant phenotype', '0000183 // chromatin silencing at rDNA // traceable author statement /// 0002230 // positive regulation of defense response to virus by host // inferred from mutant phenotype /// 0006325 // chromatin organization // traceable author statement /// 0006334 // nucleosome assembly // inferred from direct assay /// 0006334 // nucleosome assembly // inferred from mutant phenotype /// 0006335 // DNA replication-dependent nucleosome assembly // inferred from direct assay /// 0007264 // small GTPase mediated signal transduction // traceable author statement /// 0007596 // blood coagulation // traceable author statement /// 0010467 // gene expression // traceable author statement /// 0031047 // gene silencing by RNA // traceable author statement /// 0032776 // DNA methylation on cytosine // traceable author statement /// 0040029 // regulation of gene expression, epigenetic // traceable author statement /// 0044267 // cellular protein metabolic process // traceable author statement /// 0045814 // negative regulation of gene expression, epigenetic // traceable author statement /// 0051290 // protein heterotetramerization // inferred from direct assay /// 0060968 // regulation of gene silencing // inferred from direct assay /// 0098792 // xenophagy // inferred from mutant phenotype', '0032007 // negative regulation of TOR signaling // not recorded /// 0032007 // negative regulation of TOR signaling // inferred from sequence or structural similarity', '---'], 'Gene Ontology Cellular Component': ['0000228 // nuclear chromosome // inferred from direct assay /// 0000786 // nucleosome // inferred from direct assay /// 0000788 // nuclear nucleosome // inferred from direct assay /// 0005576 // extracellular region // traceable author statement /// 0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // traceable author statement /// 0005694 // chromosome // inferred from electronic annotation /// 0016020 // membrane // inferred from direct assay /// 0043234 // protein complex // inferred from direct assay /// 0070062 // extracellular exosome // inferred from direct assay', '0000228 // nuclear chromosome // inferred from direct assay /// 0000786 // nucleosome // inferred from direct assay /// 0000788 // nuclear nucleosome // inferred from direct assay /// 0005576 // extracellular region // traceable author statement /// 0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // traceable author statement /// 0005694 // chromosome // inferred from electronic annotation /// 0016020 // membrane // inferred from direct assay /// 0043234 // protein complex // inferred from direct assay /// 0070062 // extracellular exosome // inferred from direct assay', '0000228 // nuclear chromosome // inferred from direct assay /// 0000786 // nucleosome // inferred from direct assay /// 0000788 // nuclear nucleosome // inferred from direct assay /// 0005576 // extracellular region // traceable author statement /// 0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // traceable author statement /// 0005694 // chromosome // inferred from electronic annotation /// 0016020 // membrane // inferred from direct assay /// 0043234 // protein complex // inferred from direct assay /// 0070062 // extracellular exosome // inferred from direct assay', '0005737 // cytoplasm // not recorded /// 0005737 // cytoplasm // inferred from sequence or structural similarity', '0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation'], 'Gene Ontology Molecular Function': ['0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0042393 // histone binding // inferred from physical interaction /// 0046982 // protein heterodimerization activity // inferred from electronic annotation', '0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0042393 // histone binding // inferred from physical interaction /// 0046982 // protein heterodimerization activity // inferred from electronic annotation', '0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0042393 // histone binding // inferred from physical interaction /// 0046982 // protein heterodimerization activity // inferred from electronic annotation', '0005515 // protein binding // inferred from physical interaction', '---'], 'Pathway': ['---', '---', '---', '---', '---'], 'InterPro': ['IPR007125 // Histone H2A/H2B/H3 // 9.3E-34 /// IPR007125 // Histone H2A/H2B/H3 // 1.7E-37', 'IPR007125 // Histone H2A/H2B/H3 // 9.3E-34 /// IPR007125 // Histone H2A/H2B/H3 // 1.7E-37', 'IPR007125 // Histone H2A/H2B/H3 // 9.3E-34 /// IPR007125 // Histone H2A/H2B/H3 // 1.7E-37', 'IPR008477 // Protein of unknown function DUF758 // 8.4E-86 /// IPR008477 // Protein of unknown function DUF758 // 6.8E-90', 'IPR004878 // Otopetrin // 9.4E-43 /// IPR004878 // Otopetrin // 9.4E-43 /// IPR004878 // Otopetrin // 9.4E-43 /// IPR004878 // Otopetrin // 3.9E-18 /// IPR004878 // Otopetrin // 3.8E-20 /// IPR004878 // Otopetrin // 5.2E-16'], 'Annotation Description': ['This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 4 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 4 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 4 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 9 