{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc2f67f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:40:15.555371Z",
     "iopub.status.busy": "2025-03-25T05:40:15.555265Z",
     "iopub.status.idle": "2025-03-25T05:40:15.719894Z",
     "shell.execute_reply": "2025-03-25T05:40:15.719532Z"
    }
   },
   "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 = \"Height\"\n",
    "cohort = \"GSE102130\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Height\"\n",
    "in_cohort_dir = \"../../input/GEO/Height/GSE102130\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Height/GSE102130.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE102130.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE102130.csv\"\n",
    "json_path = \"../../output/preprocess/Height/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38b04a5",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed96f144",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:40:15.721357Z",
     "iopub.status.busy": "2025-03-25T05:40:15.721212Z",
     "iopub.status.idle": "2025-03-25T05:40:15.822495Z",
     "shell.execute_reply": "2025-03-25T05:40:15.822165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Single cell RNA-seq analysis of K27M-mutant glioma\"\n",
      "!Series_summary\t\"To understand the diversity of expression states within K27M-mutant gliomas, we profiled 4058 single cells, primarily from 6 K27M-mutant gliomas, by single cell RNA-seq\"\n",
      "!Series_overall_design\t\"Tumors were disaggregated, sorted into single cells, and profiled by Smart-seq2.\"\n",
      "!Series_overall_design\t\"------------------------------------------------------\"\n",
      "!Series_overall_design\t\"Authors state that  \"\"We are currently unable to deposit the raw data\"\n",
      "!Series_overall_design\t\"due to privacy concerns, but will have the raw data available once\"\n",
      "!Series_overall_design\t\"we obtain permission from our institution.\"\"\"\n",
      "!Series_overall_design\t\"Please contact the submitter directly regarding access to raw data on a case-by-case basis.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['sample type: human tumor', 'sample type: patient-derived xenogrft (PDX)', 'sample type: patient-derived xenograft(PDX)', 'sample type: cell culture']}\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": "0524a05b",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef3e887e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:40:15.823631Z",
     "iopub.status.busy": "2025-03-25T05:40:15.823518Z",
     "iopub.status.idle": "2025-03-25T05:40:15.830385Z",
     "shell.execute_reply": "2025-03-25T05:40:15.830107Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# This dataset is about K27M-mutant gliomas analyzed with single-cell RNA-seq\n",
    "# It explicitly mentions profiling cells by RNA-seq, so gene expression data should be available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Looking at the sample characteristics dictionary, we don't see height/age/gender information\n",
    "# The only key is 0, which contains sample type information, but not our variables of interest\n",
    "trait_row = None  # Height data is not available\n",
    "age_row = None    # Age data is not available \n",
    "gender_row = None # Gender data is not available\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "# Even though we don't have the data, we'll define conversion functions in case they're needed elsewhere\n",
    "def convert_trait(value):\n",
    "    # For height, we would normally extract a numeric value in cm or inches\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part after the colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        # Try to convert to float for continuous height measurement\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    # For age, we would normally extract a numeric value in years\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part after the colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        # Try to convert to float for continuous age\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # For gender, we would convert to binary: female=0, male=1\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part after the colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    \n",
    "    if isinstance(value, str):\n",
    "        value = value.lower()\n",
    "        if 'female' in value or 'f' == value:\n",
    "            return 0\n",
    "        elif 'male' in value or 'm' == value:\n",
    "            return 1\n",
    "    \n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Initial filtering - trait data is not available (trait_row is None)\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "# Since trait_row is None, we skip this substep\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1add0104",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b6d81e82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:40:15.831434Z",
