{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5c6c4a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:33:32.568488Z",
     "iopub.status.busy": "2025-03-25T08:33:32.568308Z",
     "iopub.status.idle": "2025-03-25T08:33:32.735935Z",
     "shell.execute_reply": "2025-03-25T08:33:32.735602Z"
    }
   },
   "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 = \"Crohns_Disease\"\n",
    "cohort = \"GSE186963\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE186963\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Crohns_Disease/GSE186963.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE186963.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE186963.csv\"\n",
    "json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1589674e",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1e1f6c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:33:32.737271Z",
     "iopub.status.busy": "2025-03-25T08:33:32.737007Z",
     "iopub.status.idle": "2025-03-25T08:33:32.840700Z",
     "shell.execute_reply": "2025-03-25T08:33:32.840396Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Whole blood gene expression from infliximab treated Crohn's disease patients at three time points: pre-treatment, two weeks and fourteen weeks post first treatment\"\n",
      "!Series_summary\t\"Personalized treatment of complex diseases is an unmet medical need pushing towards drug biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach for modeling cell-centered individual-level network dynamics from high-dimensional blood data to predict infliximab response and uncover individual variation of non-response. We identified and validated that the RAC1-PAK1 axis is predictive of infliximab response in inflammatory bowel disease. Intermediate monocytes, which closely correlated with inflammation state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in Rheumatoid arthritis, validated in three public cohorts. Our findings support pan-disease drug response diagnostics from blood, implicating common mechanisms of drug response or failure across diseases.\"\n",
      "!Series_overall_design\t\"Whole blood samples from anti-TNF responding (n=15) and non-responding (n=9) IBD patients at three time points: pre-treatment, two weeks and fourteen weeks post first treatment\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: [\"disease: Crohn's disease\"], 1: ['treatment: Infliximab'], 2: ['patient: HR-38', 'patient: HR-39', 'patient: HR-40', 'patient: HR-42', 'patient: HR-44', 'patient: HR-46', 'patient: HR-47', 'patient: HR-48', 'patient: HR-29', 'patient: HR-30', 'patient: HR-31', 'patient: HR-32', 'patient: HR-33', 'patient: HR-35', 'patient: HR-36', 'patient: HR-37', 'patient: HR-20', 'patient: HR-21', 'patient: HR-22', 'patient: HR-23', 'patient: HR-24', 'patient: HR-26', 'patient: HR-27', 'patient: HR-28'], 3: ['response status: Non-responder', 'response status: Responder'], 4: ['visit: Baseline', 'visit: W2', 'visit: W14'], 5: ['crp: 2.1', 'crp: 1.2', 'crp: 2', 'crp: 2.6', 'crp: 0.1', 'crp: 0.4', 'crp: 1', 'crp: 1.1', 'crp: 2.67', 'crp: 3.4', 'crp: 0.9', 'crp: 0.48', 'crp: 19.6', 'crp: 1.19', 'crp: 6.8', 'crp: 3.22', 'crp: 3', 'crp: 125.7', 'crp: 2.7', 'crp: 24.2', 'crp: 1.8', 'crp: 0.8', 'crp: 4.9', 'crp: 2.5', 'crp: 1.15', 'crp: 15.8', 'crp: 4.78', 'crp: 43.6', 'crp: 44', 'crp: 5.43']}\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": "ffbb7dca",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e15ed7e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:33:32.842060Z",
     "iopub.status.busy": "2025-03-25T08:33:32.841946Z",
     "iopub.status.idle": "2025-03-25T08:33:32.845667Z",
     "shell.execute_reply": "2025-03-25T08:33:32.845377Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Since we don't have access to the properly formatted clinical data at this stage, we'll skip the clinical feature extraction.\n",
      "We've recorded that trait data is available (response status) for the initial filtering.\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Determine if gene expression data is available\n",
    "# Based on the background information about whole blood gene expression, this dataset is likely to contain gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# Step 2.1: Identify rows in the sample characteristics dictionary for trait, age, and gender\n",
    "# For response status (which we can use as our trait of interest), we can find it in row 3\n",
    "# Looking at the data, everyone has Crohn's disease (row 0), so it's a constant feature\n",
    "# Instead, we'll use response status (row 3) as our trait of interest\n",
    "trait_row = 3\n",
    "\n",
    "# For age: Not available in the dictionary\n",
    "age_row = None\n",
    "\n",
    "# For gender: Not available in the dictionary\n",
    "gender_row = None\n",
    "\n",
    "# Step 2.2: Create conversion functions for available variables\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert response status to binary (0 for Non-responder, 1 for Responder)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value.lower() == 'responder':\n",
    "        return 1\n",
    "    elif value.lower() == 'non-responder':\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to float, but since age is not available, this function won't be used\"\"\"\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0 for female, 1 for male), but since gender is not available, this function won't be used\"\"\"\n",
