{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34d61bd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:56.021396Z",
     "iopub.status.busy": "2025-03-25T06:41:56.021215Z",
     "iopub.status.idle": "2025-03-25T06:41:56.191268Z",
     "shell.execute_reply": "2025-03-25T06:41:56.190912Z"
    }
   },
   "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 = \"Asthma\"\n",
    "cohort = \"GSE230164\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Asthma\"\n",
    "in_cohort_dir = \"../../input/GEO/Asthma/GSE230164\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Asthma/GSE230164.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Asthma/gene_data/GSE230164.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Asthma/clinical_data/GSE230164.csv\"\n",
    "json_path = \"../../output/preprocess/Asthma/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92bfef84",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95c1d853",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:56.192760Z",
     "iopub.status.busy": "2025-03-25T06:41:56.192620Z",
     "iopub.status.idle": "2025-03-25T06:41:56.489929Z",
     "shell.execute_reply": "2025-03-25T06:41:56.489587Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression profiling of asthma\"\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: ['gender: female', 'gender: male']}\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": "913b1076",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b39b3aaf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:56.491259Z",
     "iopub.status.busy": "2025-03-25T06:41:56.491144Z",
     "iopub.status.idle": "2025-03-25T06:41:56.498558Z",
     "shell.execute_reply": "2025-03-25T06:41:56.498241Z"
    }
   },
   "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, this is a SuperSeries about gene expression profiling of asthma\n",
    "# This indicates it likely contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# From the sample characteristics dictionary, we can see gender information is available at index 0\n",
    "# There's no explicit trait (asthma) or age information in the sample characteristics\n",
    "trait_row = None  # Trait information not directly available\n",
    "age_row = None    # Age information not available\n",
    "gender_row = 0     # Gender information is at index 0\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "# For trait (unavailable, but defining function for completeness)\n",
    "def convert_trait(value):\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().lower()\n",
    "    else:\n",
    "        value = value.strip().lower()\n",
    "    \n",
    "    # Binary conversion for asthma\n",
    "    if 'asthma' in value or 'yes' in value or 'positive' in value or 'case' in value:\n",
    "        return 1\n",
    "    elif 'control' in value or 'no' in value or 'negative' in value or 'healthy' in value:\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "# For age (unavailable, but defining function for completeness)\n",
    "def convert_age(value):\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",
    "    else:\n",
    "        value = value.strip()\n",
    "    \n",
    "    # Try to convert to float for continuous age\n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# For gender\n",
    "def convert_gender(value):\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().lower()\n",
    "    else:\n",
    "        value = value.strip().lower()\n",
    "    \n",
    "    # Binary conversion: female=0, male=1\n",
    "    if 'female' in value or 'f' == value:\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Check if trait data is available (trait_row is not None)\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Conduct initial filtering and save 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 skip the clinical feature extraction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81b59e8",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fcba5192",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:56.499749Z",
     "iopub.status.busy": "2025-03-25T06:41:56.499645Z",
     "iopub.status.idle": "2025-03-25T06:41:57.002029Z",
     "shell.execute_reply": "2025-03-25T06:41:57.001553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Asthma/GSE230164/GSE230164-GPL10558_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (47235, 99)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n",
      "       'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n",
      "       'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n",
      "       'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n",
      "       'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\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": "385d8636",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf8612dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:57.003453Z",
     "iopub.status.busy": "2025-03-25T06:41:57.003333Z",
     "iopub.status.idle": "2025-03-25T06:41:57.005462Z",
     "shell.execute_reply": "2025-03-25T06:41:57.005138Z"
    }
   },
   "outputs": [],
   "source": [
    "# The gene identifiers start with \"ILMN_\" which indicates they are Illumina probe IDs\n",
    "# from the Illumina BeadArray platform. These are not human gene symbols but are \n",
    "# platform-specific probe IDs that need to be mapped to gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcf6388",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "379708e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:57.006677Z",
     "iopub.status.busy": "2025-03-25T06:41:57.006567Z",
     "iopub.status.idle": "2025-03-25T06:42:06.393784Z",
     "shell.execute_reply": "2025-03-25T06:42:06.393386Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0, 1450438.0, 1240647.0], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': [nan, nan, nan, nan, nan], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan]}\n"
     ]
    }
   ],
   "source": [
    "# 1. 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",
    "# 2. 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": "fef3f8cb",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "21c2ffae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:42:06.395250Z",
     "iopub.status.busy": "2025-03-25T06:42:06.395122Z",
     "iopub.status.idle": "2025-03-25T06:42:06.770591Z",
     "shell.execute_reply": "2025-03-25T06:42:06.770198Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping dataframe shape: (44837, 2)\n",
      "First few rows of gene mapping:\n",
      "             ID                     Gene\n",
      "0  ILMN_1343048      phage_lambda_genome\n",
      "1  ILMN_1343049      phage_lambda_genome\n",
      "2  ILMN_1343050  phage_lambda_genome:low\n",
      "3  ILMN_1343052  phage_lambda_genome:low\n",
      "4  ILMN_1343059                     thrB\n",
      "Gene expression data shape after mapping: (21440, 99)\n",
      "First few gene symbols:\n",
      "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n",
      "       'A4GALT', 'A4GNT'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the key columns from the gene annotation dictionary\n",
    "# The gene identifiers in the gene expression data are \"ILMN_\" IDs which map to the \"ID\" column in gene_annotation\n",
    "# The gene symbols are stored in the \"Symbol\" column in gene_annotation\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the two identified columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col=\"ID\", gene_col=\"Symbol\")\n",
    "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
    "print(\"First few rows of gene mapping:\")\n",
    "print(gene_mapping.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First few gene symbols:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc8557d0",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdf42161",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:42:06.772025Z",
     "iopub.status.busy": "2025-03-25T06:42:06.771903Z",
     "iopub.status.idle": "2025-03-25T06:42:08.167715Z",
     "shell.execute_reply": "2025-03-25T06:42:08.167323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape after normalization: (20238, 99)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Asthma/gene_data/GSE230164.csv\n",
      "No trait information available in this dataset.\n",
      "Dataset cannot be used for analysis because trait information is missing.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
    "\n",
    "# Create directory and save the normalized gene data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Since trait_row was determined to be None in step 2, we cannot extract clinical features\n",
    "# This means the dataset lacks the necessary trait information for our analysis\n",
    "print(\"No trait information available in this dataset.\")\n",
    "\n",
    "# Use is_final=False for validation since we lack trait information\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=True,\n",
    "    is_trait_available=False  # No trait information available\n",
    ")\n",
    "\n",
    "print(\"Dataset cannot be used for analysis because trait information is missing.\")"
   ]
  }
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