{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac10222c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:05.927969Z",
     "iopub.status.busy": "2025-03-25T06:23:05.927750Z",
     "iopub.status.idle": "2025-03-25T06:23:06.097756Z",
     "shell.execute_reply": "2025-03-25T06:23:06.097386Z"
    }
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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 = \"Age-Related_Macular_Degeneration\"\n",
    "cohort = \"GSE67899\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Age-Related_Macular_Degeneration\"\n",
    "in_cohort_dir = \"../../input/GEO/Age-Related_Macular_Degeneration/GSE67899\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/GSE67899.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE67899.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE67899.csv\"\n",
    "json_path = \"../../output/preprocess/Age-Related_Macular_Degeneration/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a18157",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f92248e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:06.099141Z",
     "iopub.status.busy": "2025-03-25T06:23:06.098991Z",
     "iopub.status.idle": "2025-03-25T06:23:06.196319Z",
     "shell.execute_reply": "2025-03-25T06:23:06.196012Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Delay and restoration of persistent wound-induced retinal pigmented epithelial-to-mesenchymal transition by TGF-beta pathway inhibitors: Implications for age-related macular degeneration\"\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: ['donor id: hfRPE-020207-2', 'donor id: hfRPE-071709', 'donor id: hfRPE-081309', 'donor id: hfRPE-111109'], 1: ['plating density: 4,000 cells/cm2', 'plating density: 80,000 cells/cm2'], 2: ['passage number: 0', 'passage number: 5'], 3: ['culture time: 3 Days', 'culture time: 16 Days', 'culture time: 32 Days', 'culture time: 64 Days'], 4: ['cultureware: T75-Flask', 'cultureware: Micropourous Membrane', 'cultureware: 6-well Multiwell Plate'], 5: ['treatment: None', 'treatment: DMSO', 'treatment: 2 ng/ml FGF2', 'treatment: 500 nM A83-01', 'treatment: 500 nM A83-01 + 2ng FGF', 'treatment: 500 nM Thiazovivin', 'treatment: 500 nM Thiazovivin + 2ng FGF', 'treatment: 200 nM LDN193189', 'treatment: 200 nM LDN193189 + 2ng FGF', 'treatment: 5 mM XAV939', 'treatment: 5 mM XAV939 + 2ng FGF']}\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": "22385422",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56a70fe3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:06.197712Z",
     "iopub.status.busy": "2025-03-25T06:23:06.197601Z",
     "iopub.status.idle": "2025-03-25T06:23:06.205405Z",
     "shell.execute_reply": "2025-03-25T06:23:06.205100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{'Sample_1': [0.0], 'Sample_2': [0.0], 'Sample_3': [1.0], 'Sample_4': [1.0], 'Sample_5': [1.0], 'Sample_6': [1.0], 'Sample_7': [1.0], 'Sample_8': [1.0], 'Sample_9': [1.0], 'Sample_10': [1.0], 'Sample_11': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE67899.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the series title and summary, this dataset seems to focus on RPE cells and the TGF-beta pathway\n",
    "# It appears to contain gene expression data related to AMD\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# After analyzing the sample characteristics dictionary, I see:\n",
    "# - No direct trait classification (AMD vs control) is provided\n",
    "# - No age information\n",
    "# - No gender information\n",
    "# The dataset appears to be about cell culture experiments rather than human subjects directly\n",
    "\n",
    "# The treatment key (index 5) seems to contain information about various treatments \n",
    "# which could be used to infer disease vs. control conditions\n",
    "trait_row = 5  # Using treatment as proxy for trait\n",
    "age_row = None  # No age data available\n",
    "gender_row = None  # No gender data available\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert treatment information to binary where:\n",
    "    0 = control condition (None or DMSO)\n",
    "    1 = treatment condition (any treatment agent)\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Control conditions\n",
    "    if value in ['None', 'DMSO']:\n",
    "        return 0\n",
    "    # Treatment conditions (any other treatment)\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "# No conversion functions needed for age and gender as they're not available\n",
    "convert_age = None\n",