     "iopub.status.busy": "2025-03-25T05:40:15.831329Z",
     "iopub.status.idle": "2025-03-25T05:40:16.404442Z",
     "shell.execute_reply": "2025-03-25T05:40:16.404004Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file size: 159095 bytes\n",
      "Found data marker at line 70\n",
      "Next line after marker: 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      "\n",
      "Examining SOFT file as alternative data source:\n",
      "^DATABASE = GeoMiame\n",
      "!Database_name = Gene Expression Omnibus (GEO)\n",
      "!Database_institute = NCBI NLM NIH\n",
      "!Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n",
      "!Database_email = geo@ncbi.nlm.nih.gov\n",
      "^SERIES = GSE102130\n",
      "!Series_title = Single cell RNA-seq analysis of K27M-mutant glioma\n",
      "!Series_geo_accession = GSE102130\n",
      "!Series_status = Public on Apr 21 2018\n",
      "!Series_submission_date = Aug 01 2017\n",
      "!Series_last_update_date = Jun 03 2019\n",
      "!Series_pubmed_id = 29674595\n",
      "!Series_summary = To understand the diversity of expression states within K27M-mutant gliomas, we profiled 4058 single cells, primarily from 6 K27M-mutant gliomas, by single cell RNA-seq\n",
      "!Series_overall_design = Tumors were disaggregated, sorted into single cells, and profiled by Smart-seq2.\n",
      "!Series_overall_design = ------------------------------------------------------\n",
      "!Series_overall_design = Authors state that  \"We are currently unable to deposit the raw data\n",
      "!Series_overall_design = due to privacy concerns, but will have the raw data available once\n",
      "!Series_overall_design = we obtain permission from our institution.\"\n",
      "!Series_overall_design = Please contact the submitter directly regarding access to raw data on a case-by-case basis.\n",
      "!Series_type = Expression profiling by high throughput sequencing\n",
      "\n",
      "Attempting to extract gene data from matrix file:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracted gene expression data is empty\n",
      "\n",
      "Gene expression data available: False\n"
     ]
    }
   ],
   "source": [
    "# 1. Get the file paths for the SOFT file and matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. First, examine the structure of both files to understand their format\n",
    "import gzip\n",
    "\n",
    "# Check the size of the matrix file first\n",
    "import os\n",
    "matrix_file_size = os.path.getsize(matrix_file)\n",
    "print(f\"Matrix file size: {matrix_file_size} bytes\")\n",
    "\n",
    "# Peek at the matrix file structure\n",
    "with gzip.open(matrix_file, 'rt') as file:\n",
    "    # Read first 100 lines to find the header structure\n",
    "    lines_found = 0\n",
    "    data_marker_found = False\n",
    "    for i, line in enumerate(file):\n",
    "        if '!series_matrix_table_begin' in line:\n",
    "            print(f\"Found data marker at line {i}\")\n",
    "            data_marker_found = True\n",
    "            # Read the next line which should be the header\n",
    "            next_line = next(file, None)\n",
    "            if next_line:\n",
    "                print(f\"Next line after marker: {next_line.strip()}\")\n",
    "                lines_found += 1\n",
    "            else:\n",
    "                print(\"No data after marker\")\n",
    "            break\n",
    "        if i >= 100:  # Limit search to first 100 lines\n",
    "            break\n",
    "    \n",
    "    if not data_marker_found:\n",
    "        print(\"Matrix table marker not found in first 100 lines\")\n",
    "\n",
    "# 3. Check the SOFT file as an alternative source of gene data\n",
    "print(\"\\nExamining SOFT file as alternative data source:\")\n",
    "with gzip.open(soft_file, 'rt') as file:\n",
    "    for i, line in enumerate(file):\n",
    "        if i < 20:  # Just show the first few lines to understand format\n",
    "            print(line.strip())\n",
    "        else:\n",
    "            break\n",
    "\n",
    "# 4. Attempt to extract gene expression data with error handling\n",
    "try:\n",
    "    print(\"\\nAttempting to extract gene data from matrix file:\")\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    if gene_data.empty:\n",
    "        print(\"Extracted gene expression data is empty\")\n",
    "    else:\n",
    "        print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n",
    "        print(\"First 20 gene IDs:\")\n",
    "        print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n",
    "    print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n",
    "\n",
    "# 5. Set is_gene_available flag based on our findings\n",
    "is_gene_available = False  # Based on our examination, this dataset doesn't contain usable gene expression data\n",
    "print(f\"\\nGene expression data available: {is_gene_available}\")"
   ]
  }
 ],
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