{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f362c874",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:37.751686Z",
     "iopub.status.busy": "2025-03-25T06:25:37.751507Z",
     "iopub.status.idle": "2025-03-25T06:25:37.917224Z",
     "shell.execute_reply": "2025-03-25T06:25:37.916829Z"
    }
   },
   "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 = \"Alzheimers_Disease\"\n",
    "cohort = \"GSE117589\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Alzheimers_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Alzheimers_Disease/GSE117589\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Alzheimers_Disease/GSE117589.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Alzheimers_Disease/gene_data/GSE117589.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Alzheimers_Disease/clinical_data/GSE117589.csv\"\n",
    "json_path = \"../../output/preprocess/Alzheimers_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35049cb",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4aad409",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:37.918737Z",
     "iopub.status.busy": "2025-03-25T06:25:37.918589Z",
     "iopub.status.idle": "2025-03-25T06:25:38.009147Z",
     "shell.execute_reply": "2025-03-25T06:25:38.008804Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"REST and Neural Gene Network Dysregulation in iPS Cell Models of Alzheimer’s Disease\"\n",
      "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "!Series_overall_design\t\"Refer to individual Series\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['cell type: induced pluripotent stem cells', 'cell type: neurons', 'cell type: neural progenitor cells'], 1: ['subject: 60F', 'subject: 64M', 'subject: 72M', 'subject: 73M', 'subject: 75F', 'subject: 92F', 'subject: 60M', 'subject: 69F', 'subject: 87F'], 2: ['diagnosis: normal', \"diagnosis: sporadic Alzheimer's disease\"], 3: ['clone: Clone 1', 'clone: Clone 2'], 4: ['coriell #: AG04455', 'coriell #: AG08125', 'coriell #: AG08379', 'coriell #: AG08509', 'coriell #: AG14244', 'coriell #: AG09173', 'coriell #: AG07376', 'coriell #: AG21158', 'coriell #: AG08243', 'coriell #: AG10788', 'coriell #: AG06869']}\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": "1c037745",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d67ad1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:38.010196Z",
     "iopub.status.busy": "2025-03-25T06:25:38.010078Z",
     "iopub.status.idle": "2025-03-25T06:25:38.031828Z",
     "shell.execute_reply": "2025-03-25T06:25:38.031496Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of extracted clinical features:\n",
      "{'GSM3304268': [0.0, 60.0, 0.0], 'GSM3304269': [0.0, 64.0, 1.0], 'GSM3304270': [0.0, 72.0, 1.0], 'GSM3304271': [0.0, 73.0, 1.0], 'GSM3304272': [0.0, 75.0, 0.0], 'GSM3304273': [0.0, 92.0, 0.0], 'GSM3304274': [1.0, 60.0, 1.0], 'GSM3304275': [1.0, 69.0, 0.0], 'GSM3304276': [1.0, 72.0, 1.0], 'GSM3304277': [1.0, 87.0, 0.0], 'GSM3304278': [0.0, 60.0, 0.0], 'GSM3304279': [0.0, 64.0, 1.0], 'GSM3304280': [0.0, 72.0, 1.0], 'GSM3304281': [0.0, 73.0, 1.0], 'GSM3304282': [0.0, 75.0, 0.0], 'GSM3304283': [0.0, 92.0, 0.0], 'GSM3304284': [1.0, 60.0, 0.0], 'GSM3304285': [1.0, 60.0, 1.0], 'GSM3304286': [1.0, 69.0, 0.0], 'GSM3304287': [1.0, 72.0, 1.0], 'GSM3304288': [1.0, 87.0, 0.0], 'GSM3304289': [0.0, 60.0, 0.0], 'GSM3304290': [0.0, 64.0, 1.0], 'GSM3304291': [0.0, 72.0, 1.0], 'GSM3304292': [0.0, 73.0, 1.0], 'GSM3304293': [0.0, 92.0, 0.0], 'GSM3304294': [1.0, 60.0, 0.0], 'GSM3304295': [1.0, 60.0, 1.0], 'GSM3304296': [1.0, 69.0, 0.0], 'GSM3304297': [1.0, 72.0, 1.0], 'GSM3304298': [1.0, 87.0, 0.0]}\n",
      "Clinical features saved to ../../output/preprocess/Alzheimers_Disease/clinical_data/GSE117589.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information and sample characteristics, this appears to be a dataset with gene expression data\n",
    "# from iPSCs, neurons, and neural progenitor cells. Therefore, gene expression data is likely available.\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For Alzheimer's Disease trait:\n",
    "# Looking at key 2, we see \"diagnosis: normal\" and \"diagnosis: sporadic Alzheimer's disease\"\n",
    "trait_row = 2\n",
    "\n",
    "# For age:\n",
    "# Age is not explicitly given but might be inferred from key 1 where subject info contains age and gender\n",
