{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee37d9e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:33.225742Z",
     "iopub.status.busy": "2025-03-25T06:08:33.225637Z",
     "iopub.status.idle": "2025-03-25T06:08:33.388863Z",
     "shell.execute_reply": "2025-03-25T06:08:33.388542Z"
    }
   },
   "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 = \"Parkinsons_Disease\"\n",
    "cohort = \"GSE49126\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Parkinsons_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Parkinsons_Disease/GSE49126\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Parkinsons_Disease/GSE49126.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Parkinsons_Disease/gene_data/GSE49126.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Parkinsons_Disease/clinical_data/GSE49126.csv\"\n",
    "json_path = \"../../output/preprocess/Parkinsons_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6afe81",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ada710e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:33.390300Z",
     "iopub.status.busy": "2025-03-25T06:08:33.390153Z",
     "iopub.status.idle": "2025-03-25T06:08:33.553101Z",
     "shell.execute_reply": "2025-03-25T06:08:33.552749Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptomic profiling of peripheral blood mononuclear cells from patients with Parkinson's disease and control subjects\"\n",
      "!Series_summary\t\"To get insight into systemic molecular events associated with Parkinson's disease (PD), an age-related neurodegenerative disorder, we compared gene expression patterns of peripheral blood mononuclear cells (PBMC) derived from elderly healhy controls and from PD patients.\"\n",
      "!Series_overall_design\t\"Transcriptomic profiling of patients with Parkinson's disease and control subjects. RNA were extracted from peripheral mononuclear blood cells and were hybridized on 4x44k Agilent expression microarrays.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['disease state: control', \"disease state: Parkinson's disease\"], 1: ['cell type: peripheral blood mononuclear cells']}\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": "d2d2ed8f",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64ad1650",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:33.554477Z",
     "iopub.status.busy": "2025-03-25T06:08:33.554364Z",
     "iopub.status.idle": "2025-03-25T06:08:33.561848Z",
     "shell.execute_reply": "2025-03-25T06:08:33.561531Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{0: [0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Parkinsons_Disease/clinical_data/GSE49126.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Yes, the background information suggests this dataset contains gene expression data from Agilent microarrays\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variables Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For Parkinson's Disease (trait)\n",
    "# From sample characteristics, the first row (index 0) contains disease state (control vs. Parkinson's disease)\n",
    "trait_row = 0 \n",
    "\n",
    "# No age information available in the characteristics dictionary\n",
    "age_row = None  \n",
    "\n",
    "# No gender information available in the characteristics dictionary\n",
    "gender_row = None  \n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary: 1 for Parkinson's disease, 0 for control.\"\"\"\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",
    "    \n",
    "    # Convert to binary\n",
    "    if \"parkinson\" in value.lower():\n",
    "        return 1\n",
    "    elif \"control\" in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous numeric.\"\"\"\n",
    "    # Not used since age data is not available\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",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary: 0 for female, 1 for male.\"\"\"\n",
    "    # Not used since gender data is not available\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",
    "    \n",
    "    value = value.lower()\n",
    "    if 'female' in value or 'f' in value:\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial filtering of dataset usability\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",
    "if trait_row is not None:\n",
    "    # Create a DataFrame from the sample characteristics dictionary\n",
    "    # The sample characteristics dictionary only contains the information about available variables\n",
