{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97b9a348",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:32:10.911309Z",
     "iopub.status.busy": "2025-03-25T06:32:10.911087Z",
     "iopub.status.idle": "2025-03-25T06:32:11.078444Z",
     "shell.execute_reply": "2025-03-25T06:32:11.078091Z"
    }
   },
   "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 = \"Anxiety_disorder\"\n",
    "cohort = \"GSE94119\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Anxiety_disorder\"\n",
    "in_cohort_dir = \"../../input/GEO/Anxiety_disorder/GSE94119\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Anxiety_disorder/GSE94119.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Anxiety_disorder/gene_data/GSE94119.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Anxiety_disorder/clinical_data/GSE94119.csv\"\n",
    "json_path = \"../../output/preprocess/Anxiety_disorder/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d177aa0c",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5858111e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:32:11.079893Z",
     "iopub.status.busy": "2025-03-25T06:32:11.079744Z",
     "iopub.status.idle": "2025-03-25T06:32:11.171697Z",
     "shell.execute_reply": "2025-03-25T06:32:11.171393Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression and response to psychological therapy\"\n",
      "!Series_summary\t\"This study represents the first investigation of genome-wide expression profiles with respect to psychological treatment outcome. Participants (n=102) with panic disorder or specific phobia received exposure-based CBT. Treatment outcome was defined as percentage reduction from baseline in clinician-rated severity of their primary anxiety diagnosis at post-treatment and six month follow-up. Gene expression was determined from whole blood samples at 3 time-points using the Illumina HT-12v4 BeadChip microarray. No changes in gene expression were significantly associated with treatment outcomes when correcting for multiple testing (q<0.05), although a small number of genes showed a suggestive association with treatment outcome (q<0.5, n=20). Study reports suggestive evidence for the role of a small number of genes in treatment outcome. Although preliminary, the findings contribute to a growing body of research suggesting that response to psychological therapies may be associated with changes at a biological level.\"\n",
      "!Series_overall_design\t\"Whole blood RNA was collected from patients (n=102) receiving exposure-based CBT at pre- and post-treatment and at follow-up, for investigation of association with therapy outcome. Includes 9 technical replicates.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['gender: FEMALE', 'gender: MALE'], 1: ['tissue: Blood'], 2: ['timepoint: pre', 'timepoint: post', 'timepoint: follow-up']}\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": "6870bcc5",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24a791d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:32:11.172835Z",
     "iopub.status.busy": "2025-03-25T06:32:11.172725Z",
     "iopub.status.idle": "2025-03-25T06:32:11.178179Z",
     "shell.execute_reply": "2025-03-25T06:32:11.177886Z"
    }
   },
   "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 microarray study using Illumina HT-12v4 BeadChip\n",
    "# for gene expression profiling, so gene expression data should be available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (anxiety disorder):\n",
    "# The data doesn't explicitly state anxiety disorder status in the characteristics dictionary,\n",
    "# but from the background information, we know all participants have either panic disorder or \n",
    "# specific phobia, which are types of anxiety disorders. \n",
    "# But there's no row key that distinguishes between different anxiety disorders or severity.\n",
    "trait_row = None\n",
    "\n",
    "# For age:\n",
    "# There's no age information in the sample characteristics dictionary\n",
    "age_row = None\n",
    "\n",
    "# For gender:\n",
    "# Gender is available at index 0\n",
    "gender_row = 0\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "# Since trait data is not available in a usable form for our analysis\n",
    "def convert_trait(value):\n",
    "    return None\n",
    "\n",
    "# Since age data is not available\n",
    "def convert_age(value):\n",
    "    return None\n",
    "\n",
    "# Convert gender to binary (0 for female, 1 for male)\n",
    "def convert_gender(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':')[1].strip()\n",
    "    \n",
    "    if value.upper() == 'FEMALE':\n",
    "        return 0\n",
    "    elif value.upper() == 'MALE':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available (trait_row is not None)\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort info\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, this dataset doesn't have the necessary trait data for our analysis,\n",
    "# so we skip the clinical feature extraction step\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a1009c9",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba2fe0ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:32:11.179217Z",
     "iopub.status.busy": "2025-03-25T06:32:11.179111Z",
     "iopub.status.idle": "2025-03-25T06:32:11.352214Z",
     "shell.execute_reply": "2025-03-25T06:32:11.351740Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651228', 'ILMN_1651254',\n",
      "       'ILMN_1651262', 'ILMN_1651315', 'ILMN_1651347', 'ILMN_1651378',\n",
      "       'ILMN_1651405', 'ILMN_1651680', 'ILMN_1651692', 'ILMN_1651705',\n",
      "       'ILMN_1651719', 'ILMN_1651735', 'ILMN_1651788', 'ILMN_1651799',\n",
      "       'ILMN_1651826', 'ILMN_1651832', 'ILMN_1651850', 'ILMN_1651886'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 4381 genes × 315 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": "1c35b8ee",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da1cd796",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:32:11.353691Z",
     "iopub.status.busy": "2025-03-25T06:32:11.353572Z",
     "iopub.status.idle": "2025-03-25T06:32:11.355482Z",
     "shell.execute_reply": "2025-03-25T06:32:11.355189Z"
    }
   },
   "outputs": [],
   "source": [
    "# Reviewing the gene identifiers\n",
    "\n",
