{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b399e2f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:38.324562Z",
     "iopub.status.busy": "2025-03-25T06:41:38.324455Z",
     "iopub.status.idle": "2025-03-25T06:41:38.492988Z",
     "shell.execute_reply": "2025-03-25T06:41:38.492630Z"
    }
   },
   "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 = \"GSE188424\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Asthma\"\n",
    "in_cohort_dir = \"../../input/GEO/Asthma/GSE188424\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Asthma/GSE188424.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Asthma/gene_data/GSE188424.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Asthma/clinical_data/GSE188424.csv\"\n",
    "json_path = \"../../output/preprocess/Asthma/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dd7ac45",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f796413",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:38.494465Z",
     "iopub.status.busy": "2025-03-25T06:41:38.494328Z",
     "iopub.status.idle": "2025-03-25T06:41:38.789532Z",
     "shell.execute_reply": "2025-03-25T06:41:38.789157Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression profiling of peripheral blood from uncontrolled and controlled asthma\"\n",
      "!Series_summary\t\"We analyzed the transcriptomes of children with controlled and uncontrolled asthma in Taiwanese Consortium of Childhood Asthma Study (TCCAS). Hierarchical clustering, differentially expressed gene (DEG), weighted gene co-expression network analysis (WGCNA) and pathway enrichment methods were performed, to investigate important genes between two groups.\"\n",
      "!Series_overall_design\t\"Analysis of gene expression obtained from human whole blood comparing uncontrolled and controlled asthma.\"\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": "d6284a22",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6de9ed63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:38.790897Z",
     "iopub.status.busy": "2025-03-25T06:41:38.790786Z",
     "iopub.status.idle": "2025-03-25T06:41:38.797220Z",
     "shell.execute_reply": "2025-03-25T06:41:38.796891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Based on the provided information, let's analyze this dataset:\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# The series summary mentions \"transcriptomes\" and \"gene expression profiling\"\n",
    "# which strongly indicates gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# Trait (Asthma control status) is mentioned in the background information\n",
    "# However, we cannot locate it in the sample characteristics dictionary\n",
    "trait_row = None  # Cannot find in sample characteristics\n",
    "is_trait_available = False  # Since trait_row is None\n",
    "\n",
    "# Age data is not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender data is available at key 0\n",
    "gender_row = 0\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "# For trait (when we locate it):\n",
    "def convert_trait(val):\n",
    "    if val is None:\n",
    "        return None\n",
    "    val = val.lower().split(': ')[-1].strip()\n",
    "    if 'uncontrolled' in val:\n",
    "        return 1\n",
    "    elif 'controlled' in val:\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "# For age (if we found it, which we didn't):\n",
    "def convert_age(val):\n",
    "    if val is None:\n",
    "        return None\n",
    "    try:\n",
    "        # Extract the value after the colon and convert to float\n",
    "        return float(val.split(': ')[-1].strip())\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# For gender:\n",
    "def convert_gender(val):\n",
    "    if val is None:\n",
    "        return None\n",
    "    val = val.lower().split(': ')[-1].strip()\n",
    "    if 'female' in val:\n",
    "        return 0\n",
    "    elif 'male' in val:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Trait data is not available in the sample characteristics\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",
    "# Skip this substep since trait_row is None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a8faf5",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "181ccba0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:38.798444Z",
     "iopub.status.busy": "2025-03-25T06:41:38.798339Z",
     "iopub.status.idle": "2025-03-25T06:41:39.307150Z",
     "shell.execute_reply": "2025-03-25T06:41:39.306739Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Asthma/GSE188424/GSE188424_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": "acb57858",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f484fbb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:39.308589Z",
     "iopub.status.busy": "2025-03-25T06:41:39.308462Z",
     "iopub.status.idle": "2025-03-25T06:41:39.310567Z",
     "shell.execute_reply": "2025-03-25T06:41:39.310241Z"
    }
   },
   "outputs": [],
   "source": [
    "# The identifiers starting with ILMN_ are Illumina probe IDs, not human gene symbols\n",
    "# These are specific to Illumina microarray platforms and need to be mapped to human gene symbols\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0775a3",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec2b68cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:39.311678Z",
     "iopub.status.busy": "2025-03-25T06:41:39.311572Z",
     "iopub.status.idle": "2025-03-25T06:41:48.651871Z",
     "shell.execute_reply": "2025-03-25T06:41:48.651510Z"
    }
   },
   "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": "014ebb6b",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea26d19c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:48.653254Z",
