{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f7717e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:55:03.345212Z",
     "iopub.status.busy": "2025-03-25T06:55:03.344978Z",
     "iopub.status.idle": "2025-03-25T06:55:03.515957Z",
     "shell.execute_reply": "2025-03-25T06:55:03.515497Z"
    }
   },
   "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 = \"Bipolar_disorder\"\n",
    "cohort = \"GSE45484\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Bipolar_disorder\"\n",
    "in_cohort_dir = \"../../input/GEO/Bipolar_disorder/GSE45484\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Bipolar_disorder/GSE45484.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Bipolar_disorder/gene_data/GSE45484.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Bipolar_disorder/clinical_data/GSE45484.csv\"\n",
    "json_path = \"../../output/preprocess/Bipolar_disorder/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956aebd4",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60868666",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:55:03.517485Z",
     "iopub.status.busy": "2025-03-25T06:55:03.517333Z",
     "iopub.status.idle": "2025-03-25T06:55:03.751405Z",
     "shell.execute_reply": "2025-03-25T06:55:03.750905Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene-expression differences in peripheral blood between lithium responders and non-responders in the “Lithium Treatment -Moderate dose Use Study” (LiTMUS)\"\n",
      "!Series_summary\t\"Analysis of gene-expression changes in treatment responders vs non-responders to two different treatments among subjectrs participating in LiTMUS.\"\n",
      "!Series_summary\t\"Results provide information on pathways that may be involved in the clinical response to Lithium in patients with bipolar disorder.\"\n",
      "!Series_overall_design\t\"Total RNA isolated from PAXgene blood RNA tubes from 60 subjects with bipolar disorder, randomized to 2 treatment groups (OPT, Li+OPT) at 2 time-points (baseline, 1 month after treatment)\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['treatment group: OPT', 'treatment group: Li+OPT'], 1: ['time point: baseline', 'time point: 1 month'], 2: ['responder: NO', 'responder: YES'], 3: ['sex: M', 'sex: F'], 4: ['age: 46', 'age: 44', 'age: 59', 'age: 32', 'age: 45', 'age: 25', 'age: 26', 'age: 43', 'age: 24', 'age: 38', 'age: 47', 'age: 37', 'age: 57', 'age: 23', 'age: 30', 'age: 51', 'age: 35', 'age: 64', 'age: 53', 'age: 61', 'age: 39', 'age: 36', 'age: 18', 'age: 20', 'age: 27', 'age: 49', 'age: 29', 'age: 40', 'age: 41', 'age: 31']}\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": "7ba38fa7",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06628229",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:55:03.752948Z",
     "iopub.status.busy": "2025-03-25T06:55:03.752831Z",
     "iopub.status.idle": "2025-03-25T06:55:03.769937Z",
     "shell.execute_reply": "2025-03-25T06:55:03.769497Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical Data Preview:\n",
      "{'GSM1105438': [0.0, 46.0, 1.0], 'GSM1105439': [0.0, 44.0, 0.0], 'GSM1105440': [0.0, 46.0, 1.0], 'GSM1105441': [0.0, 44.0, 0.0], 'GSM1105442': [0.0, 59.0, 1.0], 'GSM1105443': [0.0, 32.0, 0.0], 'GSM1105444': [0.0, 59.0, 1.0], 'GSM1105445': [0.0, 32.0, 0.0], 'GSM1105446': [0.0, 45.0, 0.0], 'GSM1105447': [0.0, 25.0, 0.0], 'GSM1105448': [0.0, 45.0, 0.0], 'GSM1105449': [0.0, 25.0, 0.0], 'GSM1105450': [1.0, 25.0, 0.0], 'GSM1105451': [1.0, 26.0, 0.0], 'GSM1105452': [1.0, 25.0, 0.0], 'GSM1105453': [1.0, 26.0, 0.0], 'GSM1105454': [1.0, 43.0, 1.0], 'GSM1105455': [0.0, 24.0, 0.0], 'GSM1105456': [1.0, 43.0, 