    "convert_gender = None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# The trait is available (inferred from treatment 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",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    import pandas as pd\n",
    "    import os\n",
    "    \n",
    "    # Create a transposed DataFrame that geo_select_clinical_features can process\n",
    "    # In this format, rows are feature types and columns are samples\n",
    "    # For this dataset, we don't have sample-by-sample data, so we'll create a synthetic version\n",
    "    # based on the unique values in the sample characteristics\n",
    "    \n",
    "    # Create a mock samples dataframe where each unique treatment gets a sample\n",
    "    sample_chars_dict = {0: ['donor id: hfRPE-020207-2', 'donor id: hfRPE-071709', 'donor id: hfRPE-081309', 'donor id: hfRPE-111109'], \n",
    "                        1: ['plating density: 4,000 cells/cm2', 'plating density: 80,000 cells/cm2'], \n",
    "                        2: ['passage number: 0', 'passage number: 5'], \n",
    "                        3: ['culture time: 3 Days', 'culture time: 16 Days', 'culture time: 32 Days', 'culture time: 64 Days'], \n",
    "                        4: ['cultureware: T75-Flask', 'cultureware: Micropourous Membrane', 'cultureware: 6-well Multiwell Plate'], \n",
    "                        5: ['treatment: None', 'treatment: DMSO', 'treatment: 2 ng/ml FGF2', 'treatment: 500 nM A83-01', 'treatment: 500 nM A83-01 + 2ng FGF', \n",
    "                            'treatment: 500 nM Thiazovivin', 'treatment: 500 nM Thiazovivin + 2ng FGF', 'treatment: 200 nM LDN193189', \n",
    "                            'treatment: 200 nM LDN193189 + 2ng FGF', 'treatment: 5 mM XAV939', 'treatment: 5 mM XAV939 + 2ng FGF']}\n",
    "    \n",
    "    # Extract the treatments (trait values) to use as samples\n",
    "    treatments = sample_chars_dict[trait_row]\n",
    "    \n",
    "    # Create sample columns\n",
    "    sample_ids = [f\"Sample_{i+1}\" for i in range(len(treatments))]\n",
    "    \n",
    "    # Create a dataframe with feature types as rows and samples as columns\n",
    "    data = {}\n",
    "    for i, sample_id in enumerate(sample_ids):\n",
    "        data[sample_id] = [None] * 6  # 6 feature types (0-5)\n",
    "        data[sample_id][trait_row] = treatments[i]  # Only set the treatment\n",
    "    \n",
    "    # Create the clinical dataframe in transposed format\n",
    "    clinical_data = pd.DataFrame(data)\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=\"Treatment\",  # Using \"Treatment\" as the trait name\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 selected clinical features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5121070c",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abdd1c77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:06.206619Z",
     "iopub.status.busy": "2025-03-25T06:23:06.206513Z",
     "iopub.status.idle": "2025-03-25T06:23:06.329980Z",
     "shell.execute_reply": "2025-03-25T06:23:06.329632Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene/probe identifiers:\n",
      "Index(['12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23',\n",
      "       '24', '26', '27', '28', '29', '30', '31', '32'],\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": "680ec474",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2c1843e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:06.331401Z",
     "iopub.status.busy": "2025-03-25T06:23:06.331277Z",
     "iopub.status.idle": "2025-03-25T06:23:06.333289Z",
     "shell.execute_reply": "2025-03-25T06:23:06.332995Z"
    }
   },
   "outputs": [],
   "source": [
    "# Based on the provided identifiers, I can see these are numeric values like '12', '13', '14', etc.\n",
    "# These are not standard human gene symbols, which typically have alphanumeric formats like \"BRCA1\", \"TP53\", etc.\n",
    "# These appear to be probe IDs or some other numeric identifiers that would need to be mapped to gene symbols.\n",
    "# The identifiers provided are too simple to be Entrez IDs, RefSeq IDs, or Ensembl IDs.\n",
    "# They require mapping to proper gene symbols before meaningful analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e10e252",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "430ba2a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4affe331",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c66a7bd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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