    "    # We need to construct clinical_data DataFrame in a format compatible with geo_select_clinical_features\n",
    "    \n",
    "    # For this dataset, we need to create a DataFrame with the disease state information\n",
    "    sample_characteristics = {0: ['disease state: control', \"disease state: Parkinson's disease\"]}\n",
    "    \n",
    "    # Create a DataFrame from the sample characteristics\n",
    "    clinical_data = pd.DataFrame(sample_characteristics)\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the resulting DataFrame\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save 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": "c6d07bbb",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8872f063",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:33.562881Z",
     "iopub.status.busy": "2025-03-25T06:08:33.562771Z",
     "iopub.status.idle": "2025-03-25T06:08:33.817046Z",
     "shell.execute_reply": "2025-03-25T06:08:33.816660Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene/probe identifiers:\n",
      "Index(['12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23',\n",
      "       '24', '25', '26', '27', '28', '29', '30', '31'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths again to access the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_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) for future observation\n",
    "print(\"First 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bce6ff2",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "464e4eeb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:33.818206Z",
     "iopub.status.busy": "2025-03-25T06:08:33.818093Z",
     "iopub.status.idle": "2025-03-25T06:08:33.819972Z",
     "shell.execute_reply": "2025-03-25T06:08:33.819698Z"
    }
   },
   "outputs": [],
   "source": [
    "# Analyzing gene identifiers from the previous step output\n",
    "# These identifiers appear to be numeric values rather than gene symbols\n",
    "# Human gene symbols typically follow nomenclature patterns like BRCA1, TP53, etc.\n",
    "# The numeric identifiers (12, 13, 14, etc.) likely need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e904956f",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "438af9fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:33.820968Z",
     "iopub.status.busy": "2025-03-25T06:08:33.820866Z",
     "iopub.status.idle": "2025-03-25T06:08:38.187101Z",
     "shell.execute_reply": "2025-03-25T06:08:38.186603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['1', '2', '3', '4', '5'], 'COL': ['266', '266', '266', '266', '266'], 'ROW': [170.0, 168.0, 166.0, 164.0, 162.0], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'DarkCorner', 'DarkCorner'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'DarkCorner', 'DarkCorner'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'pos', 'pos'], 'REFSEQ': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan], 'GENE': [nan, nan, nan, nan, nan], 'GENE_SYMBOL': [nan, nan, nan, nan, nan], 'GENE_NAME': [nan, nan, nan, nan, nan], 'UNIGENE_ID': [nan, nan, nan, nan, nan], 'ENSEMBL_ID': [nan, nan, nan, nan, nan], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan, nan, nan], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, nan, nan], 'CYTOBAND': [nan, nan, nan, nan, nan], 'DESCRIPTION': [nan, nan, nan, nan, nan], 'GO_ID': [nan, nan, nan, nan, nan], 'SEQUENCE': [nan, nan, nan, nan, nan], 'SPOT_ID.1': [nan, nan, nan, nan, nan], 'ORDER': [1.0, 2.0, 3.0, 4.0, 5.0]}\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": "3afffa13",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6edfa8de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:38.188644Z",
     "iopub.status.busy": "2025-03-25T06:08:38.188518Z",
     "iopub.status.idle": "2025-03-25T06:08:38.516733Z",
     "shell.execute_reply": "2025-03-25T06:08:38.516343Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows of gene annotation with non-null gene symbols:\n",
      "    ID   GENE_SYMBOL\n",
      "11  12      APOBEC3B\n",
      "13  14        ATP11B\n",
      "14  15  LOC100132006\n",
      "15  16        DNAJA1\n",
      "17  18         EHMT2\n",
      "Preview of mapping data:\n",
      "    ID          Gene\n",
      "11  12      APOBEC3B\n",
      "13  14        ATP11B\n",
      "14  15  LOC100132006\n",
      "15  16        DNAJA1\n",
      "17  18         EHMT2\n",