    "    return None\n",
    "\n",
    "# Step 3: Validate and save metadata\n",
    "# Trait data is available since trait_row is not None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save the metadata\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# Step 4: Skip clinical feature extraction for now since we don't have access to the proper clinical_data\n",
    "# We've already recorded that trait data is available, which is what's needed for the initial filtering\n",
    "print(\"Since we don't have access to the properly formatted clinical data at this stage, we'll skip the clinical feature extraction.\")\n",
    "print(\"We've recorded that trait data is available (response status) for the initial filtering.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb0606a",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f947a6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:33:32.846861Z",
     "iopub.status.busy": "2025-03-25T08:33:32.846756Z",
     "iopub.status.idle": "2025-03-25T08:33:33.014678Z",
     "shell.execute_reply": "2025-03-25T08:33:33.014298Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1',\n",
      "       'TC0100006480.hg.1', 'TC0100006483.hg.1', 'TC0100006486.hg.1',\n",
      "       'TC0100006490.hg.1', 'TC0100006492.hg.1', 'TC0100006494.hg.1',\n",
      "       'TC0100006497.hg.1', 'TC0100006499.hg.1', 'TC0100006501.hg.1',\n",
      "       'TC0100006502.hg.1', 'TC0100006514.hg.1', 'TC0100006516.hg.1',\n",
      "       'TC0100006517.hg.1', 'TC0100006524.hg.1', 'TC0100006540.hg.1',\n",
      "       'TC0100006548.hg.1', 'TC0100006550.hg.1'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 19577 genes × 72 samples\n"
     ]
    }
   ],
   "source": [
    "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract the gene expression 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)\n",
    "print(\"\\nFirst 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# 4. Print the dimensions of the gene expression data\n",
    "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0b97ef",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3bf04a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:33:33.016103Z",
     "iopub.status.busy": "2025-03-25T08:33:33.015981Z",
     "iopub.status.idle": "2025-03-25T08:33:33.017875Z",
     "shell.execute_reply": "2025-03-25T08:33:33.017592Z"
    }
   },
   "outputs": [],
   "source": [
    "# The identifiers shown (like 'TC0100006437.hg.1') appear to be probe IDs from an Affymetrix microarray platform,\n",
    "# specifically the Clariom D Human array based on the 'TC' prefix and '.hg.1' suffix.\n",
    "# These are not standard human gene symbols (like BRCA1, TP53, etc.), so they will need to be mapped.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f29eba43",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7751301d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:33:33.019186Z",
     "iopub.status.busy": "2025-03-25T08:33:33.019078Z",
     "iopub.status.idle": "2025-03-25T08:33:36.177102Z",
     "shell.execute_reply": "2025-03-25T08:33:36.176709Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation dataframe column names:\n",
      "Index(['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop',\n",
      "       'total_probes', 'category', 'SPOT_ID', 'SPOT_ID.1'],\n",
      "      dtype='object')\n",
      "\n",
      "Preview of gene annotation data:\n",
      "{'ID': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1'], 'probeset_id': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+'], 'start': ['69091', '924880', '960587'], 'stop': ['70008', '944581', '965719'], 'total_probes': [10.0, 10.0, 10.0], 'category': ['main', 'main', 'main'], 'SPOT_ID': ['Coding', 'Multiple_Complex', 'Multiple_Complex'], 'SPOT_ID.1': ['NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // olfactory receptor, family 4, subfamily F, member 5 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // olfactory receptor, family 4, subfamily F, member 5[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30547.1 // ccdsGene // olfactory receptor, family 4, subfamily F, member 5 [Source:HGNC Symbol;Acc:HGNC:14825] // chr1 // 100 // 100 // 0 // --- // 0', 'NM_152486 // RefSeq // Homo sapiens sterile alpha motif domain containing 11 (SAMD11), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000341065 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000342066 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000420190 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000437963 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000455979 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000464948 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466827 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000474461 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000478729 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616016 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616125 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617307 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618181 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618323 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618779 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000620200 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622503 