    "# e.g., 'subject: 60F', 'subject: 64M'\n",
    "age_row = 1\n",
    "\n",
    "# For gender:\n",
    "# Gender is also in key 1 as part of subject information\n",
    "gender_row = 1\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "def convert_trait(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    value = value.split(': ')[-1].strip().lower()\n",
    "    if \"alzheimer\" in value or \"ad\" in value:\n",
    "        return 1\n",
    "    elif \"normal\" in value or \"control\" in value or \"healthy\" in value:\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    # Extract age from patterns like 'subject: 60F', 'subject: 64M'\n",
    "    value = value.split(': ')[-1].strip()\n",
    "    # Extract digits from the beginning of the string\n",
    "    import re\n",
    "    age_match = re.match(r'^(\\d+)', value)\n",
    "    if age_match:\n",
    "        try:\n",
    "            return int(age_match.group(1))\n",
    "        except ValueError:\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    # Extract gender from patterns like 'subject: 60F', 'subject: 64M'\n",
    "    value = value.split(': ')[-1].strip()\n",
    "    # Check if the last character is 'F' or 'M'\n",
    "    if value.endswith('F'):\n",
    "        return 0  # Female\n",
    "    elif value.endswith('M'):\n",
    "        return 1  # Male\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, \n",
    "                             is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Assume clinical_data is already loaded from a previous step\n",
    "    try:\n",
    "        # Extract clinical features using the clinical_data DataFrame from step 1\n",
    "        clinical_features = geo_select_clinical_features(\n",
    "            clinical_df=clinical_data,\n",
    "            trait=trait,\n",
    "            trait_row=trait_row,\n",
    "            convert_trait=convert_trait,\n",
    "            age_row=age_row,\n",
    "            convert_age=convert_age,\n",
    "            gender_row=gender_row,\n",
    "            convert_gender=convert_gender\n",
    "        )\n",
    "        \n",
    "        # Preview the extracted clinical features\n",
    "        print(\"Preview of extracted clinical features:\")\n",
    "        print(preview_df(clinical_features))\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, index=False)\n",
    "        print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "    except NameError:\n",
    "        print(\"Clinical data not available from previous steps. Skipping clinical feature extraction.\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error in clinical feature extraction: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eede0869",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ace4ccca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:38.032917Z",
     "iopub.status.busy": "2025-03-25T06:25:38.032804Z",
     "iopub.status.idle": "2025-03-25T06:25:38.116296Z",
     "shell.execute_reply": "2025-03-25T06:25:38.115929Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene/probe identifiers:\n",
      "Index(['ENSG00000000003_at', 'ENSG00000000005_at', 'ENSG00000000419_at',\n",
      "       'ENSG00000000457_at', 'ENSG00000000460_at', 'ENSG00000000938_at',\n",
      "       'ENSG00000000971_at', 'ENSG00000001036_at', 'ENSG00000001084_at',\n",
      "       'ENSG00000001167_at', 'ENSG00000001460_at', 'ENSG00000001461_at',\n",
      "       'ENSG00000001497_at', 'ENSG00000001561_at', 'ENSG00000001617_at',\n",
      "       'ENSG00000001626_at', 'ENSG00000001629_at', 'ENSG00000001631_at',\n",
      "       'ENSG00000002016_at', 'ENSG00000002079_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": "99156441",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98f0cc09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:38.117690Z",
     "iopub.status.busy": "2025-03-25T06:25:38.117568Z",
     "iopub.status.idle": "2025-03-25T06:25:38.119538Z",
     "shell.execute_reply": "2025-03-25T06:25:38.119217Z"
    }
   },
   "outputs": [],
   "source": [
    "# Analysis of gene identifiers\n",
    "# The identifiers start with 'ENSG' which indicates they are Ensembl gene IDs\n",
    "# These are not standard human gene symbols (like BRCA1, APP, etc.)\n",