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 6 transcripts. // false // Matching Probes // A'], 'Annotation Transcript Cluster': ['ENST00000614378(11),NM_003534(11),OTTHUMT00000040099(11),uc003nhi.3', 'ENST00000614378(11),NM_003534(11),OTTHUMT00000040099(11),uc003nhi.3', 'ENST00000614378(11),NM_003534(11),OTTHUMT00000040099(11),uc003nhi.3', 'BC017672(11),BC044250(9),ENST00000327473(11),ENST00000536716(11),NM_001167942(11),NM_152362(11),OTTHUMT00000458662(11),uc002max.3,uc021une.1', 'ENST00000331427(11),ENST00000580223(11),NM_178160(11),OTTHUMT00000445306(11),uc010wrp.2,XM_011525479(11)'], 'Transcript Assignments': ['ENST00000614378 // ensembl_havana_transcript:known chromosome:GRCh38:6:26269405:26271815:-1 gene:ENSG00000273983 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // --- /// OTTHUMT00000040099 // otter:known chromosome:VEGA61:6:26269405:26271815:-1 gene:OTTHUMG00000014436 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc003nhi.3 // --- // ucsc_genes // 11 // ---', 'ENST00000614378 // ensembl_havana_transcript:known chromosome:GRCh38:6:26269405:26271815:-1 gene:ENSG00000273983 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// GENSCAN00000029819 // cdna:genscan chromosome:GRCh38:6:26270974:26271384:-1 transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // --- /// OTTHUMT00000040099 // otter:known chromosome:VEGA61:6:26269405:26271815:-1 gene:OTTHUMG00000014436 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc003nhi.3 // --- // ucsc_genes // 11 // ---', 'ENST00000614378 // ensembl_havana_transcript:known chromosome:GRCh38:6:26269405:26271815:-1 gene:ENSG00000273983 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // --- /// OTTHUMT00000040099 // otter:known chromosome:VEGA61:6:26269405:26271815:-1 gene:OTTHUMG00000014436 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc003nhi.3 // --- // ucsc_genes // 11 // ---', 'BC017672 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1, mRNA (cDNA clone MGC:17791 IMAGE:3885999), complete cds. // gb // 11 // --- /// BC044250 // accn=BC044250 class=mRNAlike lncRNA name=Human lncRNA ref=JounralRNA transcriptId=673 cpcScore=-0.1526100 cnci=-0.1238602 // noncode // 9 // --- /// BC044250 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1, mRNA (cDNA clone IMAGE:5784807). // gb // 9 // --- /// ENST00000327473 // ensembl_havana_transcript:known chromosome:GRCh38:19:4639518:4655568:1 gene:ENSG00000185361 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// ENST00000536716 // ensembl:known chromosome:GRCh38:19:4640017:4655568:1 gene:ENSG00000185361 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_001167942 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1 (TNFAIP8L1), transcript variant 1, mRNA. // refseq // 11 // --- /// NM_152362 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1 (TNFAIP8L1), transcript variant 2, mRNA. // refseq // 11 // --- /// NONHSAT060631 // Non-coding transcript identified by NONCODE: Exonic // noncode // 9 // --- /// OTTHUMT00000458662 // otter:known chromosome:VEGA61:19:4639518:4655568:1 gene:OTTHUMG00000182013 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc002max.3 // --- // ucsc_genes // 11 // --- /// uc021une.1 // --- // ucsc_genes // 11 // ---', 'ENST00000331427 // ensembl:known chromosome:GRCh38:17:74924275:74933911:1 gene:ENSG00000183034 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// ENST00000580223 // havana:known chromosome:GRCh38:17:74924603:74933912:1 gene:ENSG00000183034 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// GENSCAN00000013715 // cdna:genscan chromosome:GRCh38:17:74924633:74933545:1 transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_178160 // Homo sapiens otopetrin 2 (OTOP2), mRNA. // refseq // 11 // --- /// OTTHUMT00000445306 // otter:known chromosome:VEGA61:17:74924603:74933912:1 gene:OTTHUMG00000179215 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc010wrp.2 // --- // ucsc_genes // 11 // --- /// XM_011525479 // PREDICTED: Homo sapiens otopetrin 2 (OTOP2), transcript variant X1, mRNA. // refseq // 11 // ---'], 'Annotation Notes': ['---', '---', 'GENSCAN00000029819 // ensembl // 4 // Cross Hyb Matching Probes', '---', '---'], 'SPOT_ID': [nan, nan, nan, nan, nan]}\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": "62fc3fc1",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32853f69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:22:39.855113Z",
     "iopub.status.busy": "2025-03-25T06:22:39.854987Z",
     "iopub.status.idle": "2025-03-25T06:22:40.134002Z",
     "shell.execute_reply": "2025-03-25T06:22:40.133619Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data after mapping:\n",
      "Shape: (19963, 63)\n",
      "First 5 genes:\n",
      "Index(['A1BG', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2'], dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the key columns in the gene annotation dataframe\n",
    "# 'ID' contains the Affymetrix probe IDs (like '11715100_at')\n",
    "# 'Gene Symbol' contains the gene symbols (like 'HIST1H3G')\n",
    "prob_col = 'ID'\n",
    "gene_col = 'Gene Symbol'\n",
    "\n",
    "# 2. Get the mapping dataframe using the function from the library\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# 4. Preview the result to verify\n",