1.0], 'GSM1105457': [0.0, 24.0, 0.0], 'GSM1105458': [0.0, 43.0, 1.0], 'GSM1105459': [0.0, 43.0, 0.0], 'GSM1105460': [0.0, 43.0, 1.0], 'GSM1105461': [0.0, 43.0, 0.0], 'GSM1105462': [0.0, 38.0, 0.0], 'GSM1105463': [0.0, 47.0, 1.0], 'GSM1105464': [0.0, 38.0, 0.0], 'GSM1105465': [0.0, 47.0, 1.0], 'GSM1105466': [1.0, 37.0, 1.0], 'GSM1105467': [0.0, 57.0, 0.0], 'GSM1105468': [1.0, 37.0, 1.0], 'GSM1105469': [0.0, 57.0, 0.0], 'GSM1105470': [0.0, 23.0, 0.0], 'GSM1105471': [0.0, 57.0, 0.0], 'GSM1105472': [0.0, 23.0, 0.0], 'GSM1105473': [0.0, 57.0, 0.0], 'GSM1105474': [0.0, 30.0, 0.0], 'GSM1105475': [0.0, 37.0, 0.0], 'GSM1105476': [0.0, 30.0, 0.0], 'GSM1105477': [0.0, 37.0, 0.0], 'GSM1105478': [1.0, 51.0, 1.0], 'GSM1105479': [1.0, 35.0, 0.0], 'GSM1105480': [1.0, 51.0, 1.0], 'GSM1105481': [1.0, 35.0, 0.0], 'GSM1105482': [0.0, 64.0, 0.0], 'GSM1105483': [0.0, 45.0, 0.0], 'GSM1105484': [0.0, 64.0, 0.0], 'GSM1105485': [0.0, 45.0, 0.0], 'GSM1105486': [0.0, 53.0, 0.0], 'GSM1105487': [0.0, 57.0, 1.0], 'GSM1105488': [0.0, 53.0, 0.0], 'GSM1105489': [0.0, 57.0, 1.0], 'GSM1105490': [0.0, 25.0, 0.0], 'GSM1105491': [0.0, 61.0, 0.0], 'GSM1105492': [0.0, 25.0, 0.0], 'GSM1105493': [0.0, 61.0, 0.0], 'GSM1105494': [0.0, 44.0, 1.0], 'GSM1105495': [0.0, 39.0, 1.0], 'GSM1105496': [0.0, 44.0, 1.0], 'GSM1105497': [0.0, 39.0, 1.0], 'GSM1105498': [0.0, 26.0, 0.0], 'GSM1105499': [0.0, 45.0, 0.0], 'GSM1105500': [0.0, 26.0, 0.0], 'GSM1105501': [0.0, 45.0, 0.0], 'GSM1105502': [1.0, 53.0, 0.0], 'GSM1105503': [0.0, 51.0, 0.0], 'GSM1105504': [1.0, 53.0, 0.0], 'GSM1105505': [0.0, 51.0, 0.0], 'GSM1105506': [0.0, 36.0, 1.0], 'GSM1105507': [0.0, 45.0, 0.0], 'GSM1105508': [0.0, 36.0, 1.0], 'GSM1105509': [0.0, 45.0, 0.0], 'GSM1105510': [1.0, 38.0, 0.0], 'GSM1105511': [0.0, 18.0, 0.0], 'GSM1105512': [1.0, 38.0, 0.0], 'GSM1105513': [0.0, 18.0, 0.0], 'GSM1105514': [0.0, 20.0, 0.0], 'GSM1105515': [1.0, 27.0, 1.0], 'GSM1105516': [0.0, 20.0, 0.0], 'GSM1105517': [1.0, 27.0, 1.0], 'GSM1105518': [0.0, 49.0, 0.0], 'GSM1105519': [0.0, 43.0, 0.0], 'GSM1105520': [0.0, 49.0, 0.0], 'GSM1105521': [0.0, 43.0, 0.0], 'GSM1105522': [0.0, 29.0, 1.0], 'GSM1105523': [1.0, 20.0, 0.0], 'GSM1105524': [0.0, 29.0, 1.0], 'GSM1105525': [1.0, 20.0, 0.0], 'GSM1105526': [0.0, 32.0, 0.0], 'GSM1105527': [0.0, 40.0, 1.0], 'GSM1105528': [0.0, 32.0, 0.0], 'GSM1105529': [0.0, 40.0, 1.0], 'GSM1105530': [1.0, 59.0, 0.0], 'GSM1105531': [0.0, 41.0, 0.0], 'GSM1105532': [1.0, 59.0, 0.0], 'GSM1105533': [0.0, 41.0, 0.0], 'GSM1105534': [0.0, 20.0, 0.0], 'GSM1105535': [0.0, 31.0, 1.0], 'GSM1105536': [0.0, 20.0, 0.0], 'GSM1105537': [0.0, 31.0, 1.0], 'GSM1105538': [0.0, 29.0, 1.0], 'GSM1105539': [1.0, 49.0, 0.0], 'GSM1105540': [0.0, 29.0, 1.0], 'GSM1105541': [1.0, 49.0, 0.0], 'GSM1105542': [0.0, 52.0, 0.0], 'GSM1105543': [0.0, 22.0, 1.0], 'GSM1105544': [0.0, 52.0, 0.0], 'GSM1105545': [0.0, 22.0, 1.0], 'GSM1105546': [0.0, 52.0, 0.0], 'GSM1105547': [0.0, 39.0, 0.0], 'GSM1105548': [0.0, 52.0, 0.0], 'GSM1105549': [0.0, 39.0, 0.0], 'GSM1105550': [1.0, 27.0, 0.0], 'GSM1105551': [0.0, 57.0, 1.0], 'GSM1105552': [1.0, 27.0, 0.0], 'GSM1105553': [0.0, 57.0, 1.0], 'GSM1105554': [0.0, 27.0, 0.0], 'GSM1105555': [0.0, 36.0, 0.0], 'GSM1105556': [0.0, 27.0, 0.0], 'GSM1105557': [0.0, 36.0, 0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Bipolar_disorder/clinical_data/GSE45484.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "is_gene_available = True  # Based on the series title and summary, this dataset contains gene expression data\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "trait_row = 2  # 'responder: NO', 'responder: YES' - this represents bipolar disorder response to treatment\n",
