{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fab1f182",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:30:23.199213Z",
     "iopub.status.busy": "2025-03-25T06:30:23.198707Z",
     "iopub.status.idle": "2025-03-25T06:30:23.365181Z",
     "shell.execute_reply": "2025-03-25T06:30:23.364843Z"
    }
   },
   "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 = \"Ankylosing_Spondylitis\"\n",
    "cohort = \"GSE25101\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Ankylosing_Spondylitis\"\n",
    "in_cohort_dir = \"../../input/GEO/Ankylosing_Spondylitis/GSE25101\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Ankylosing_Spondylitis/GSE25101.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Ankylosing_Spondylitis/gene_data/GSE25101.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Ankylosing_Spondylitis/clinical_data/GSE25101.csv\"\n",
    "json_path = \"../../output/preprocess/Ankylosing_Spondylitis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b318b9",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26ee1954",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:30:23.366608Z",
     "iopub.status.busy": "2025-03-25T06:30:23.366461Z",
     "iopub.status.idle": "2025-03-25T06:30:23.433821Z",
     "shell.execute_reply": "2025-03-25T06:30:23.433524Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Expression profiling in whole blood in ankylosing spondylitis patients and controls\"\n",
      "!Series_summary\t\"Introduction: A number of genetic-association studies have identified genes contributing to AS susceptibility but such approaches provide little information as to the gene activity changes occurring during the disease process. Transcriptional profiling generates a “snapshot” of the sampled cells activity and thus can provide insights into the molecular processes driving the disease process. We undertook a whole-genome microarray approach to identify candidate genes associated with AS and validated these gene-expression changes in a larger sample cohort.  Methods: 18 active AS patients, classified according to the New York criteria. and 18 gender-and age-matched controls were profiled using Illumina HT-12 Whole-Genome Expression BeadChips which carry cDNAs for 48000 genes and transcripts. Class comparison analysis identified a number of differentially expressed candidate genes. These candidate genes were then validated in a larger cohort using qPCR-based TaqMan Low Density Arrays (TLDAs).  Results: 239 probes corresponding to 221 genes were identified as being significantly different between patients and controls with a p-value <0.0005 (80% confidence level of false discovery rate). Forty seven genes were then selected for validation studies, using the TLDAs. Thirteen of these genes were validated in the second patient cohort with 12 down-regulated 1.3-2-fold and only 1 upregulated (1.6-fold). Among a number of identified genes with well-documented inflammatory roles we also validated genes that might be of great interest to the understanding of AS progression such as SPOCK2 (osteonectin) and EP300 which modulate cartilage and bone metabolism.  Conclusion: We have validated a gene expression signature for AS from whole blood and identified strong candidate genes that may play roles in both the inflammatory and joint destruction aspects of the disease.\"\n",
      "!Series_overall_design\t\"RNA was extracted from whole blood using PAXGene tubes. 16 AS patients with active disease and 16 gender- and age-matched controls were analysed.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: Whole blood'], 1: ['cell type: PBMC'], 2: ['disease status: Ankylosing spondylitis patient', 'disease status: Normal control']}\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": "3d431616",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "048b5bc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:30:23.434904Z",
     "iopub.status.busy": "2025-03-25T06:30:23.434796Z",
     "iopub.status.idle": "2025-03-25T06:30:23.442035Z",