    "# Ensembl IDs need to be mapped to standard gene symbols for better interpretability\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27223c64",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "767604d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:38.120814Z",
     "iopub.status.busy": "2025-03-25T06:25:38.120700Z",
     "iopub.status.idle": "2025-03-25T06:25:38.865986Z",
     "shell.execute_reply": "2025-03-25T06:25:38.865603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['ENSG00000000003_at', 'ENSG00000000005_at', 'ENSG00000000419_at', 'ENSG00000000457_at', 'ENSG00000000460_at'], 'SPOT_ID': ['ENSG00000000003', 'ENSG00000000005', 'ENSG00000000419', 'ENSG00000000457', 'ENSG00000000460'], 'Description': ['tetraspanin 6 [Source:HGNC Symbol;Acc:HGNC:11858]', 'tenomodulin [Source:HGNC Symbol;Acc:HGNC:17757]', 'dolichyl-phosphate mannosyltransferase subunit 1, catalytic [Source:HGNC Symbol;Acc:HGNC:3005]', 'SCY1 like pseudokinase 3 [Source:HGNC Symbol;Acc:HGNC:19285]', 'chromosome 1 open reading frame 112 [Source:HGNC Symbol;Acc:HGNC:25565]']}\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": "60f1a9f6",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fdc3d9f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:38.867417Z",
     "iopub.status.busy": "2025-03-25T06:25:38.867286Z",
     "iopub.status.idle": "2025-03-25T06:25:39.342695Z",
     "shell.execute_reply": "2025-03-25T06:25:39.342322Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample SPOT_ID and Description pairs:\n",
      "SPOT_ID: ENSG00000000003 - Description: tetraspanin 6 [Source:HGNC Symbol;Acc:HGNC:11858]\n",
      "SPOT_ID: ENSG00000000005 - Description: tenomodulin [Source:HGNC Symbol;Acc:HGNC:17757]\n",
      "SPOT_ID: ENSG00000000419 - Description: dolichyl-phosphate mannosyltransferase subunit 1, catalytic [Source:HGNC Symbol;Acc:HGNC:3005]\n",
      "SPOT_ID: ENSG00000000457 - Description: SCY1 like pseudokinase 3 [Source:HGNC Symbol;Acc:HGNC:19285]\n",
      "SPOT_ID: ENSG00000000460 - Description: chromosome 1 open reading frame 112 [Source:HGNC Symbol;Acc:HGNC:25565]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping preview:\n",
      "{'ID': ['ENSG00000000003_at', 'ENSG00000000005_at', 'ENSG00000000419_at', 'ENSG00000000457_at', 'ENSG00000000460_at'], 'Gene': [['HGNC'], ['HGNC'], ['HGNC'], ['SCY1', 'HGNC'], ['HGNC']]}\n",
      "Number of probes with gene symbols: 18144\n",
      "Gene data shape before normalization: (0, 31)\n",
      "Sample gene symbols before normalization:\n",
      "[]\n",
      "Gene data shape after normalization: (0, 31)\n",
      "\n",
      "Processed gene expression data preview (first 5 rows, 5 columns):\n",
      "Gene data is empty after processing\n",
      "Processed gene data saved to ../../output/preprocess/Alzheimers_Disease/gene_data/GSE117589.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine which columns contain gene identifiers and gene symbols\n",
    "# The 'ID' column in gene_annotation matches the index in gene_data\n",
    "# We need to extract the official gene symbols from the Description field\n",
    "\n",
    "# Let's look at the SPOT_ID and Description columns more closely\n",
    "print(\"Sample SPOT_ID and Description pairs:\")\n",
    "for i in range(min(5, len(gene_annotation))):\n",
    "    print(f\"SPOT_ID: {gene_annotation.iloc[i]['SPOT_ID']} - Description: {gene_annotation.iloc[i]['Description']}\")\n",
    "\n",
    "# Create a mapping from ENSEMBL IDs to gene symbols using regex to extract symbols from Description\n",
    "import re\n",
    "\n",
    "def extract_gene_symbol_from_description(description_text):\n",
    "    if not isinstance(description_text, str):\n",
    "        return []\n",
    "    \n",
    "    # Pattern to extract HGNC symbols from description\n",
    "    # Example: \"tetraspanin 6 [Source:HGNC Symbol;Acc:HGNC:11858]\" -> extract the HGNC ID 11858\n",
    "    hgnc_match = re.search(r'HGNC:(\\d+)', description_text)\n",
    "    if hgnc_match:\n",
    "        # Use extract_human_gene_symbols to get any gene symbols in the text\n",
    "        symbols = extract_human_gene_symbols(description_text)\n",
    "        if symbols:\n",
    "            return symbols\n",
    "        \n",
    "        # If no symbols found with extract_human_gene_symbols, try to get the first word\n",
    "        # that might be a gene symbol\n",
    "        first_part_match = re.match(r'^(\\w+)', description_text)\n",
    "        if first_part_match:\n",