    "age_row = 4    # Contains age information with multiple values\n",
    "gender_row = 3  # Contains sex information 'M' and 'F'\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary: YES=1 (responder), NO=0 (non-responder)\n",
    "    if value.upper() == \"YES\":\n",
    "        return 1\n",
    "    elif value.upper() == \"NO\":\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to integer\n",
    "    try:\n",
    "        return int(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary: F=0, M=1\n",
    "    if value.upper() == \"F\":\n",
    "        return 0\n",
    "    elif value.upper() == \"M\":\n",
    "        return 1\n",
    "    else:\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 (only if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # Get the 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 resulting dataframe\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Clinical Data Preview:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data to CSV\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": "f73cabab",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "84b16b22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:55:03.771268Z",
     "iopub.status.busy": "2025-03-25T06:55:03.771157Z",
     "iopub.status.idle": "2025-03-25T06:55:04.214033Z",
     "shell.execute_reply": "2025-03-25T06:55:04.213490Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Bipolar_disorder/GSE45484/GSE45484_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (47323, 120)\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": "fc03f25d",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "890f78ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:55:04.215429Z",
     "iopub.status.busy": "2025-03-25T06:55:04.215312Z",
     "iopub.status.idle": "2025-03-25T06:55:04.217694Z",
     "shell.execute_reply": "2025-03-25T06:55:04.217289Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers start with \"ILMN_\" which indicates they are Illumina BeadArray probe IDs\n",
    "# They are not human gene symbols and require mapping to standard gene symbols\n",
    "# Illumina probe IDs are specific to the microarray platform and need to be converted\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "130078fa",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7fb58729",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:55:04.219139Z",
     "iopub.status.busy": "2025-03-25T06:55:04.218988Z",
     "iopub.status.idle": "2025-03-25T06:55:15.103374Z",
     "shell.execute_reply": "2025-03-25T06:55:15.102727Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene annotation preview:\n",
      "Columns in gene annotation: ['ID', 'Species', 'Source', 'Search_Key', 'Transcript', 'ILMN_Gene', 'Source_Reference_ID', 'RefSeq_ID', 'Unigene_ID', 'Entrez_Gene_ID', 'GI', 'Accession', 'Symbol', 'Protein_Product', 'Probe_Id', 'Array_Address_Id', 'Probe_Type', 'Probe_Start', 'SEQUENCE', 'Chromosome', 'Probe_Chr_Orientation', 'Probe_Coordinates', 'Cytoband', 'Definition', 'Ontology_Component', 'Ontology_Process', 'Ontology_Function', 'Synonyms', 'Obsolete_Probe_Id', 'GB_ACC']\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",
      "\n",
      "First row as dictionary:\n",
      "ID: ILMN_1343048\n",
      "Species: nan\n",
      "Source: nan\n",
      "Search_Key: nan\n",
      "Transcript: nan\n",
      "ILMN_Gene: nan\n",
      "Source_Reference_ID: nan\n",
      "RefSeq_ID: nan\n",
      "Unigene_ID: nan\n",
      "Entrez_Gene_ID: nan\n",
      "GI: nan\n",
      "Accession: nan\n",
      "Symbol: phage_lambda_genome\n",
      "Protein_Product: nan\n",