      "Number of mappings: 32696\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of gene expression data after mapping:\n",
      "Number of genes after mapping: 18379\n",
      "        GSM1194062   GSM1194063   GSM1194064   GSM1194065   GSM1194066  \\\n",
      "Gene                                                                     \n",
      "A1BG   1346.496358  1630.091552  2081.789469  1796.719663  2008.200069   \n",
      "A1CF      4.538759     5.038548     3.803502     8.955622     3.817977   \n",
      "A2BP1     9.909301     9.704206     7.470562     8.859836     7.333760   \n",
      "A2LD1   669.066900   548.872900   403.105000   463.760900   319.830400   \n",
      "A2M      42.052910    38.886210    23.065940    43.597190    26.161890   \n",
      "\n",
      "        GSM1194067   GSM1194068   GSM1194069   GSM1194070   GSM1194071  ...  \\\n",
      "Gene                                                                    ...   \n",
      "A1BG   1522.559963  1009.084895  1341.158950  2027.324354  2003.861906  ...   \n",
      "A1CF      5.237851     3.216614     3.457880     4.237291     5.366957  ...   \n",
      "A2BP1     9.685222    16.132344     6.926038     8.215888     9.822340  ...   \n",
      "A2LD1   443.100900   438.633900   248.013200   469.619600   454.442200  ...   \n",
      "A2M      30.920590    28.413580    15.422110    57.794280    43.735720  ...   \n",
      "\n",
      "        GSM1194102   GSM1194103   GSM1194104   GSM1194105  GSM1194106  \\\n",
      "Gene                                                                    \n",
      "A1BG   1749.987044  1385.016852  2304.430180  2092.178360  1663.14074   \n",
      "A1CF      3.578176     6.867698    10.412570     8.163887    12.59161   \n",
      "A2BP1     8.951356    12.970842    20.853829    15.223017    24.24613   \n",
      "A2LD1   425.331700   902.101400   329.947800   904.598200   740.07340   \n",
      "A2M      23.917930    43.075670    55.747480    67.217300    60.45679   \n",
      "\n",
      "        GSM1194107  GSM1194108   GSM1194109   GSM1194110   GSM1194111  \n",
      "Gene                                                                   \n",
      "A1BG   1721.694577  1592.98306  1607.190855  2017.009250  1737.085181  \n",
      "A1CF      6.483706    15.54629     4.621959    10.410410     4.920152  \n",
      "A2BP1    12.878769    29.79084     9.201703    19.218434     9.708699  \n",
      "A2LD1   743.762600   511.14170   299.508100   622.202700   394.375000  \n",
      "A2M      49.509160    95.17065    26.399070    89.052390    32.440310  \n",
      "\n",
      "[5 rows x 50 columns]\n",
      "Preview of gene expression data after normalization:\n",
      "Number of genes after normalization: 17901\n",
      "          GSM1194062   GSM1194063   GSM1194064   GSM1194065   GSM1194066  \\\n",
      "Gene                                                                       \n",
      "A1BG     1346.496358  1630.091552  2081.789469  1796.719663  2008.200069   \n",
      "A1CF        4.538759     5.038548     3.803502     8.955622     3.817977   \n",
      "A2M        42.052910    38.886210    23.065940    43.597190    26.161890   \n",
      "A2ML1     125.865200   203.504000   139.937500   181.789300   134.115600   \n",
      "A3GALT2     4.721398     5.191688     4.019145     4.739811     4.043736   \n",
      "\n",
      "          GSM1194067   GSM1194068   GSM1194069   GSM1194070   GSM1194071  ...  \\\n",
      "Gene                                                                      ...   \n",
      "A1BG     1522.559963  1009.084895  1341.158950  2027.324354  2003.861906  ...   \n",
      "A1CF        5.237851     3.216614     3.457880     4.237291     5.366957  ...   \n",
      "A2M        30.920590    28.413580    15.422110    57.794280    43.735720  ...   \n",
      "A2ML1     166.589100   163.001800   140.692600   333.713600   226.403700  ...   \n",
      "A3GALT2     5.098981     3.256807     3.651523     4.297328     5.081932  ...   \n",
      "\n",
      "          GSM1194102   GSM1194103  GSM1194104   GSM1194105  GSM1194106  \\\n",
      "Gene                                                                     \n",
      "A1BG     1749.987044  1385.016852  2304.43018  2092.178360  1663.14074   \n",
      "A1CF        3.578176     6.867698    10.41257     8.163887    12.59161   \n",
      "A2M        23.917930    43.075670    55.74748    67.217300    60.45679   \n",
      "A2ML1     154.066500   432.067100   360.51350   453.911000   628.54790   \n",