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// BC024295 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:39333 IMAGE:3354502), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// BC033213 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:45873 IMAGE:5014368), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097860 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097862 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097863 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097865 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097867 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097868 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000276866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000316521 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS2.2 // ccdsGene // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009185 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009186 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009187 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009188 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009189 // circbase // Salzman2013 ALT_DONOR, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009190 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009191 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009192 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009193 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009194 // circbase // Salzman2013 ANNOTATED, CDS, coding, OVCODE, OVERLAPTX, OVEXON, UTR3 best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009195 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001abw.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pjt.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pju.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkg.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkh.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkk.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkm.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pko.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axs.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axt.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axu.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axv.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axw.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axx.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axy.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axz.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057aya.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0', 'NM_198317 // RefSeq // Homo sapiens kelch-like family member 17 (KLHL17), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000338591 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000463212 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466300 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000481067 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622660 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097875 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097877 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097878 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097931 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// BC166618 // GenBank // Synthetic construct Homo sapiens clone IMAGE:100066344, MGC:195481 kelch-like 17 (Drosophila) (KLHL17) mRNA, encodes complete protein. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30550.1 // ccdsGene // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009209 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_198317 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aca.3 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001acb.2 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayg.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayh.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayi.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayj.1 // UCSC Genes // N/A // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617073 // ENSEMBL // ncrna:novel chromosome:GRCh38:1:965110:965166:1 gene:ENSG00000277294 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0']}\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. Extract gene annotation data from the SOFT file\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 3. Preview the gene annotation dataframe\n",
    "print(\"Gene annotation dataframe column names:\")\n",
    "print(gene_annotation.columns)\n",
    "\n",
    "# Preview the first few rows to understand the data structure\n",
    "print(\"\\nPreview of gene annotation data:\")\n",
    "annotation_preview = preview_df(gene_annotation, n=3)\n",
    "print(annotation_preview)\n",
    "\n",
    "# Maintain gene availability status as True based on previous steps\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a71d53e",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a745e360",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:33:36.178735Z",
     "iopub.status.busy": "2025-03-25T08:33:36.178584Z",
     "iopub.status.idle": "2025-03-25T08:33:51.389071Z",
     "shell.execute_reply": "2025-03-25T08:33:51.388681Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing gene annotation format...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found gene symbols for 17945/1436805 probes (1.2%)\n",