      "Probe_Id: nan\n",
      "Array_Address_Id: 5090180.0\n",
      "Probe_Type: nan\n",
      "Probe_Start: nan\n",
      "SEQUENCE: GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA\n",
      "Chromosome: nan\n",
      "Probe_Chr_Orientation: nan\n",
      "Probe_Coordinates: nan\n",
      "Cytoband: nan\n",
      "Definition: nan\n",
      "Ontology_Component: nan\n",
      "Ontology_Process: nan\n",
      "Ontology_Function: nan\n",
      "Synonyms: nan\n",
      "Obsolete_Probe_Id: nan\n",
      "GB_ACC: nan\n",
      "\n",
      "Comparing gene data IDs with annotation IDs:\n",
      "First 5 gene data IDs: ['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209', 'ILMN_1651210']\n",
      "First 5 annotation IDs: ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Exact ID match between gene data and annotation:\n",
      "Matching IDs: 47323 out of 47323 (100.00%)\n",
      "\n",
      "Potential columns for gene symbols: ['ILMN_Gene', 'Unigene_ID', 'Entrez_Gene_ID', 'Symbol']\n",
      "Column 'ILMN_Gene': 47323 non-null values (0.83%)\n",
      "Column 'Unigene_ID': 3270 non-null values (0.06%)\n",
      "Column 'Entrez_Gene_ID': 43960 non-null values (0.77%)\n",
      "Column 'Symbol': 44837 non-null values (0.78%)\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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Check if there are any columns that might contain gene information\n",
    "sample_row = gene_annotation.iloc[0].to_dict()\n",
    "print(\"\\nFirst row as dictionary:\")\n",
    "for col, value in sample_row.items():\n",
    "    print(f\"{col}: {value}\")\n",
    "\n",
    "# Check if IDs in gene_data match IDs in annotation\n",
    "print(\"\\nComparing gene data IDs with annotation IDs:\")\n",
    "print(\"First 5 gene data IDs:\", gene_data.index[:5].tolist())\n",
    "print(\"First 5 annotation IDs:\", gene_annotation['ID'].head().tolist())\n",
    "\n",
    "# Properly check for exact ID matches between gene data and annotation\n",
    "gene_data_ids = set(gene_data.index)\n",
    "annotation_ids = set(gene_annotation['ID'].astype(str))\n",
    "matching_ids = gene_data_ids.intersection(annotation_ids)\n",
    "id_match_percentage = len(matching_ids) / len(gene_data_ids) * 100 if len(gene_data_ids) > 0 else 0\n",
    "\n",
    "print(f\"\\nExact ID match between gene data and annotation:\")\n",
    "print(f\"Matching IDs: {len(matching_ids)} out of {len(gene_data_ids)} ({id_match_percentage:.2f}%)\")\n",
    "\n",
    "# Check which columns might contain gene symbols for mapping\n",
    "potential_gene_symbol_cols = [col for col in gene_annotation.columns \n",
    "                             if any(term in col.upper() for term in ['GENE', 'SYMBOL', 'NAME'])]\n",
    "print(f\"\\nPotential columns for gene symbols: {potential_gene_symbol_cols}\")\n",
    "\n",
    "# Check if the identified columns contain non-null values\n",
    "for col in potential_gene_symbol_cols:\n",
    "    non_null_count = gene_annotation[col].notnull().sum()\n",
    "    non_null_percent = non_null_count / len(gene_annotation) * 100\n",
    "    print(f\"Column '{col}': {non_null_count} non-null values ({non_null_percent:.2f}%)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55ba1e5",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c96ec2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:55:15.105242Z",
     "iopub.status.busy": "2025-03-25T06:55:15.105120Z",
     "iopub.status.idle": "2025-03-25T06:55:16.904289Z",
     "shell.execute_reply": "2025-03-25T06:55:16.903634Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created mapping dataframe with shape: (44837, 2)\n",
