{
 "cells": [
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   "cell_type": "code",
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   "id": "f86fa2e5",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T06:14:47.742906Z"
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   "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 = \"Post-Traumatic_Stress_Disorder\"\n",
    "cohort = \"GSE52875\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Post-Traumatic_Stress_Disorder\"\n",
    "in_cohort_dir = \"../../input/GEO/Post-Traumatic_Stress_Disorder/GSE52875\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Post-Traumatic_Stress_Disorder/GSE52875.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Post-Traumatic_Stress_Disorder/gene_data/GSE52875.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Post-Traumatic_Stress_Disorder/clinical_data/GSE52875.csv\"\n",
    "json_path = \"../../output/preprocess/Post-Traumatic_Stress_Disorder/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0ac56c",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2063d8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:14:47.744650Z",
     "iopub.status.busy": "2025-03-25T06:14:47.744512Z",
     "iopub.status.idle": "2025-03-25T06:14:47.771018Z",
     "shell.execute_reply": "2025-03-25T06:14:47.770753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Expression signatures in heart tissues of mice simulating posttraumatic stress disorder (PTSD)\"\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: C57BL/6'], 1: ['tissue: Control_heart_tissue, C10R42', 'tissue: Control_heart_tissue, C10R1', 'tissue: Stressed_heart_tissue, T10R1', 'tissue: Control_heart_tissue, C5R1', 'tissue: Control_heart_tissue, C5R10', 'tissue: Stressed_heart_tissue, T10R42', 'tissue: Stressed_heart_tissue, T5R10', 'tissue: Stressed_heart_tissue, T5R1']}\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": "28f2eb94",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3126a204",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:14:47.772226Z",
     "iopub.status.busy": "2025-03-25T06:14:47.772124Z",
     "iopub.status.idle": "2025-03-25T06:14:47.778673Z",
     "shell.execute_reply": "2025-03-25T06:14:47.778435Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Determine Gene Expression Data Availability\n",
    "# Based on the background info and sample characteristics, this dataset appears to be related to gene expression \n",
    "# in heart tissues of mice simulating PTSD. The title suggests it's an expression study.\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# Looking at the sample characteristics dictionary, we don't see explicit trait (PTSD),\n",
    "# age, or gender information. The dictionary only contains strain and tissue information.\n",
    "trait_row = None  # No explicit PTSD status in the sample characteristics\n",
    "age_row = None    # No age information available\n",
    "gender_row = None # No gender information available\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "# Since we don't have these variables in the data, we'll define placeholder functions\n",
    "# that would be appropriate if the data were available\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert PTSD status to binary format.\n",
    "    Expected format: \"status: value\"\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    else:\n",
    "        value = value.lower()\n",
    "    \n",
    "    if value in ['ptsd', 'yes', 'true', 'case', 'positive', '1']:\n",
    "        return 1\n",
    "    elif value in ['control', 'no', 'false', 'normal', 'negative', '0']:\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age to continuous format.\n",
    "    Expected format: \"age: value\"\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"\n",
    "    Convert gender to binary format (0=female, 1=male).\n",
    "    Expected format: \"gender: value\" or \"sex: value\"\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    else:\n",
    "        value = value.lower()\n",
    "    \n",
    "    if value in ['male', 'm', 'man', 'boy', '1']:\n",
    "        return 1\n",
    "    elif value in ['female', 'f', 'woman', 'girl', '0']:\n",
    "        return 0\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",
    "\n",
    "# Save the initial filtering 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",
    "# 4. Clinical Feature Extraction\n",
    "# Since trait_row is None, we should skip the clinical feature extraction\n",