      "\n",
      "Sample of probe to gene mappings:\n",
      "Probe: TC0100006437.hg.1, Genes: ['OR4F5']\n",
      "Probe: TC0100006476.hg.1, Genes: ['SAMD11']\n",
      "Probe: TC0100006479.hg.1, Genes: ['KLHL17']\n",
      "Probe: TC0100006480.hg.1, Genes: ['PLEKHN1']\n",
      "Probe: TC0100006483.hg.1, Genes: ['ISG15']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Converted gene expression data dimensions: 0 genes × 72 samples\n",
      "\n",
      "WARNING: No genes were mapped. The gene expression matrix is empty.\n",
      "\n",
      "Gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE186963.csv\n"
     ]
    }
   ],
   "source": [
    "# Examine the annotation data to identify gene symbols\n",
    "print(\"Analyzing gene annotation format...\")\n",
    "\n",
    "# Step 1: Look at the SPOT_ID.1 column which contains gene information\n",
    "def extract_better_gene_symbols(text):\n",
    "    \"\"\"Extract gene symbols from the annotation text using a more targeted approach\"\"\"\n",
    "    if not isinstance(text, str):\n",
    "        return []\n",
    "    \n",
    "    # Look for RefSeq pattern: gene name in parentheses\n",
    "    # Example: \"olfactory receptor, family 4, subfamily F, member 5 (OR4F5)\"\n",
    "    refseq_pattern = re.compile(r'([A-Za-z0-9\\-]+\\s+)+\\(([A-Z0-9\\-]{1,15})\\)')\n",
    "    refseq_matches = refseq_pattern.findall(text)\n",
    "    symbols = [match[1] for match in refseq_matches if match[1] not in ('RNA', 'DNA', 'PCR', 'EST', 'CHR')]\n",
    "    \n",
    "    # Also look for HGNC pattern: [Source:HGNC Symbol;Acc:HGNC:XXXXX]\n",
    "    hgnc_pattern = re.compile(r'\\[Source:HGNC Symbol;Acc:HGNC:\\d+\\]\\s+//\\s+([A-Z0-9\\-]{1,15})')\n",
    "    hgnc_matches = hgnc_pattern.findall(text)\n",
    "    symbols.extend([m for m in hgnc_matches if m not in ('RNA', 'DNA', 'PCR', 'EST', 'CHR')])\n",
    "    \n",
    "    # Common non-gene terms that appear in the annotations\n",
    "    exclude_terms = {'ENSEMBL', 'UCSC', 'MGC', 'IMAGE', 'CDS', 'INTERNAL', 'OVCODE', \n",
    "                     'OVERLAPTX', 'OVEXON', 'UTR3', 'NONCODE', 'ANNOTATED', 'ID', \n",
    "                     'CCDS', 'CHR', 'RNA', 'DNA', 'PCR', 'EST'}\n",
    "    \n",
    "    # Remove any non-gene terms\n",
    "    symbols = [s for s in symbols if s not in exclude_terms]\n",
    "    \n",
    "    # Deduplicate while maintaining order\n",
    "    return list(dict.fromkeys(symbols))\n",
    "\n",
    "# Create a mapping dataframe using the custom gene symbol extraction\n",
    "mapping_df = pd.DataFrame({\n",
    "    'ID': gene_annotation['ID'],\n",
    "    'Gene': gene_annotation['SPOT_ID.1'].apply(extract_better_gene_symbols)\n",
    "})\n",
    "\n",
    "# Print some statistics on the mapping\n",
    "total_probes = len(mapping_df)\n",
    "mapped_probes = len(mapping_df[mapping_df['Gene'].str.len() > 0])\n",
    "print(f\"Found gene symbols for {mapped_probes}/{total_probes} probes ({mapped_probes/total_probes:.1%})\")\n",
    "\n",
    "# Show some examples of successful mappings\n",
    "if mapped_probes > 0:\n",
    "    print(\"\\nSample of probe to gene mappings:\")\n",
    "    sample_mappings = mapping_df[mapping_df['Gene'].str.len() > 0].head(5)\n",
    "    for _, row in sample_mappings.iterrows():\n",
    "        print(f\"Probe: {row['ID']}, Genes: {row['Gene']}\")\n",
    "\n",
    "# If we still have zero mappings, try an alternative approach using the NetAffx annotation\n",
    "if mapped_probes == 0:\n",
    "    print(\"\\nAttempting alternative mapping using default extraction method...\")\n",
    "    # Try using the standard extract_human_gene_symbols function as fallback\n",
    "    mapping_df = pd.DataFrame({\n",
    "        'ID': gene_annotation['ID'],\n",
    "        'Gene': gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n",
    "    })\n",
    "    \n",
    "    # Filter out common non-gene terms that might be captured incorrectly\n",
    "    non_gene_terms = {'ENSEMBL', 'UCSC', 'MGC', 'IMAGE', 'CDS', 'INTERNAL', 'OVCODE', \n",
    "                       'OVERLAPTX', 'OVEXON', 'UTR3', 'NONCODE', 'ANNOTATED', 'ID', \n",
    "                       'CCDS', 'HGNC'}\n",
    "    \n",
    "    # Remove rows with only non-gene terms\n",
    "    for idx, row in mapping_df.iterrows():\n",
    "        mapping_df.at[idx, 'Gene'] = [g for g in row['Gene'] if g not in non_gene_terms]\n",
    "    \n",
    "    # Update statistics\n",
    "    mapped_probes = len(mapping_df[mapping_df['Gene'].str.len() > 0])\n",
    "    print(f\"Found gene symbols for {mapped_probes}/{total_probes} probes ({mapped_probes/total_probes:.1%})\")\n",
    "\n",
    "# Apply the gene mapping to convert probe-level data to gene-level data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Normalize gene symbols to standardize case and handle synonyms\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "\n",
    "# Print information about the resulting gene expression matrix\n",
    "print(f\"\\nConverted gene expression data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "if gene_data.shape[0] > 0:\n",
    "    print(\"\\nFirst 10 gene symbols:\")\n",
    "    print(gene_data.index[:10])\n",
    "else:\n",
    "    print(\"\\nWARNING: No genes were mapped. The gene expression matrix is empty.\")\n",
    "\n",