      "Sample of mapping data:\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",
      "Created gene expression dataframe with shape: (21464, 120)\n",
      "First 5 genes and their expression values:\n",
      "       GSM1105438  GSM1105439  GSM1105440  GSM1105441  GSM1105442  GSM1105443  \\\n",
      "Gene                                                                            \n",
      "A1BG     14.03088    13.93148    13.81406    13.83391    13.78051    13.96313   \n",
      "A1CF     20.32521    20.63063    20.66692    20.54370    20.64116    20.71162   \n",
      "A26C3    20.39159    20.30175    20.52752    20.41063    20.47654    20.56898   \n",
      "A2BP1    27.15309    27.27428    26.94768    27.12319    27.09179    26.95093   \n",
      "A2LD1     7.32095     7.29238     7.47339     7.21109     7.26801     7.35792   \n",
      "\n",
      "       GSM1105444  GSM1105445  GSM1105446  GSM1105447  ...  GSM1105548  \\\n",
      "Gene                                                   ...               \n",
      "A1BG     13.99187    13.98965    13.65448    13.59944  ...    13.92483   \n",
      "A1CF     20.58564    20.50426    20.82332    20.55633  ...    20.55526   \n",
      "A26C3    20.29670    20.46836    20.55004    20.29735  ...    20.27635   \n",
      "A2BP1    27.35133    26.94886    27.07386    27.20497  ...    26.93445   \n",
      "A2LD1     7.45566     7.36758     6.93695     7.21794  ...     7.33647   \n",
      "\n",
      "       GSM1105549  GSM1105550  GSM1105551  GSM1105552  GSM1105553  GSM1105554  \\\n",
      "Gene                                                                            \n",
      "A1BG     13.72544    13.94040    13.70261    13.91181    13.70411    13.73513   \n",
      "A1CF     20.64901    20.55603    20.36035    20.75112    20.61628    20.46376   \n",
      "A26C3    20.61305    20.40383    20.40375    20.54260    20.47298    20.35977   \n",
      "A2BP1    27.09989    27.18521    27.42922    26.94529    26.91467    26.90594   \n",
      "A2LD1     7.09422     7.19649     7.22026     7.35474     7.58675     7.12003   \n",
      "\n",
      "       GSM1105555  GSM1105556  GSM1105557  \n",
      "Gene                                       \n",
      "A1BG     13.70526    13.75100    13.80716  \n",
      "A1CF     21.03313    20.70163    20.67360  \n",
      "A26C3    20.67486    20.61405    20.41787  \n",
      "A2BP1    27.04305    27.16635    27.29938  \n",
      "A2LD1     7.30742     7.14190     7.48781  \n",
      "\n",
      "[5 rows x 120 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Bipolar_disorder/gene_data/GSE45484.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine which columns to use for probe IDs and gene symbols\n",
    "# Based on previous step output, we need:\n",
    "# - The 'ID' column (contains probe IDs like ILMN_1343048) that matches gene_data index\n",
    "# - The 'Symbol' column (contains gene symbols) has 78% non-null values\n",
    "\n",
    "# 2. Get gene mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "print(f\"Created mapping dataframe with shape: {mapping_df.shape}\")\n",
    "print(f\"Sample of mapping data:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression\n",
    "gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)\n",
    "print(f\"Created gene expression dataframe with shape: {gene_data.shape}\")\n",
    "print(f\"First 5 genes and their expression values:\")\n",
    "print(gene_data.head())\n",
    "\n",
    "# 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\"Gene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2dd9ec2",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08577309",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:55:16.906242Z",