    "# The dataset appears to be a mouse model study without explicit trait classification in the sample characteristics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f057cdbe",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4702f582",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:14:47.779804Z",
     "iopub.status.busy": "2025-03-25T06:14:47.779705Z",
     "iopub.status.idle": "2025-03-25T06:14:47.793475Z",
     "shell.execute_reply": "2025-03-25T06:14:47.793222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['-1', '10138', '10306', '105441', '10899', '10901', '10902', '10903',\n",
      "       '10904', '10905', '10906', '10907', '10916', '10919', '10923', '10925',\n",
      "       '10928', '10936', '10937', '10942'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 2025 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": "c0919029",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d6fd0c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:14:47.794604Z",
     "iopub.status.busy": "2025-03-25T06:14:47.794506Z",
     "iopub.status.idle": "2025-03-25T06:14:47.796132Z",
     "shell.execute_reply": "2025-03-25T06:14:47.795856Z"
    }
   },
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers, these appear to be simple numeric values (1, 2, 3, etc.)\n",
    "# These are not standard human gene symbols like BRCA1, TP53, etc.\n",
    "# These are likely probe IDs or some other identifier that needs to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "584e7103",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "adda72ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:14:47.797230Z",
     "iopub.status.busy": "2025-03-25T06:14:47.797134Z",
     "iopub.status.idle": "2025-03-25T06:14:50.939949Z",
     "shell.execute_reply": "2025-03-25T06:14:50.939581Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['10916', '10998', '42918', '17883', '10997'], 'name': ['hsa-miR-1/mmu-miR-1', 'hsa-miR-19b/mmu-miR-19b/rno-miR-19b', 'hsa-miR-19b-2*', 'hsa-miR-19b-1*', 'hsa-miR-19a/mmu-miR-19a/rno-miR-19a'], 'accession': ['MIMAT0000416/MIMAT0000123', 'MIMAT0000074/MIMAT0000513/MIMAT0000788', 'MIMAT0004492', 'MIMAT0004491', 'MIMAT0000073/MIMAT0000651/MIMAT0000789'], 'miRNA_LIST': ['hsa-miR-1/mmu-miR-1', 'hsa-miR-19b/mmu-miR-19b/rno-miR-19b', 'hsa-miR-19b-2*', 'hsa-miR-19b-1*', 'hsa-miR-19a/mmu-miR-19a/rno-miR-19a'], 'SEQUENCE': ['UGGAAUGUAAAGAAGUAUGUAU', 'UGUGCAAAUCCAUGCAAAACUGA', 'AGUUUUGCAGGUUUGCAUUUCA', 'AGUUUUGCAGGUUUGCAUCCAGC', 'UGUGCAAAUCUAUGCAAAACUGA'], 'database': ['miRBase 14.0', 'miRBase 14.0', 'miRBase 14.0', 'miRBase 14.0', 'miRBase 14.0'], 'SPOT_ID': [nan, nan, nan, nan, nan]}\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": "d9a4be76",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf3459ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:14:50.941435Z",
     "iopub.status.busy": "2025-03-25T06:14:50.941319Z",
     "iopub.status.idle": "2025-03-25T06:14:50.959509Z",
     "shell.execute_reply": "2025-03-25T06:14:50.959221Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column names in gene_annotation: ['ID', 'name', 'accession', 'miRNA_LIST', 'SEQUENCE', 'database', 'SPOT_ID']\n",
      "\n",
      "This appears to be a miRNA dataset, not gene expression data\n",
      "\n",
      "Dataset rejected: This dataset contains miRNA data from mice rather than human gene expression data.\n"
     ]
    }
   ],
   "source": [
    "# 1. Based on the gene annotation preview, this dataset contains miRNA data, not gene expression data\n",
    "# The \"ID\" column does match the numeric identifiers in the gene expression data\n",
    "# However, miRNA_LIST contains miRNA identifiers, not human gene symbols\n",
    "print(\"Column names in gene_annotation:\", gene_annotation.columns.tolist())\n",
    "print(\"\\nThis appears to be a miRNA dataset, not gene expression data\")\n",
    "\n",
    "# This is a critical issue as our pipeline requires gene expression data that can be mapped to human gene symbols\n",
    "# According to the background info, this is a mouse study with miRNA data\n",
    "\n",
    "# Update our assessment of gene data availability\n",
    "is_gene_available = False  # miRNA data is not suitable for our gene expression analysis pipeline\n",
    "\n",
    "# Save the updated metadata reflecting that this dataset doesn't contain usable gene expression data\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",
    "print(\"\\nDataset rejected: This dataset contains miRNA data from mice rather than human gene expression data.\")"
   ]
  }
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