     "iopub.status.busy": "2025-03-25T06:41:48.653125Z",
     "iopub.status.idle": "2025-03-25T06:41:50.435635Z",
     "shell.execute_reply": "2025-03-25T06:41:50.435236Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mapping dataframe shape: (44837, 2)\n",
      "First few rows of mapping dataframe:\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-level expression data shape: (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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene expression data shape: (20238, 99)\n",
      "First few normalized gene symbols:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT',\n",
      "       'A4GNT', 'AAA1', 'AAAS'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved gene expression data to ../../output/preprocess/Asthma/gene_data/GSE188424.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the relevant columns for gene mapping\n",
    "# From examining the preview, we can see:\n",
    "# - 'ID' column contains identifiers matching those in the gene expression data (ILMN_*)\n",
    "# - 'Symbol' column contains gene symbols we want to map to\n",
    "\n",
    "# 2. Get the gene mapping dataframe by extracting the identifier and symbol columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"First few rows of mapping dataframe:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene-level expression\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Gene-level expression data shape: {gene_data.shape}\")\n",
    "print(\"First few gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# 4. Normalize gene symbols to ensure consistency (optional but recommended)\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First few normalized gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# 5. Save the processed gene expression 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\"Saved gene expression data to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c21032",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ade5f9e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:41:50.437053Z",
     "iopub.status.busy": "2025-03-25T06:41:50.436918Z",
     "iopub.status.idle": "2025-03-25T06:41:53.975418Z",
     "shell.execute_reply": "2025-03-25T06:41:53.975020Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data shape: (1, 100)\n",
      "Clinical data column names: ['!Sample_geo_accession', 'GSM5681954', 'GSM5681955', 'GSM5681956', 'GSM5681957', 'GSM5681958', 'GSM5681959', 'GSM5681960', 'GSM5681961', 'GSM5681962', 'GSM5681963', 'GSM5681964', 'GSM5681965', 'GSM5681966', 'GSM5681967', 'GSM5681968', 'GSM5681969', 'GSM5681970', 'GSM5681971', 'GSM5681972', 'GSM5681973', 'GSM5681974', 'GSM5681975', 'GSM5681976', 'GSM5681977', 'GSM5681978', 'GSM5681979', 'GSM5681980', 'GSM5681981', 'GSM5681982', 'GSM5681983', 'GSM5681984', 'GSM5681985', 'GSM5681986', 'GSM5681987', 'GSM5681988', 'GSM5681989', 'GSM5681990', 'GSM5681991', 'GSM5681992', 'GSM5681993', 'GSM5681994', 'GSM5681995', 'GSM5681996', 'GSM5681997', 'GSM5681998', 'GSM5681999', 'GSM5682000', 'GSM5682001', 'GSM5682002', 'GSM5682003', 'GSM5682004', 'GSM5682005', 'GSM5682006', 'GSM5682007', 'GSM5682008', 'GSM5682009', 'GSM5682010', 'GSM5682011', 'GSM5682012', 'GSM5682013', 'GSM5682014', 'GSM5682015', 'GSM5682016', 'GSM5682017', 'GSM5682018', 'GSM5682019', 'GSM5682020', 'GSM5682021', 'GSM5682022', 'GSM5682023', 'GSM5682024', 'GSM5682025', 'GSM5682026', 'GSM5682027', 'GSM5682028', 'GSM5682029', 'GSM5682030', 'GSM5682031', 'GSM5682032', 'GSM5682033', 'GSM5682034', 'GSM5682035', 'GSM5682036', 'GSM5682037', 'GSM5682038', 'GSM5682039', 'GSM5682040', 'GSM5682041', 'GSM5682042', 'GSM5682043', 'GSM5682044', 'GSM5682045', 'GSM5682046', 'GSM5682047', 'GSM5682048', 'GSM5682049', 'GSM5682050', 'GSM5682051', 'GSM5682052']\n",
      "Sample characteristics: {0: ['gender: female', 'gender: male']}\n",
      "Gene data shape: (47235, 99)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data saved to ../../output/preprocess/Asthma/gene_data/GSE188424.csv\n",
      "Dataset usability status: False\n",
      "No linked data file saved since trait data is unavailable.\n"
     ]
    }
   ],
   "source": [
    "# First, re-extract the necessary files from the cohort directory\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# Get the gene data again \n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# Read background information and clinical data again\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",
    "# Examine the clinical data structure to see what's actually available\n",
    "print(\"Clinical data shape:\", clinical_data.shape)\n",
    "print(\"Clinical data column names:\", clinical_data.columns.tolist())\n",
    "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "print(\"Sample characteristics:\", sample_characteristics_dict)\n",
    "\n",
    "# Since we previously determined trait data is not available (trait_row = None),\n",
    "# we can't create proper clinical data for this dataset\n",
    "is_trait_available = False\n",
    "\n",
    "# The gene data has already been normalized and saved in previous steps\n",
    "print(f\"Gene data shape: {gene_data.shape}\")\n",
    "\n",
    "# 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\"Gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Since trait data is not available, use is_final=False in validate_and_save_cohort_info\n",
    "# This bypasses the need for the is_biased parameter\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=is_trait_available\n",
    ")\n",
    "\n",
    "print(f\"Dataset usability status: {is_usable}\")\n",
    "print(\"No linked data file saved since trait data is unavailable.\")"
   ]
  }
 ],
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