{
 "cells": [
  {
   "cell_type": "code",
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   "id": "fa18649a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:10:01.642928Z",
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     "shell.execute_reply": "2025-03-25T04:10:01.826302Z"
    }
   },
   "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 = \"Thyroid_Cancer\"\n",
    "cohort = \"GSE104006\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Thyroid_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Thyroid_Cancer/GSE104006\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Thyroid_Cancer/GSE104006.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Thyroid_Cancer/gene_data/GSE104006.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Thyroid_Cancer/clinical_data/GSE104006.csv\"\n",
    "json_path = \"../../output/preprocess/Thyroid_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fdb4331",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0abfbf9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:10:01.828088Z",
     "iopub.status.busy": "2025-03-25T04:10:01.827931Z",
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     "shell.execute_reply": "2025-03-25T04:10:01.925896Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"miRNA and gene expression profiling in human thyroid carcinomas and non-neoplastic thyroids\"\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: ['disease: Thyroid_carcinoma', 'disease: Non-neoplastic_thyroid'], 1: ['histology: PDTC', 'histology: PDTC+ATC', 'histology: PTC', 'histology: Non-neoplastic_thyroid', 'histology: PDTC+PTC', 'histology: PTC_lymph_node_metastasis', 'histology: PTC+PDTC'], 2: ['age: 74', 'age: 67', 'age: 72', 'age: 38', 'age: 50', 'age: 41', 'age: 51', 'age: 73', 'age: 52', 'age: 48', 'age: 59', 'age: 58', 'age: 39', 'age: 37', 'age: 33', 'age: 36', 'age: 70', 'age: 26', 'age: 46', 'age: 57', 'age: 44', 'age: 35', 'age: 42', 'age: 61', 'age: 49'], 3: ['Sex: M', 'Sex: F']}\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": "04766a5e",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7d7ff09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:10:01.927465Z",
     "iopub.status.busy": "2025-03-25T04:10:01.927348Z",
     "iopub.status.idle": "2025-03-25T04:10:01.933357Z",
     "shell.execute_reply": "2025-03-25T04:10:01.933065Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data file not found at ../../input/GEO/Thyroid_Cancer/GSE104006/clinical_data.csv\n",
      "Skipping clinical feature extraction.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "import json\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the title and summary, this dataset appears to contain gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# For trait, key 0 contains disease status (Thyroid_carcinoma vs Non-neoplastic_thyroid)\n",
    "trait_row = 0\n",
    "# For age, key 2 contains age information\n",
    "age_row = 2\n",
    "# For gender, key 3 contains sex information\n",
    "gender_row = 3\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary format: 1 for Thyroid_carcinoma, 0 for Non-neoplastic_thyroid.\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        # Extract the value after the colon\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        if value.lower() == 'thyroid_carcinoma':\n",
    "            return 1\n",
    "        elif value.lower() == 'non-neoplastic_thyroid':\n",
    "            return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous format.\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        # Extract the value after the colon\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        try:\n",
    "            return float(value)\n",
    "        except ValueError:\n",
    "            pass\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary format: 1 for male, 0 for female.\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        # Extract the value after the colon\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        if value.upper() == 'M':\n",
    "            return 1\n",
    "        elif value.upper() == 'F':\n",
    "            return 0\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\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",
    "# For this step, we'll check if clinical data exists before processing\n",
    "if trait_row is not None:\n",
    "    clinical_file_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
    "    \n",
    "    if os.path.exists(clinical_file_path):\n",
    "        try:\n",
    "            # Load the clinical data\n",
    "            clinical_data = pd.read_csv(clinical_file_path)\n",
    "            \n",
    "            # Use the function to extract clinical features\n",