    "# The identifiers starting with \"ILMN_\" are Illumina probe IDs, not direct human gene symbols\n",
    "# These are probe identifiers used in Illumina microarray platforms and need to be mapped to human gene symbols\n",
    "# for proper biological interpretation and cross-platform compatibility\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdde4ce1",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebc0e296",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:32:11.356699Z",
     "iopub.status.busy": "2025-03-25T06:32:11.356594Z",
     "iopub.status.idle": "2025-03-25T06:32:15.013158Z",
     "shell.execute_reply": "2025-03-25T06:32:15.012763Z"
    }
   },
   "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. 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": "e8e0dadf",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b56bc108",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:32:15.014550Z",
     "iopub.status.busy": "2025-03-25T06:32:15.014425Z",
     "iopub.status.idle": "2025-03-25T06:32:15.142288Z",
     "shell.execute_reply": "2025-03-25T06:32:15.141937Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping preview:\n",
      "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Gene': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB']}\n",
      "\n",
      "Gene data after mapping preview:\n",
      "Shape: (3270, 315)\n",
      "First 5 gene symbols: ['AARS', 'AATF', 'ABCF1', 'ABHD5', 'ABI1']\n"
     ]
    }
   ],
   "source": [
    "# Identify the columns in the gene annotation dataframe\n",
    "# For the gene identifiers, we need 'ID' column which contains the ILMN_* probe IDs\n",
    "# For gene symbols, we need the 'Symbol' column\n",
    "\n",
    "# 1. Create a gene mapping dataframe with the probe IDs and gene symbols\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "\n",
    "# 2. Print a preview of the mapping dataframe to verify\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply the gene mapping to convert the probe-level gene expression data to gene-level data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# 4. Print a preview of the gene expression data after mapping\n",
    "print(\"\\nGene data after mapping preview:\")\n",
    "print(f\"Shape: {gene_data.shape}\")\n",
    "print(f\"First 5 gene symbols: {list(gene_data.index[:5])}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ccaa06f",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d22caa05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:32:15.143700Z",
     "iopub.status.busy": "2025-03-25T06:32:15.143577Z",
     "iopub.status.idle": "2025-03-25T06:32:15.898744Z",
     "shell.execute_reply": "2025-03-25T06:32:15.898403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data after normalization: shape (3207, 315)\n",
      "First 5 normalized gene symbols: ['AARS1', 'AATF', 'ABCF1', 'ABHD5', 'ABI1']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Anxiety_disorder/gene_data/GSE94119.csv\n",
      "Clinical data saved to ../../output/preprocess/Anxiety_disorder/clinical_data/GSE94119.csv\n",
      "Linked data shape: (315, 3208)\n",
      "Trait data is unavailable in this dataset.\n",
      "Dataset deemed not usable for anxiety disorder association studies due to missing trait measurements.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "try:\n",
    "    # Use the NCBI gene synonym information to normalize gene symbols\n",
    "    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "    print(f\"Gene data after normalization: shape {normalized_gene_data.shape}\")\n",
    "    print(f\"First 5 normalized gene symbols: {normalized_gene_data.index[:5].tolist()}\")\n",
    "    \n",
    "    # Save the normalized gene data\n",
    "    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "    normalized_gene_data.to_csv(out_gene_data_file)\n",
    "    print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "    \n",
    "    # Use the normalized gene data for subsequent steps\n",
    "    gene_data_final = normalized_gene_data\n",
    "except Exception as e:\n",
    "    print(f\"Error during normalization: {e}\")\n",
    "    print(\"Using original gene data instead.\")\n",
    "    gene_data_final = gene_data\n",
    "    \n",
    "    # Save the original gene data\n",
    "    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "    gene_data_final.to_csv(out_gene_data_file)\n",
    "    print(f\"Original gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Create clinical data with gender information (since trait data is unavailable)\n",
    "if gender_row is not None:\n",
    "    # Create a DataFrame with just gender information\n",
    "    gender_data = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender)\n",
    "    clinical_df = gender_data\n",
    "    \n",
    "    # Save clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    \n",
    "    # Link clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data_final)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "else:\n",
    "    print(\"No clinical features available to link with gene expression data.\")\n",
    "    # Create a minimal DataFrame with gene expression data\n",
    "    linked_data = gene_data_final.T  # Transpose to have samples as rows\n",
    "\n",
    "# 3. Since trait data is unavailable, we can't perform trait-specific operations\n",
    "# but we can still handle missing values in the gene expression data\n",
    "is_trait_available = False\n",
    "print(\"Trait data is unavailable in this dataset.\")\n",
    "\n",
    "# 4. Since trait data is unavailable, the dataset is not usable for trait association studies\n",
    "is_biased = True  # Not applicable since trait is unavailable\n",
    "\n",
    "# 5. Validate and save cohort info\n",
    "note = \"This dataset contains human anxiety disorder gene expression data, but lacks specific anxiety disorder trait measurements (e.g., severity scores) for association studies.\"\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=True,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Don't save linked data as it's not usable for trait association studies\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset deemed not usable for anxiety disorder association studies due to missing trait measurements.\")"
   ]
  }
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