    "            return [first_part_match.group(1)]\n",
    "    \n",
    "    return []\n",
    "\n",
    "# Create a custom mapping dataframe that contains both ENSEMBL IDs and symbol information\n",
    "mapping_df = pd.DataFrame({\n",
    "    'ID': gene_annotation['ID'],\n",
    "    'Gene': gene_annotation['Description'].apply(extract_human_gene_symbols)\n",
    "})\n",
    "\n",
    "# Filter out rows where Gene is an empty list\n",
    "mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n",
    "\n",
    "# Print the first few rows of the mapping to verify\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(mapping_df))\n",
    "print(f\"Number of probes with gene symbols: {len(mapping_df)}\")\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print shape before normalization\n",
    "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "\n",
    "# Check if gene symbols need normalization\n",
    "print(\"Sample gene symbols before normalization:\")\n",
    "print(list(gene_data.index[:10]))\n",
    "\n",
    "# Normalize gene symbols to ensure consistency\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "\n",
    "# Print shape after normalization\n",
    "print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
    "\n",
    "# Preview the first few rows of the processed gene expression data\n",
    "print(\"\\nProcessed gene expression data preview (first 5 rows, 5 columns):\")\n",
    "if not gene_data.empty:\n",
    "    print(gene_data.iloc[:5, :5])\n",
    "else:\n",
    "    print(\"Gene data is empty after processing\")\n",
    "\n",
    "# Save the processed gene data to 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\"Processed gene data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1524db6",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "972390aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:39.344195Z",
     "iopub.status.busy": "2025-03-25T06:25:39.344071Z",
     "iopub.status.idle": "2025-03-25T06:25:40.736480Z",
     "shell.execute_reply": "2025-03-25T06:25:40.736105Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping preview:\n",
      "{'ID': ['ENSG00000000003_at', 'ENSG00000000005_at', 'ENSG00000000419_at', 'ENSG00000000457_at', 'ENSG00000000460_at'], 'Gene': [['HGNC'], ['HGNC'], ['HGNC'], ['SCY1', 'HGNC'], ['HGNC']]}\n",
      "Number of probes with gene symbols: 18145\n",
      "\n",
      "Gene expression data preview:\n",
      "Gene expression data shape: (0, 31)\n",
      "Sample column names: ['GSM3304268', 'GSM3304269', 'GSM3304270', 'GSM3304271', 'GSM3304272']\n",
      "Re-loaded gene data shape: (20027, 31)\n",
      "Gene data shape after mapping: (0, 31)\n",
      "Mapped gene data is suspiciously small. Trying alternative approach...\n",
      "Alternative mapping created with 18146 entries\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape after alternative mapping: (2551, 31)\n",
      "Processed gene data saved to ../../output/preprocess/Alzheimers_Disease/gene_data/GSE117589.csv\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. Extract gene symbols from Description field properly\n",
    "def extract_gene_symbol_from_description(description_text):\n",
    "    if not isinstance(description_text, str):\n",
    "        return []\n",
    "    \n",
    "    # Get the gene name from the beginning of description (before [Source:...])\n",
    "    # Example: \"tetraspanin 6 [Source:HGNC Symbol;Acc:HGNC:11858]\" -> \"tetraspanin 6\"\n",
    "    name_part = description_text.split('[Source:')[0].strip()\n",
    "    \n",
    "    # Many descriptions have format \"Gene Name [Source:...]\" - extract the gene symbol\n",
    "    # Gene symbols are typically uppercase, so look for capital letters\n",
    "    symbols = extract_human_gene_symbols(description_text)\n",
    "    \n",
    "    # If we found symbols using the extract_human_gene_symbols function, return them\n",
    "    if symbols:\n",
    "        return symbols\n",
    "    \n",
    "    # Fallback: try to extract the first word if it looks like a gene symbol\n",
    "    words = name_part.split()\n",
    "    if words and len(words[0]) <= 10 and any(c.isupper() for c in words[0]):\n",
    "        return [words[0]]\n",
    "    \n",
    "    return []\n",
    "\n",
    "# Create a custom mapping dataframe\n",
    "mapping_df = pd.DataFrame({\n",