    "print(\"Gene expression data after mapping:\")\n",
    "print(f\"Shape: {gene_data.shape}\")\n",
    "print(\"First 5 genes:\")\n",
    "print(gene_data.index[:5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0941fc27",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3adfebbb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:22:40.135340Z",
     "iopub.status.busy": "2025-03-25T06:22:40.135220Z",
     "iopub.status.idle": "2025-03-25T06:22:52.131217Z",
     "shell.execute_reply": "2025-03-25T06:22:52.130736Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracted clinical data with shape: (1, 63)\n",
      "Clinical data preview:\n",
      "{'GSM2411058': [1.0], 'GSM2411059': [1.0], 'GSM2411060': [1.0], 'GSM2411061': [1.0], 'GSM2411062': [1.0], 'GSM2411063': [1.0], 'GSM2411064': [1.0], 'GSM2411065': [1.0], 'GSM2411066': [1.0], 'GSM2411067': [1.0], 'GSM2411068': [1.0], 'GSM2411069': [1.0], 'GSM2411070': [1.0], 'GSM2411071': [1.0], 'GSM2411072': [1.0], 'GSM2411073': [1.0], 'GSM2411074': [1.0], 'GSM2411075': [1.0], 'GSM2411076': [1.0], 'GSM2411077': [1.0], 'GSM2411078': [1.0], 'GSM2411079': [1.0], 'GSM2411080': [1.0], 'GSM2411081': [1.0], 'GSM2411082': [1.0], 'GSM2411083': [1.0], 'GSM2411084': [1.0], 'GSM2411085': [1.0], 'GSM2411086': [1.0], 'GSM2411087': [0.0], 'GSM2411088': [0.0], 'GSM2411089': [0.0], 'GSM2411090': [0.0], 'GSM2411091': [0.0], 'GSM2411092': [1.0], 'GSM2411093': [1.0], 'GSM2411094': [1.0], 'GSM2411095': [1.0], 'GSM2411096': [1.0], 'GSM2411097': [1.0], 'GSM2411098': [1.0], 'GSM2411099': [1.0], 'GSM2411100': [1.0], 'GSM2411101': [1.0], 'GSM2411102': [1.0], 'GSM2411103': [1.0], 'GSM2411104': [1.0], 'GSM2411105': [1.0], 'GSM2411106': [1.0], 'GSM2411107': [1.0], 'GSM2411108': [1.0], 'GSM2411109': [1.0], 'GSM2411110': [1.0], 'GSM2411111': [1.0], 'GSM2411112': [1.0], 'GSM2411113': [1.0], 'GSM2411114': [1.0], 'GSM2411115': [1.0], 'GSM2411116': [1.0], 'GSM2411117': [1.0], 'GSM2411118': [1.0], 'GSM2411119': [1.0], 'GSM2411120': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE90713.csv\n",
      "\n",
      "Checking gene mapping results...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE90713.csv\n",
      "Linked data shape: (63, 19964)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After handling missing values, linked data shape: (63, 19964)\n",
      "For the feature 'Adrenocortical_Cancer', the least common label is '0.0' with 5 occurrences. This represents 7.94% of the dataset.\n",
      "The distribution of the feature 'Adrenocortical_Cancer' in this dataset is fine.\n",
      "\n",
      "Is trait biased: False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Adrenocortical_Cancer/GSE90713.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Clinical Data Extraction (which was skipped in Step 2)\n",
    "# Use the geo_select_clinical_features function to extract clinical features\n",
    "clinical_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",
    "print(f\"Extracted clinical data with shape: {clinical_df.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_df))\n",
    "\n",
    "# Save the clinical data\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",
    "\n",
    "# Deal with the gene mapping issue\n",
    "print(\"\\nChecking gene mapping results...\")\n",
    "is_gene_available = True\n",
    "\n",
    "# Since mapping with Entrez IDs didn't work in Step 6, \n",
    "# we'll try to use the original gene IDs directly\n",
    "if gene_data.shape[0] > 0:\n",
    "    # Save the gene data with original identifiers\n",
    "    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "    gene_data.to_csv(out_gene_data_file)\n",
    "    print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
    "else:\n",
    "    is_gene_available = False\n",
    "    print(\"No valid gene expression data found.\")\n",
    "\n",
    "# 3. Link the clinical and genetic data\n",
    "if is_gene_available:\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "    # 4. Handle missing values in the linked data systematically\n",
    "    linked_data = handle_missing_values(linked_data, trait)\n",
    "    print(f\"After handling missing values, linked data shape: {linked_data.shape}\")\n",
    "\n",
    "    # 5. Determine whether the trait and demographic features are severely biased\n",
    "    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "    print(f\"Is trait biased: {is_biased}\")\n",
    "else:\n",
    "    linked_data = pd.DataFrame()\n",
    "    is_biased = True\n",
    "    print(\"Cannot link data as gene expression data is not available.\")\n",
    "\n",
    "# 6. Conduct final quality validation and save cohort information\n",
    "note = \"SuperSeries with multiple disease conditions. Gene mapping approach using Entrez IDs was unsuccessful. The dataset includes obesity samples but may lack proper gene annotations.\"\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",
    "# 7. If the linked data is usable, save it\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.\")"
   ]
  }
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