     "iopub.status.busy": "2025-03-25T06:55:16.906075Z",
     "iopub.status.idle": "2025-03-25T06:55:33.696705Z",
     "shell.execute_reply": "2025-03-25T06:55:33.696328Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape after normalization: (20259, 120)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene expression data saved to ../../output/preprocess/Bipolar_disorder/gene_data/GSE45484.csv\n",
      "Loaded clinical data shape: (3, 120)\n",
      "Selected clinical data shape: (3, 120)\n",
      "Clinical data preview:\n",
      "{'GSM1105438': [0.0, 46.0, 1.0], 'GSM1105439': [0.0, 44.0, 0.0], 'GSM1105440': [0.0, 46.0, 1.0], 'GSM1105441': [0.0, 44.0, 0.0], 'GSM1105442': [0.0, 59.0, 1.0], 'GSM1105443': [0.0, 32.0, 0.0], 'GSM1105444': [0.0, 59.0, 1.0], 'GSM1105445': [0.0, 32.0, 0.0], 'GSM1105446': [0.0, 45.0, 0.0], 'GSM1105447': [0.0, 25.0, 0.0], 'GSM1105448': [0.0, 45.0, 0.0], 'GSM1105449': [0.0, 25.0, 0.0], 'GSM1105450': [1.0, 25.0, 0.0], 'GSM1105451': [1.0, 26.0, 0.0], 'GSM1105452': [1.0, 25.0, 0.0], 'GSM1105453': [1.0, 26.0, 0.0], 'GSM1105454': [1.0, 43.0, 1.0], 'GSM1105455': [0.0, 24.0, 0.0], 'GSM1105456': [1.0, 43.0, 1.0], 'GSM1105457': [0.0, 24.0, 0.0], 'GSM1105458': [0.0, 43.0, 1.0], 'GSM1105459': [0.0, 43.0, 0.0], 'GSM1105460': [0.0, 43.0, 1.0], 'GSM1105461': [0.0, 43.0, 0.0], 'GSM1105462': [0.0, 38.0, 0.0], 'GSM1105463': [0.0, 47.0, 1.0], 'GSM1105464': [0.0, 38.0, 0.0], 'GSM1105465': [0.0, 47.0, 1.0], 'GSM1105466': [1.0, 37.0, 1.0], 'GSM1105467': [0.0, 57.0, 0.0], 'GSM1105468': [1.0, 37.0, 1.0], 'GSM1105469': [0.0, 57.0, 0.0], 'GSM1105470': [0.0, 23.0, 0.0], 'GSM1105471': [0.0, 57.0, 0.0], 'GSM1105472': [0.0, 23.0, 0.0], 'GSM1105473': [0.0, 57.0, 0.0], 'GSM1105474': [0.0, 30.0, 0.0], 'GSM1105475': [0.0, 37.0, 0.0], 'GSM1105476': [0.0, 30.0, 0.0], 'GSM1105477': [0.0, 37.0, 0.0], 'GSM1105478': [1.0, 51.0, 1.0], 'GSM1105479': [1.0, 35.0, 0.0], 'GSM1105480': [1.0, 51.0, 1.0], 'GSM1105481': [1.0, 35.0, 0.0], 'GSM1105482': [0.0, 64.0, 0.0], 'GSM1105483': [0.0, 45.0, 0.0], 'GSM1105484': [0.0, 64.0, 0.0], 'GSM1105485': [0.0, 45.0, 0.0], 'GSM1105486': [0.0, 53.0, 0.0], 'GSM1105487': [0.0, 57.0, 1.0], 'GSM1105488': [0.0, 53.0, 0.0], 'GSM1105489': [0.0, 57.0, 1.0], 'GSM1105490': [0.0, 25.0, 0.0], 'GSM1105491': [0.0, 61.0, 0.0], 'GSM1105492': [0.0, 25.0, 0.0], 'GSM1105493': [0.0, 61.0, 0.0], 'GSM1105494': [0.0, 44.0, 1.0], 'GSM1105495': [0.0, 39.0, 1.0], 'GSM1105496': [0.0, 44.0, 1.0], 'GSM1105497': [0.0, 39.0, 1.0], 'GSM1105498': [0.0, 26.0, 0.0], 'GSM1105499': [0.0, 45.0, 0.0], 'GSM1105500': [0.0, 26.0, 0.0], 'GSM1105501': [0.0, 45.0, 0.0], 'GSM1105502': [1.0, 53.0, 0.0], 'GSM1105503': [0.0, 51.0, 0.0], 'GSM1105504': [1.0, 53.0, 0.0], 'GSM1105505': [0.0, 51.0, 0.0], 'GSM1105506': [0.0, 36.0, 1.0], 'GSM1105507': [0.0, 45.0, 0.0], 'GSM1105508': [0.0, 36.0, 1.0], 'GSM1105509': [0.0, 45.0, 0.0], 'GSM1105510': [1.0, 38.0, 0.0], 'GSM1105511': [0.0, 18.0, 0.0], 'GSM1105512': [1.0, 38.0, 0.0], 'GSM1105513': [0.0, 18.0, 0.0], 'GSM1105514': [0.0, 20.0, 0.0], 'GSM1105515': [1.0, 27.0, 1.0], 'GSM1105516': [0.0, 20.0, 0.0], 'GSM1105517': [1.0, 27.0, 1.0], 'GSM1105518': [0.0, 49.0, 0.0], 'GSM1105519': [0.0, 43.0, 0.0], 'GSM1105520': [0.0, 49.0, 0.0], 'GSM1105521': [0.0, 43.0, 0.0], 'GSM1105522': [0.0, 29.0, 1.0], 'GSM1105523': [1.0, 20.0, 0.0], 'GSM1105524': [0.0, 29.0, 1.0], 'GSM1105525': [1.0, 20.0, 0.0], 'GSM1105526': [0.0, 32.0, 0.0], 'GSM1105527': [0.0, 40.0, 1.0], 'GSM1105528': [0.0, 32.0, 0.0], 'GSM1105529': [0.0, 40.0, 1.0], 'GSM1105530': [1.0, 59.0, 0.0], 