    "    'ID': gene_annotation['ID'],\n",
    "    'Gene': gene_annotation['Description'].apply(extract_gene_symbol_from_description)\n",
    "})\n",
    "\n",
    "# Filter out rows where Gene is an empty list\n",
    "mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n",
    "\n",
    "# Print the first few rows of the mapping to verify\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(mapping_df))\n",
    "print(f\"Number of probes with gene symbols: {len(mapping_df)}\")\n",
    "\n",
    "# Let's also check gene expression data to make sure it's not empty\n",
    "print(\"\\nGene expression data preview:\")\n",
    "print(f\"Gene expression data shape: {gene_data.shape}\")\n",
    "print(f\"Sample column names: {list(gene_data.columns[:5])}\")\n",
    "\n",
    "# Extract gene expression data again from the matrix file to ensure we have good data\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "print(f\"Re-loaded gene data shape: {gene_data.shape}\")\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Gene data shape after mapping: {gene_data_mapped.shape}\")\n",
    "\n",
    "# If the mapped data is too small or empty, try a different approach\n",
    "if gene_data_mapped.shape[0] < 100:\n",
    "    print(\"Mapped gene data is suspiciously small. Trying alternative approach...\")\n",
    "    # Direct approach: Extract gene name from the beginning of the Description\n",
    "    mapping_df = pd.DataFrame({\n",
    "        'ID': gene_annotation['ID'],\n",
    "        'Gene': gene_annotation['Description'].apply(lambda x: \n",
    "            x.split('[')[0].strip() if isinstance(x, str) else '')\n",
    "    })\n",
    "    # Keep only non-empty gene names\n",
    "    mapping_df = mapping_df[mapping_df['Gene'] != '']\n",
    "    print(f\"Alternative mapping created with {len(mapping_df)} entries\")\n",
    "    \n",
    "    # Apply alternative mapping\n",
    "    gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n",
    "    print(f\"Gene data shape after alternative mapping: {gene_data_mapped.shape}\")\n",
    "\n",
    "# If still empty, use the original gene data with ENSEMBL IDs as gene names\n",
    "if gene_data_mapped.shape[0] < 100:\n",
    "    print(\"Using original gene data with ENSEMBL IDs as fallback\")\n",
    "    # Remove the _at suffix from the index\n",
    "    gene_data.index = gene_data.index.str.replace('_at', '')\n",
    "    gene_data_mapped = gene_data\n",
    "    print(f\"Using original gene data: {gene_data_mapped.shape}\")\n",
    "\n",
    "# Save the processed gene data to file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data_mapped.to_csv(out_gene_data_file)\n",
    "print(f\"Processed gene data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af163808",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "740e6730",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:25:40.737991Z",
     "iopub.status.busy": "2025-03-25T06:25:40.737869Z",
     "iopub.status.idle": "2025-03-25T06:25:40.757971Z",
     "shell.execute_reply": "2025-03-25T06:25:40.757644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data columns: Index(['GSM3304268', 'GSM3304269', 'GSM3304270', 'GSM3304271', 'GSM3304272',\n",
      "       'GSM3304273', 'GSM3304274', 'GSM3304275', 'GSM3304276', 'GSM3304277',\n",
      "       'GSM3304278', 'GSM3304279', 'GSM3304280', 'GSM3304281', 'GSM3304282',\n",
      "       'GSM3304283', 'GSM3304284', 'GSM3304285', 'GSM3304286', 'GSM3304287',\n",
      "       'GSM3304288', 'GSM3304289', 'GSM3304290', 'GSM3304291', 'GSM3304292',\n",
      "       'GSM3304293', 'GSM3304294', 'GSM3304295', 'GSM3304296', 'GSM3304297',\n",
      "       'GSM3304298'],\n",
      "      dtype='object')\n",
      "Gene data shape: (2551, 31)\n",
      "Linked data shape: (2554, 31)\n",
      "Linked data index preview: ['Alzheimers_Disease', 'Age', 'Gender', 'A-', 'A-52', 'A0', 'A1', 'A10', 'A11', 'A12']\n",
      "Transposed linked data shape: (31, 2554)\n",
      "Actual columns in linked_data: ['Alzheimers_Disease', 'Age', 'Gender', 'A-', 'A-52', 'A0', 'A1', 'A10', 'A11', 'A12']\n",
      "Data shape after handling missing values: (0, 2)\n",
      "Quartiles for 'Alzheimers_Disease':\n",
      "  25%: nan\n",
      "  50% (Median): nan\n",
      "  75%: nan\n",
      "Min: nan\n",
      "Max: nan\n",
      "The distribution of the feature 'Alzheimers_Disease' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: nan\n",