     "shell.execute_reply": "2025-03-25T06:30:23.441749Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical data:\n",
      "{'GSM616668': [1.0], 'GSM616669': [1.0], 'GSM616670': [1.0], 'GSM616671': [1.0], 'GSM616672': [1.0], 'GSM616673': [1.0], 'GSM616674': [1.0], 'GSM616675': [1.0], 'GSM616676': [1.0], 'GSM616677': [1.0], 'GSM616678': [1.0], 'GSM616679': [1.0], 'GSM616680': [1.0], 'GSM616681': [1.0], 'GSM616682': [1.0], 'GSM616683': [1.0], 'GSM616684': [0.0], 'GSM616685': [0.0], 'GSM616686': [0.0], 'GSM616687': [0.0], 'GSM616688': [0.0], 'GSM616689': [0.0], 'GSM616690': [0.0], 'GSM616691': [0.0], 'GSM616692': [0.0], 'GSM616693': [0.0], 'GSM616694': [0.0], 'GSM616695': [0.0], 'GSM616696': [0.0], 'GSM616697': [0.0], 'GSM616698': [0.0], 'GSM616699': [0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Ankylosing_Spondylitis/clinical_data/GSE25101.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# From the background information, this is a whole-genome microarray study\n",
    "# using Illumina HT-12 Whole-Genome Expression BeadChips which carry cDNAs for 48000 genes\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Clinical Feature Extraction and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Looking at the sample characteristics dictionary:\n",
    "# Key 2 contains disease status which relates to our trait (Ankylosing_Spondylitis)\n",
    "trait_row = 2\n",
    "\n",
    "# No age information is available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# No gender information is available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert Ankylosing Spondylitis disease status to binary values.\n",
    "    0 = Normal control (no disease)\n",
    "    1 = Ankylosing spondylitis patient (has disease)\n",
    "    \"\"\"\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if \"ankylosing spondylitis patient\" in value.lower():\n",
    "        return 1\n",
    "    elif \"normal control\" in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value: str) -> float:\n",
    "    \"\"\"\n",
    "    Convert age to continuous value.\n",
    "    Not used in this dataset as age information is not available.\n",
    "    \"\"\"\n",
    "    return None\n",
    "\n",
    "def convert_gender(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert gender to binary value.\n",
    "    Not used in this dataset as gender information is not available.\n",
    "    \"\"\"\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save initial validation information\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 (if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # Create a directory for the clinical data if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Assuming clinical_data is already loaded in the environment from a previous step\n",
    "    # If not, we'll need to skip this part\n",
    "    try:\n",
    "        # Extract clinical features\n",
    "        selected_clinical_df = geo_select_clinical_features(\n",
    "            clinical_df=clinical_data,  # Use the variable from the environment\n",
    "            trait=trait,\n",
    "            trait_row=trait_row,\n",
    "            convert_trait=convert_trait,\n",
    "            age_row=age_row,\n",
    "            convert_age=convert_age,\n",
    "            gender_row=gender_row,\n",
    "            convert_gender=convert_gender\n",
    "        )\n",
    "        \n",
    "        # Preview the extracted clinical data\n",
    "        preview = preview_df(selected_clinical_df)\n",
    "        print(\"Preview of selected clinical data:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Save the clinical data\n",
    "        selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    except NameError:\n",
    "        print(\"Clinical data not found in the environment. This step will be handled in a subsequent processing stage.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a92a18",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2787cc9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:30:23.442994Z",
     "iopub.status.busy": "2025-03-25T06:30:23.442888Z",