      "A3GALT2     3.901351    14.812840    12.03404     7.739103    28.92620   \n",
      "\n",
      "          GSM1194107  GSM1194108   GSM1194109   GSM1194110   GSM1194111  \n",
      "Gene                                                                     \n",
      "A1BG     1721.694577  1592.98306  1607.190855  2017.009250  1737.085181  \n",
      "A1CF        6.483706    15.54629     4.621959    10.410410     4.920152  \n",
      "A2M        49.509160    95.17065    26.399070    89.052390    32.440310  \n",
      "A2ML1     598.126800   420.07030   138.735700   464.638000   202.998200  \n",
      "A3GALT2    13.079970    27.06521     6.184034     9.633035     4.929014  \n",
      "\n",
      "[5 rows x 50 columns]\n"
     ]
    }
   ],
   "source": [
    "# 1. Analyze the gene expression data indices and gene annotation columns to determine mapping\n",
    "# From the gene annotation preview, we can see that 'ID' could be the gene identifier and 'GENE_SYMBOL' looks like the column for gene symbols\n",
    "\n",
    "# Get a more comprehensive view of the gene annotation data to confirm our column choices\n",
    "print(\"First few rows of gene annotation with non-null gene symbols:\")\n",
    "non_null_gene_symbols = gene_annotation.dropna(subset=['GENE_SYMBOL']).head(5)\n",
    "print(non_null_gene_symbols[['ID', 'GENE_SYMBOL']])\n",
    "\n",
    "# 2. Extract the mapping between gene identifiers and gene symbols\n",
    "# The 'ID' column in gene_annotation seems to match the index in gene_data\n",
    "mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')\n",
    "\n",
    "print(\"Preview of mapping data:\")\n",
    "print(mapping_data.head())\n",
    "print(f\"Number of mappings: {len(mapping_data)}\")\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
    "\n",
    "print(\"Preview of gene expression data after mapping:\")\n",
    "print(f\"Number of genes after mapping: {len(gene_data)}\")\n",
    "print(gene_data.head())\n",
    "\n",
    "# Normalize gene symbols if needed\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "\n",
    "print(\"Preview of gene expression data after normalization:\")\n",
    "print(f\"Number of genes after normalization: {len(gene_data)}\")\n",
    "print(gene_data.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d65a1d",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5ed7125f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:08:38.518202Z",
     "iopub.status.busy": "2025-03-25T06:08:38.518076Z",
     "iopub.status.idle": "2025-03-25T06:08:47.231341Z",
     "shell.execute_reply": "2025-03-25T06:08:47.230938Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data shape: (17901, 50)\n",
      "First 5 gene symbols:\n",
      "Index(['A1BG', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2'], dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Parkinsons_Disease/gene_data/GSE49126.csv\n",
      "Original clinical data columns (should contain sample IDs):\n",
      "Index(['!Sample_geo_accession', 'GSM1194062', 'GSM1194063', 'GSM1194064',\n",
      "       'GSM1194065'],\n",
      "      dtype='object')\n",
      "Genetic data columns (sample IDs):\n",
      "Index(['GSM1194062', 'GSM1194063', 'GSM1194064', 'GSM1194065', 'GSM1194066'], dtype='object')\n",
      "Selected clinical data shape: (1, 50)\n",
      "Selected clinical data preview (transposed):\n",
      "            Parkinsons_Disease\n",
      "GSM1194062                 0.0\n",
      "GSM1194063                 0.0\n",
      "GSM1194064                 0.0\n",
      "GSM1194065                 0.0\n",
      "GSM1194066                 0.0\n",
      "Clinical data saved to ../../output/preprocess/Parkinsons_Disease/clinical_data/GSE49126.csv\n",
      "Number of clinical samples: 50\n",
      "Number of genetic samples: 50\n",
      "Number of common samples: 50\n",
      "Linked data shape: (50, 17902)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (50, 17902)\n",
      "For the feature 'Parkinsons_Disease', the least common label is '0.0' with 20 occurrences. This represents 40.00% of the dataset.\n",
      "The distribution of the feature 'Parkinsons_Disease' in this dataset is fine.\n",
      "\n",
      "Data shape after removing biased features: (50, 17902)\n",
      "Is the trait biased: False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Parkinsons_Disease/GSE49126.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols from the already mapped gene expression data from Step 6\n",