    "            selected_clinical_df = 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 dataframe\n",
    "            preview = preview_df(selected_clinical_df)\n",
    "            print(\"Preview of selected clinical features:\")\n",
    "            print(preview)\n",
    "            \n",
    "            # Save the processed 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",
    "        except Exception as e:\n",
    "            print(f\"Error processing clinical data: {e}\")\n",
    "    else:\n",
    "        print(f\"Clinical data file not found at {clinical_file_path}\")\n",
    "        print(\"Skipping clinical feature extraction.\")\n",
    "else:\n",
    "    print(\"No trait data available, skipping clinical feature extraction.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8763f82e",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f26ebc34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:10:01.934436Z",
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     "shell.execute_reply": "2025-03-25T04:10:02.068723Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SOFT file: ../../input/GEO/Thyroid_Cancer/GSE104006/GSE104006_family.soft.gz\n",
      "Matrix file: ../../input/GEO/Thyroid_Cancer/GSE104006/GSE104006-GPL14951_series_matrix.txt.gz\n",
      "Found the matrix table marker in the file.\n",
      "Gene data shape: (29377, 34)\n",
      "First 20 gene/probe identifiers:\n",
      "['ILMN_1343291', 'ILMN_1651209', 'ILMN_1651228', 'ILMN_1651229', 'ILMN_1651235', 'ILMN_1651236', 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651254', 'ILMN_1651260', 'ILMN_1651262', 'ILMN_1651268', 'ILMN_1651278', 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651286', 'ILMN_1651292', 'ILMN_1651303', 'ILMN_1651309', 'ILMN_1651315']\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# Set gene availability flag\n",
    "is_gene_available = True  # Initially assume gene data is available\n",
    "\n",
    "# First check if the matrix file contains the expected marker\n",
    "found_marker = False\n",
    "try:\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        for line in file:\n",
    "            if \"!series_matrix_table_begin\" in line:\n",
    "                found_marker = True\n",
    "                break\n",
    "    \n",
    "    if found_marker:\n",
    "        print(\"Found the matrix table marker in the file.\")\n",
    "    else:\n",
    "        print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
    "        \n",
    "    # Try to extract gene data from the matrix file\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    \n",
    "    if gene_data.shape[0] == 0:\n",
    "        print(\"Warning: Extracted gene data has 0 rows.\")\n",
    "        is_gene_available = False\n",
    "    else:\n",
    "        print(f\"Gene data shape: {gene_data.shape}\")\n",
    "        # Print the first 20 gene/probe identifiers\n",
    "        print(\"First 20 gene/probe identifiers:\")\n",
    "        print(gene_data.index[:20].tolist())\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n",
    "    is_gene_available = False\n",
    "    \n",
    "    # Try to diagnose the file format\n",
    "    print(\"Examining file content to diagnose the issue:\")\n",
    "    try:\n",
    "        with gzip.open(matrix_file, 'rt') as file:\n",
    "            for i, line in enumerate(file):\n",
    "                if i < 10:  # Print first 10 lines to diagnose\n",
    "                    print(f\"Line {i}: {line.strip()[:100]}...\")  # Print first 100 chars of each line\n",
    "                else:\n",
    "                    break\n",
    "    except Exception as e2:\n",
    "        print(f\"Error examining file: {e2}\")\n",
    "\n",
    "if not is_gene_available:\n",
    "    print(\"Gene expression data could not be successfully extracted from this dataset.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41a2a49f",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de37464e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:10:02.070371Z",
     "iopub.status.busy": "2025-03-25T04:10:02.070251Z",
     "iopub.status.idle": "2025-03-25T04:10:02.072374Z",
     "shell.execute_reply": "2025-03-25T04:10:02.072079Z"
    }
   },
   "outputs": [],
   "source": [
    "# Reviewing the gene identifiers from the output\n",
    "# These identifiers like 'hsa-let-7a-3p', 'hsa-let-7b-5p' appear to be microRNA identifiers\n",
    "# The 'hsa-' prefix indicates human (Homo sapiens) microRNAs\n",
    "# These are standard microRNA nomenclature, not gene symbols like BRCA1 or TP53\n",
    "# They don't require mapping to gene symbols as they're already in a standard format\n",
    "\n",
    "requires_gene_mapping = False"
   ]
  }
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