     "iopub.status.idle": "2025-03-25T06:30:23.520421Z",
     "shell.execute_reply": "2025-03-25T06:30:23.520099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651209', 'ILMN_1651228',\n",
      "       'ILMN_1651229', 'ILMN_1651232', 'ILMN_1651237', 'ILMN_1651254',\n",
      "       'ILMN_1651262', 'ILMN_1651268', 'ILMN_1651278', 'ILMN_1651282',\n",
      "       'ILMN_1651286', 'ILMN_1651296', 'ILMN_1651315', 'ILMN_1651316',\n",
      "       'ILMN_1651328', 'ILMN_1651336', 'ILMN_1651341', 'ILMN_1651346'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 18168 genes × 32 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": "6101e2af",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "13ff3911",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:30:23.521619Z",
     "iopub.status.busy": "2025-03-25T06:30:23.521502Z",
     "iopub.status.idle": "2025-03-25T06:30:23.523340Z",
     "shell.execute_reply": "2025-03-25T06:30:23.523067Z"
    }
   },
   "outputs": [],
   "source": [
    "# Analyzing the gene identifiers from the previous step\n",
    "# These are Illumina BeadArray identifiers (ILMN_) which are probe IDs, not gene symbols\n",
    "# They need to be mapped to human gene symbols for proper biological interpretation\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c62367",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "609cdda0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:30:23.524420Z",
     "iopub.status.busy": "2025-03-25T06:30:23.524314Z",
     "iopub.status.idle": "2025-03-25T06:30:25.018442Z",
     "shell.execute_reply": "2025-03-25T06:30:25.018072Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['ILMN_1725881', 'ILMN_1910180', 'ILMN_1804174', 'ILMN_1796063', 'ILMN_1811966'], 'nuID': ['rp13_p1x6D80lNLk3c', 'NEX0oqCV8.er4HVfU4', 'KyqQynMZxJcruyylEU', 'xXl7eXuF7sbPEp.KFI', '9ckqJrioiaej9_ajeQ'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Source': ['RefSeq', 'Unigene', 'RefSeq', 'RefSeq', 'RefSeq'], 'Search_Key': ['ILMN_44919', 'ILMN_127219', 'ILMN_139282', 'ILMN_5006', 'ILMN_38756'], 'Transcript': ['ILMN_44919', 'ILMN_127219', 'ILMN_139282', 'ILMN_5006', 'ILMN_38756'], 'ILMN_Gene': ['LOC23117', 'HS.575038', 'FCGR2B', 'TRIM44', 'LOC653895'], 'Source_Reference_ID': ['XM_933824.1', 'Hs.575038', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'RefSeq_ID': ['XM_933824.1', nan, 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'Unigene_ID': [nan, 'Hs.575038', nan, nan, nan], 'Entrez_Gene_ID': [23117.0, nan, 2213.0, 54765.0, 653895.0], 'GI': [89040007.0, 10437021.0, 88952550.0, 29029528.0, 89033487.0], 'Accession': ['XM_933824.1', 'AK024680', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'Symbol': ['LOC23117', nan, 'FCGR2B', 'TRIM44', 'LOC653895'], 'Protein_Product': ['XP_938917.1', nan, 'XP_943944.1', 'NP_060053.2', 'XP_941472.1'], 'Array_Address_Id': [1710221.0, 5900364.0, 2480717.0, 1300239.0, 4480719.0], 'Probe_Type': ['I', 'S', 'I', 'S', 'S'], 'Probe_Start': [122.0, 1409.0, 1643.0, 2901.0, 25.0], 'SEQUENCE': ['GGCTCCTCTTTGGGCTCCTACTGGAATTTATCAGCCATCAGTGCATCTCT', 'ACACCTTCAGGAGGGAAGCCCTTATTTCTGGGTTGAACTCCCCTTCCATG', 'TAGGGGCAATAGGCTATACGCTACAGCCTAGGTGTGTAGTAGGCCACACC', 'CCTGCCTGTCTGCCTGTGACCTGTGTACGTATTACAGGCTTTAGGACCAG', 'CTAGCAGGGAGCGGTGAGGGAGAGCGGCTGGATTTCTTGCGGGATCTGCA'], 'Chromosome': ['16', nan, nan, '11', nan], 'Probe_Chr_Orientation': ['-', nan, nan, '+', nan], 'Probe_Coordinates': ['21766363-21766363:21769901-21769949', nan, nan, '35786070-35786119', nan], 'Cytoband': ['16p12.2a', nan, '1q23.3b', '11p13a', '10q11.23b'], 'Definition': ['PREDICTED: Homo sapiens KIAA0220-like protein, transcript variant 11 (LOC23117), mRNA.', 'Homo sapiens cDNA: FLJ21027 fis, clone CAE07110', 'PREDICTED: Homo sapiens Fc fragment of IgG, low affinity IIb, receptor (CD32) (FCGR2B), mRNA.', 'Homo sapiens tripartite motif-containing 44 (TRIM44), mRNA.', 'PREDICTED: Homo sapiens similar to protein geranylgeranyltransferase type I, beta subunit (LOC653895), mRNA.'], 'Ontology_Component': [nan, nan, nan, 'intracellular [goid 5622] [evidence IEA]', nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, 'zinc ion binding [goid 8270] [evidence IEA]; metal ion binding [goid 46872] [evidence IEA]', nan], 'Synonyms': [nan, nan, nan, 'MGC3490; MC7; HSA249128; DIPB', nan], 'Obsolete_Probe_Id': [nan, nan, nan, 'MGC3490; MC7; HSA249128; DIPB', nan], 'GB_ACC': ['XM_933824.1', 'AK024680', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1']}\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths using geo_get_relevant_filepaths function\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 3. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beeecafd",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd0a929e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:30:25.019830Z",
     "iopub.status.busy": "2025-03-25T06:30:25.019703Z",
     "iopub.status.idle": "2025-03-25T06:30:25.384239Z",
     "shell.execute_reply": "2025-03-25T06:30:25.383865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total probe IDs in annotation: 630984\n",
      "Total probe IDs with gene symbols: 36157\n",
      "\n",
      "Number of unique genes after mapping: 11631\n",
      "\n",
      "First 10 gene symbols:\n",
      "Index(['A26A1', 'AAAS', 'AACS', 'AACSL', 'AADACL1', 'AAK1', 'AAMP', 'AARS',\n",
      "       'AARS2', 'AARSD1'],\n",
      "      dtype='object', name='Gene')\n",
      "\n",
      "Number of genes after normalization: 11317\n",
      "\n",
      "First 10 normalized gene symbols:\n",
      "Index(['AAAS', 'AACS', 'AACSP1', 'AAK1', 'AAMDC', 'AAMP', 'AAR2', 'AARS1',\n",
      "       'AARS2', 'AARSD1'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene expression data saved to ../../output/preprocess/Ankylosing_Spondylitis/gene_data/GSE25101.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the column names for mapping\n",
    "# From the gene annotation preview, I can see that:\n",
    "# - 'ID' column contains the ILMN identifiers (e.g., ILMN_1725881) same as in the gene expression data\n",
    "# - 'Symbol' column contains the gene symbols (e.g., LOC23117, FCGR2B, TRIM44)\n",
    "\n",
    "# 2. Extract the mapping between IDs and gene symbols\n",
    "gene_map_df = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n",
    "\n",
    "# Print mapping statistics\n",
    "print(f\"Total probe IDs in annotation: {len(gene_annotation)}\")\n",
    "print(f\"Total probe IDs with gene symbols: {len(gene_map_df)}\")\n",
    "\n",
    "# 3. Convert probe-level measurements to gene-level expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_map_df)\n",
    "\n",
    "# 4. Print results of the gene mapping\n",
    "print(f\"\\nNumber of unique genes after mapping: {len(gene_data)}\")\n",
    "print(\"\\nFirst 10 gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# 5. Normalize gene symbols to handle synonyms and ensure consistency\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"\\nNumber of genes after normalization: {len(gene_data)}\")\n",
    "print(\"\\nFirst 10 normalized gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# 6. Create directory and save the gene expression data\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": "cba66c58",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76537054",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:30:25.385575Z",
     "iopub.status.busy": "2025-03-25T06:30:25.385455Z",
     "iopub.status.idle": "2025-03-25T06:30:28.997898Z",
     "shell.execute_reply": "2025-03-25T06:30:28.997267Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols in the gene expression data...\n",
      "Original gene data shape: 11317 genes × 32 samples\n",
      "Normalized gene data shape: 11317 genes × 32 samples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene expression data saved to ../../output/preprocess/Ankylosing_Spondylitis/gene_data/GSE25101.csv\n",
      "Extracting clinical features from original clinical data...\n",
      "Clinical features saved to ../../output/preprocess/Ankylosing_Spondylitis/clinical_data/GSE25101.csv\n",
      "Clinical features preview:\n",