      "  50% (Median): nan\n",
      "  75%: nan\n",
      "Min: nan\n",
      "Max: nan\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "Trait bias assessment: False\n",
      "Data columns after bias assessment: ['Alzheimers_Disease', 'Age']\n",
      "Abnormality detected in the cohort: GSE117589. Preprocessing failed.\n",
      "A new JSON file was created at: ../../output/preprocess/Alzheimers_Disease/cohort_info.json\n",
      "Dataset not usable due to bias or other issues. Linked data not saved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_51556/2649569560.py:40: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  linked_data = pd.concat([clinical_data, gene_data_mapped], axis=0)\n"
     ]
    }
   ],
   "source": [
    "# Let's continue from where we left off with the gene data processing\n",
    "# Load clinical data that was saved earlier\n",
    "clinical_data = pd.read_csv(out_clinical_data_file)\n",
    "print(\"Clinical data columns:\", clinical_data.columns)\n",
    "\n",
    "# Load gene expression data \n",
    "gene_data_mapped = pd.read_csv(out_gene_data_file, index_col=0)\n",
    "print(\"Gene data shape:\", gene_data_mapped.shape)\n",
    "\n",
    "# We need to transform clinical data into the right format for linking\n",
    "# First, check if the clinical data has any column that we can use as sample identifiers\n",
    "if 'Unnamed: 0' in clinical_data.columns:\n",
    "    clinical_data.rename(columns={'Unnamed: 0': 'Sample'}, inplace=True)\n",
    "    clinical_data.set_index('Sample', inplace=True)\n",
    "else:\n",
    "    # Create a DataFrame with the appropriate structure: samples as columns, features as rows\n",
    "    # First get sample IDs from gene data\n",
    "    sample_ids = gene_data_mapped.columns.tolist()\n",
    "    \n",
    "    # Create a new DataFrame with the right structure\n",
    "    new_clinical_df = pd.DataFrame(index=[trait, 'Age', 'Gender'], columns=sample_ids)\n",
    "    \n",
    "    # Fill in the values - assuming clinical_data has the same order of samples\n",
    "    if len(clinical_data) == len(sample_ids):\n",
    "        for i, sample_id in enumerate(sample_ids):\n",
    "            if i < len(clinical_data):\n",
    "                # Get values from clinical_data row i\n",
    "                row = clinical_data.iloc[i]\n",
    "                # Assign values to the new DataFrame\n",
    "                if trait in row:\n",
    "                    new_clinical_df.loc[trait, sample_id] = row[trait]\n",
    "                if 'Age' in row:\n",
    "                    new_clinical_df.loc['Age', sample_id] = row['Age']\n",
    "                if 'Gender' in row:\n",
    "                    new_clinical_df.loc['Gender', sample_id] = row['Gender']\n",
    "    \n",
    "    clinical_data = new_clinical_df\n",
    "\n",
    "# 2. Link clinical and genetic data\n",
    "linked_data = pd.concat([clinical_data, gene_data_mapped], axis=0)\n",
    "print(\"Linked data shape:\", linked_data.shape)\n",
    "print(\"Linked data index preview:\", list(linked_data.index[:10]))\n",
    "\n",
    "# Transpose the linked data to have samples as rows and features as columns\n",
    "linked_data = linked_data.T\n",
    "print(\"Transposed linked data shape:\", linked_data.shape)\n",
    "print(\"Actual columns in linked_data:\", linked_data.columns.tolist()[:10])\n",
    "\n",
    "# 3. Handle missing values - use the trait variable from environment setup\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(\"Data shape after handling missing values:\", linked_data.shape)\n",
    "\n",
    "# 4. Determine trait and demographic bias\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "print(f\"Trait bias assessment: {is_biased}\")\n",
    "print(\"Data columns after bias assessment:\", list(linked_data.columns[:10]))\n",
    "\n",
    "# 5. Final quality validation and saving metadata\n",
    "note = \"Used alternative gene mapping approach to extract gene symbols from descriptions.\"\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=note\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, index=True)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset not usable due to bias or other issues. Linked data not saved.\")"
   ]
  }
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