    "# Gene data was already normalized in Step 6\n",
    "print(f\"Gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First 5 gene symbols:\")\n",
    "print(gene_data.index[:5])\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\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Re-extract clinical data correctly to ensure it matches with genetic data\n",
    "# Re-obtain the original data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_df = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Print sample identifiers from original clinical data to debug\n",
    "print(\"Original clinical data columns (should contain sample IDs):\")\n",
    "print(clinical_df.columns[:5])  # Show first 5 sample IDs\n",
    "\n",
    "# Print sample identifiers from genetic data to debug\n",
    "print(\"Genetic data columns (sample IDs):\")\n",
    "print(gene_data.columns[:5])  # Show first 5 sample IDs\n",
    "\n",
    "# Extract clinical features directly from the original clinical_df\n",
    "# The trait information is in row 0 (disease state)\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()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if \"parkinson\" in value.lower():\n",
    "        return 1\n",
    "    elif \"control\" in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Create clinical data with the trait information\n",
    "trait_row = 0  # The disease state row\n",
    "selected_clinical_df = geo_select_clinical_features(\n",
    "    clinical_df=clinical_df,\n",
    "    trait=trait,\n",
    "    trait_row=trait_row,\n",
    "    convert_trait=convert_trait,\n",
    "    age_row=None,\n",
    "    convert_age=None,\n",
    "    gender_row=None,\n",
    "    convert_gender=None\n",
    ")\n",
    "\n",
    "print(\"Selected clinical data shape:\", selected_clinical_df.shape)\n",
    "print(\"Selected clinical data preview (transposed):\")\n",
    "# Transpose to show samples as rows to make structure clearer\n",
    "print(selected_clinical_df.T.head())\n",
    "\n",
    "# Save the processed clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "# Both datasets should have samples as columns initially\n",
    "# Check for matching sample IDs\n",
    "clinical_samples = set(selected_clinical_df.columns)\n",
    "genetic_samples = set(gene_data.columns)\n",
    "common_samples = clinical_samples.intersection(genetic_samples)\n",
    "\n",
    "print(f\"Number of clinical samples: {len(clinical_samples)}\")\n",
    "print(f\"Number of genetic samples: {len(genetic_samples)}\")\n",
    "print(f\"Number of common samples: {len(common_samples)}\")\n",
    "\n",
    "if len(common_samples) > 0:\n",
    "    # Filter both datasets to include only common samples\n",
    "    clinical_data_filtered = selected_clinical_df[list(common_samples)]\n",
    "    gene_data_filtered = gene_data[list(common_samples)]\n",
    "    \n",
    "    # Now link them using geo_link_clinical_genetic_data function\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_data_filtered, gene_data_filtered)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    \n",
    "    # 4. 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",
    "    # 5. Determine if trait and demographic features are biased\n",
    "    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "    print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
    "    print(f\"Is the trait biased: {is_biased}\")\n",
    "    \n",
    "    # 6. Validate and save cohort info\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 PD patients and controls.\"\n",
    "    )\n",
    "    \n",
    "    # 7. Save 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 deemed not usable. Linked data was not saved.\")\n",
    "else:\n",
    "    print(\"No common samples found between clinical and genetic data. Cannot link the datasets.\")\n",
    "    # Record that the dataset is not usable\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=False,  # No common samples means trait data isn't available for gene samples\n",
    "        is_biased=True,  # Mark as biased since we can't use it\n",
    "        df=pd.DataFrame(),  # Empty dataframe\n",
    "        note=\"No common samples between clinical and genetic data.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable. Linked data was not saved.\")"
   ]
  }
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