'GSM1105531': [0.0, 41.0, 0.0], 'GSM1105532': [1.0, 59.0, 0.0], 'GSM1105533': [0.0, 41.0, 0.0], 'GSM1105534': [0.0, 20.0, 0.0], 'GSM1105535': [0.0, 31.0, 1.0], 'GSM1105536': [0.0, 20.0, 0.0], 'GSM1105537': [0.0, 31.0, 1.0], 'GSM1105538': [0.0, 29.0, 1.0], 'GSM1105539': [1.0, 49.0, 0.0], 'GSM1105540': [0.0, 29.0, 1.0], 'GSM1105541': [1.0, 49.0, 0.0], 'GSM1105542': [0.0, 52.0, 0.0], 'GSM1105543': [0.0, 22.0, 1.0], 'GSM1105544': [0.0, 52.0, 0.0], 'GSM1105545': [0.0, 22.0, 1.0], 'GSM1105546': [0.0, 52.0, 0.0], 'GSM1105547': [0.0, 39.0, 0.0], 'GSM1105548': [0.0, 52.0, 0.0], 'GSM1105549': [0.0, 39.0, 0.0], 'GSM1105550': [1.0, 27.0, 0.0], 'GSM1105551': [0.0, 57.0, 1.0], 'GSM1105552': [1.0, 27.0, 0.0], 'GSM1105553': [0.0, 57.0, 1.0], 'GSM1105554': [0.0, 27.0, 0.0], 'GSM1105555': [0.0, 36.0, 0.0], 'GSM1105556': [0.0, 27.0, 0.0], 'GSM1105557': [0.0, 36.0, 0.0]}\n",
      "Linked data shape: (120, 20262)\n",
      "Linked data preview (first 5 rows, 5 columns):\n",
      "            Bipolar_disorder   Age  Gender      A1BG  A1BG-AS1\n",
      "GSM1105438               0.0  46.0     1.0  14.03088   6.76428\n",
      "GSM1105439               0.0  44.0     0.0  13.93148   6.87647\n",
      "GSM1105440               0.0  46.0     1.0  13.81406   6.81161\n",
      "GSM1105441               0.0  44.0     0.0  13.83391   6.87946\n",
      "GSM1105442               0.0  59.0     1.0  13.78051   6.86722\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (120, 20262)\n",
      "For the feature 'Bipolar_disorder', the least common label is '1.0' with 26 occurrences. This represents 21.67% of the dataset.\n",
      "The distribution of the feature 'Bipolar_disorder' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 28.5\n",
      "  50% (Median): 39.5\n",
      "  75%: 49.0\n",
      "Min: 18.0\n",
      "Max: 64.0\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "For the feature 'Gender', the least common label is '1.0' with 36 occurrences. This represents 30.00% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Bipolar_disorder/GSE45484.csv\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",
    "# Save the normalized gene data to file\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 expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "# Read the saved clinical data file\n",
    "clinical_df = pd.read_csv(out_clinical_data_file)\n",
    "print(f\"Loaded clinical data shape: {clinical_df.shape}\")\n",
    "\n",
    "# Load proper clinical data with the correct conversion functions from Step 2\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,  # Using the previously defined function from Step 2\n",
    "    age_row=age_row,\n",
    "    convert_age=convert_age,      # Using the previously defined function from Step 2\n",
    "    gender_row=gender_row,\n",
    "    convert_gender=convert_gender # Using the previously defined function from Step 2\n",
    ")\n",
    "\n",
    "print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\n",
    "\n",
    "# 3. Handle missing values\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check for bias in features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Validate and save cohort information\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=True,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from blood samples of bipolar disorder patients, analyzing response to lithium treatment.\"\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if usable\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 is not usable for analysis. No linked data file saved.\")"
   ]
  }
 ],
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