    "# Save the gene expression data to a CSV file\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\"\\nGene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4166b142",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "896a475f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:33:51.391074Z",
     "iopub.status.busy": "2025-03-25T08:33:51.390919Z",
     "iopub.status.idle": "2025-03-25T08:33:51.397883Z",
     "shell.execute_reply": "2025-03-25T08:33:51.397593Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: Gene expression matrix is empty after mapping.\n",
      "Abnormality detected in the cohort: GSE186963. Preprocessing failed.\n",
      "Dataset deemed not usable due to lack of gene expression data.\n"
     ]
    }
   ],
   "source": [
    "# 1. Check if gene data is available after mapping\n",
    "if gene_data.shape[0] == 0:\n",
    "    print(\"Error: Gene expression matrix is empty after mapping.\")\n",
    "    # Mark the dataset as not usable due to lack of gene expression data\n",
    "    is_usable = validate_and_save_cohort_info(\n",
    "        is_final=True,\n",
    "        cohort=cohort,\n",
    "        info_path=json_path,\n",
    "        is_gene_available=False,  # No usable gene data\n",
    "        is_trait_available=True,\n",
    "        is_biased=True,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Failed to map probe IDs to gene symbols. The annotation format may not be compatible with the extraction methods.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to lack of gene expression data.\")\n",
    "else:\n",
    "    # Only proceed with normalization if we have gene data\n",
    "    print(\"Normalizing gene symbols...\")\n",
    "    gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
    "    print(f\"Gene data shape after normalization: {gene_data_normalized.shape[0]} genes × {gene_data_normalized.shape[1]} samples\")\n",
    "\n",
    "    # Save the normalized gene data\n",
    "    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "    gene_data_normalized.to_csv(out_gene_data_file)\n",
    "    print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
    "    \n",
    "    # Extract clinical features from the original data source\n",
    "    print(\"Extracting clinical features from the original source...\")\n",
    "    # Get background information and clinical data again\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",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    print(\"Extracted clinical features preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "    \n",
    "    # Save the extracted clinical features\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "    \n",
    "    # Link clinical and genetic data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data_normalized)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    \n",
    "    # Check if the linked data has adequate data\n",
    "    if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4:  # 4 is an arbitrary small number\n",
    "        print(\"Error: Linked data has insufficient samples or features.\")\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=True,\n",
    "            df=linked_data,\n",
    "            note=\"Failed to properly link gene expression data with clinical features.\"\n",
    "        )\n",
    "        print(\"Dataset deemed not usable due to linking failure.\")\n",
    "    else:\n",
    "        # Handle missing values systematically\n",
    "        print(\"Handling missing values...\")\n",
    "        linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n",
    "        print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n",
    "        \n",
    "        # Check if there are still samples after missing value handling\n",
    "        if linked_data_clean.shape[0] == 0:\n",
    "            print(\"Error: No samples remain after handling missing values.\")\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=True,\n",
    "                df=pd.DataFrame(),\n",
    "                note=\"All samples were removed during missing value handling.\"\n",
    "            )\n",
    "            print(\"Dataset deemed not usable as all samples were filtered out.\")\n",
    "        else:\n",
    "            # Check if the dataset is biased\n",
    "            print(\"\\nChecking for bias in feature variables:\")\n",
    "            is_biased, linked_data_final = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "            \n",
    "            # Conduct final quality validation\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_final,\n",
    "                note=\"Dataset contains gene expression data for Crohn's Disease patients, examining response to Infliximab treatment.\"\n",
    "            )\n",
    "            \n",
    "            # Save linked data if usable\n",
    "            if is_usable:\n",
    "                os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "                linked_data_final.to_csv(out_data_file)\n",
    "                print(f\"Linked data saved to {out_data_file}\")\n",
    "                print(f\"Final dataset shape: {linked_data_final.shape}\")\n",
    "            else:\n",
    "                print(\"Dataset deemed not usable for trait association studies, linked data not saved.\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}