      "{'GSM616668': [1.0], 'GSM616669': [1.0], 'GSM616670': [1.0], 'GSM616671': [1.0], 'GSM616672': [1.0], 'GSM616673': [1.0], 'GSM616674': [1.0], 'GSM616675': [1.0], 'GSM616676': [1.0], 'GSM616677': [1.0], 'GSM616678': [1.0], 'GSM616679': [1.0], 'GSM616680': [1.0], 'GSM616681': [1.0], 'GSM616682': [1.0], 'GSM616683': [1.0], 'GSM616684': [0.0], 'GSM616685': [0.0], 'GSM616686': [0.0], 'GSM616687': [0.0], 'GSM616688': [0.0], 'GSM616689': [0.0], 'GSM616690': [0.0], 'GSM616691': [0.0], 'GSM616692': [0.0], 'GSM616693': [0.0], 'GSM616694': [0.0], 'GSM616695': [0.0], 'GSM616696': [0.0], 'GSM616697': [0.0], 'GSM616698': [0.0], 'GSM616699': [0.0]}\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (32, 11318)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (32, 11318)\n",
      "\n",
      "Checking for bias in feature variables:\n",
      "For the feature 'Ankylosing_Spondylitis', the least common label is '1.0' with 16 occurrences. This represents 50.00% of the dataset.\n",
      "The distribution of the feature 'Ankylosing_Spondylitis' in this dataset is fine.\n",
      "\n",
      "A new JSON file was created at: ../../output/preprocess/Ankylosing_Spondylitis/cohort_info.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Ankylosing_Spondylitis/GSE25101.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols in the gene expression data...\")\n",
    "# From the previous step output, we can see the data already contains gene symbols\n",
    "# like 'A1BG', 'A1CF', 'A2M' which need to be normalized\n",
    "gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Original gene data shape: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "print(f\"Normalized gene data shape: {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",
    "# 2. Extract clinical features from scratch instead of loading the empty file\n",
    "print(\"Extracting clinical features from original clinical data...\")\n",
    "clinical_features = geo_select_clinical_features(\n",
    "    clinical_data, \n",
    "    trait, \n",
    "    trait_row,\n",
    "    convert_trait,\n",
    "    age_row,\n",
    "    convert_age,\n",
    "    gender_row,\n",
    "    convert_gender\n",
    ")\n",
    "\n",
    "# Save the extracted clinical features\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "\n",
    "print(\"Clinical features preview:\")\n",
    "print(preview_df(clinical_features))\n",
    "\n",
    "# Check if clinical features were successfully extracted\n",
    "if clinical_features.empty:\n",
    "    print(\"Failed to extract clinical features. Dataset cannot be processed further.\")\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=False,\n",
    "        is_biased=True,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Clinical features could not be extracted from the dataset.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to lack of clinical features.\")\n",
    "else:\n",
    "    # 2. Link clinical and genetic data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data_normalized)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "    # 3. Handle missing values systematically\n",
    "    linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
    "    print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "    # 4. Check if the dataset is biased\n",
    "    print(\"\\nChecking for bias in feature variables:\")\n",
    "    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "    # 5. 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,\n",
    "        note=\"Dataset contains gene expression data for aniridia patients and healthy controls.\"\n",
    "    )\n",
    "\n",
    "    # 6. Save linked data if usable\n",
    "    if is_usable:\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        linked_data.to_csv(out_data_file)\n",
    "        print(f\"Linked data saved to {out_data_file}\")\n",
    "    else:\n",
    "        print(\"Dataset deemed not usable for trait association studies, linked data not saved.\")"
   ]
  }
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