{
 "cells": [
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   "id": "3ff89209",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T05:47:07.849013Z"
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   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
    "\n",
    "# Path Configuration\n",
    "from tools.preprocess import *\n",
    "\n",
    "# Processing context\n",
    "trait = \"Huntingtons_Disease\"\n",
    "cohort = \"GSE95843\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Huntingtons_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Huntingtons_Disease/GSE95843\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Huntingtons_Disease/GSE95843.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Huntingtons_Disease/gene_data/GSE95843.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Huntingtons_Disease/clinical_data/GSE95843.csv\"\n",
    "json_path = \"../../output/preprocess/Huntingtons_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2e4b034",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7de7bbdb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:47:07.850713Z",
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     "shell.execute_reply": "2025-03-25T05:47:07.976013Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression from embryoid bodies derived from HBG3 HB9 ES cells\"\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: ['strain: F1 progeny of two different strains (C57BL/6 and SJL)', 'strain: B6CBA-Tg(HDexon1)62Gpb/3J'], 1: ['time point: 9 weeks cultures', 'time point: 10 weeks cultures', \"treatment: plasma from a Huntington's disease mouse model\"]}\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": "33dc60b8",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "545b1d9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:47:07.977859Z",
     "iopub.status.busy": "2025-03-25T05:47:07.977606Z",
     "iopub.status.idle": "2025-03-25T05:47:08.000304Z",
     "shell.execute_reply": "2025-03-25T05:47:07.999978Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information and sample characteristics, it appears this dataset contains\n",
    "# information about amyloid beta levels, but not gene expression data\n",
    "is_gene_available = False\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# Looking at the sample characteristics dictionary, we don't see any clear information about \n",
    "# Huntington's Disease, age, or gender\n",
    "\n",
    "# 2.1 Data Availability\n",
    "trait_row = None  # No information about Huntington's Disease in the samples\n",
    "age_row = None    # No age information available\n",
    "gender_row = None # No gender information available\n",
    "\n",
    "# 2.2 Data Type Conversion (defining functions even though data is unavailable)\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (0 for control, 1 for Huntington's Disease)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    value = value.lower() if isinstance(value, str) else str(value).lower()\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    if \"disease\" in value or \"patient\" in value or \"case\" in value or \"huntington\" in value or \"hd\" in value:\n",
    "        return 1\n",
    "    elif \"control\" in value or \"healthy\" in value or \"normal\" in value:\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    value = value.lower() if isinstance(value, str) else str(value).lower()\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    if \"female\" in value or \"f\" == value.strip():\n",
    "        return 0\n",
    "    elif \"male\" in value or \"m\" == value.strip():\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# We'll set is_trait_available to False as we couldn't find trait information\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, \n",
    "                             is_trait_available=is_trait_available)\n",
    "\n",
    "# 4. Clinical Feature Extraction - Skip this step as trait_row is None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d4e288",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbcd1c7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:47:08.001603Z",
     "iopub.status.busy": "2025-03-25T05:47:08.001500Z",
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     "shell.execute_reply": "2025-03-25T05:47:08.206481Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Huntingtons_Disease/GSE95843/GSE95843-GPL23148_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (19620, 65)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['a', 'A030009H04Rik', 'A130010J15Rik', 'A130023I24Rik', 'A1bg', 'A1cf',\n",
      "       'A230006K03Rik', 'A230046K03Rik', 'A230050P20Rik', 'A230051G13Rik',\n",
      "       'A230051N06Rik', 'A230083G16Rik', 'A2ld1', 'A2m', 'A330021E22Rik',\n",
      "       'A330049M08Rik', 'A330070K13Rik', 'A3galt2', 'A430005L14Rik',\n",
      "       'A430033K04Rik'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. Get the SOFT and matrix file paths again \n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "print(f\"Matrix file found: {matrix_file}\")\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(f\"Gene data shape: {gene_data.shape}\")\n",
    "    \n",
    "    # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ea8fda",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab3add52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:47:08.208307Z",
     "iopub.status.busy": "2025-03-25T05:47:08.208190Z",
     "iopub.status.idle": "2025-03-25T05:47:08.210136Z",
     "shell.execute_reply": "2025-03-25T05:47:08.209839Z"
    }
   },
   "outputs": [],
   "source": [
    "# Based on the provided output, the gene identifiers appear to be human gene symbols.\n",
    "# These are standard gene symbols like A1BG, A2M, AAAS, etc. that match official human gene nomenclature.\n",
    "# They are not probe IDs (which would typically be numeric or have manufacturer prefixes)\n",
    "# and they are not other types of identifiers that would require mapping.\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b08f86be",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "939f6a7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:47:08.211437Z",
     "iopub.status.busy": "2025-03-25T05:47:08.211334Z",
     "iopub.status.idle": "2025-03-25T05:47:09.069110Z",
     "shell.execute_reply": "2025-03-25T05:47:09.068706Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n",
      "Gene data shape after normalization: (14859, 65)\n",
      "First 10 normalized gene symbols:\n",
      "Index(['A1BG', 'A1CF', 'A2M', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAAS', 'AACS',\n",
      "       'AADAC', 'AADACL3'],\n",
      "      dtype='object', name='ID')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to: ../../output/preprocess/Huntingtons_Disease/gene_data/GSE95843.csv\n",
      "\n",
      "Validating cohort usability...\n",
      "Dataset usability: False\n",
      "Dataset lacks trait information for Huntington's Disease, so no linked data will be saved.\n"
     ]
    }
   ],
   "source": [
    "# Based on previous steps, this dataset does not contain Huntington's Disease trait information\n",
    "# and trait_row was found to be None, so we need to adjust our approach\n",
    "\n",
    "# 1. First, normalize the gene symbols in the gene data we extracted in Step 3\n",
    "print(\"Normalizing gene symbols...\")\n",
    "# Make sure output directory exists\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "\n",
    "# Normalize gene symbols directly from gene_data variable obtained in Step 3\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(\"First 10 normalized gene symbols:\")\n",
    "print(normalized_gene_data.index[:10])\n",
    "\n",
    "# Save the normalized gene data\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to: {out_gene_data_file}\")\n",
    "\n",
    "# 2. Since we don't have trait data, we need to prepare a proper dataframe for validation\n",
    "# and set is_biased=True since without trait data, the dataset is biased/unusable for trait analysis\n",
    "dummy_df = normalized_gene_data.iloc[:5, :5].reset_index()  # Create small sample for efficiency\n",
    "is_biased = True  # Without trait data, the dataset is considered biased/unusable\n",
    "\n",
    "print(\"\\nValidating cohort usability...\")\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,  # We did find gene expression data\n",
    "    is_trait_available=False,  # Previous steps found no trait information\n",
    "    is_biased=is_biased,  # Dataset is biased without trait data\n",
    "    df=dummy_df,  # Use a small sample of the data for validation\n",
    "    note=\"This dataset contains gene expression data but lacks Huntington's Disease trait information.\"\n",
    ")\n",
    "\n",
    "print(f\"Dataset usability: {is_usable}\")\n",
    "print(\"Dataset lacks trait information for Huntington's Disease, so no linked data will be saved.\")"
   ]
  }
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