{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61b25fec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:26:08.890358Z",
     "iopub.status.busy": "2025-03-25T06:26:08.890254Z",
     "iopub.status.idle": "2025-03-25T06:26:09.048716Z",
     "shell.execute_reply": "2025-03-25T06:26:09.048286Z"
    }
   },
   "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 = \"Alzheimers_Disease\"\n",
    "cohort = \"GSE132903\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Alzheimers_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Alzheimers_Disease/GSE132903\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Alzheimers_Disease/GSE132903.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Alzheimers_Disease/gene_data/GSE132903.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Alzheimers_Disease/clinical_data/GSE132903.csv\"\n",
    "json_path = \"../../output/preprocess/Alzheimers_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735a22d8",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d76ea28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:26:09.049973Z",
     "iopub.status.busy": "2025-03-25T06:26:09.049829Z",
     "iopub.status.idle": "2025-03-25T06:26:09.485141Z",
     "shell.execute_reply": "2025-03-25T06:26:09.484788Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptome changes in the Alzheimer's middle temporal gyrus: importance of RNA metabolism and mitochondria-associated membrane (MAM) genes\"\n",
      "!Series_summary\t\"We used Illumina Human HT-12 v4 arrays to compare RNA expression of middle temporal gyrus (MTG; BA21) in Alzheimer’s Disease (AD = 97) and non-demented controls (ND = 98).  A total of 938 transcripts were highly differentially expressed (adj p < 0.01; log2 Fold Change (FC) ≥ |0.500|, with 411 overexpressed and 527 underexpressed in AD.  Our results correlated with expression profiling in neurons from AD and ND obtained by Laser Capture Microscopy in MTG  from an independent dataset (log2 FC correlation: r = 0.504; p = 2.2e-16).  Additionally selected effects were validated by qPCR. ANOVA analysis yielded no difference between genders in response to AD, but some gender specific genes were detected (e.g: IL8 and AGRN in males, and HSPH1 and GRM1 in females).  Several transcripts were associated with Braak Staging (e.g AEBP1 and DNALI1), ante-mortem MMSE (e.g. AEBP1 and GFAP) and Tangle density (eg. RNU1G2, and DNALI1). At the pathway level we detected enrichment of Synaptic Vesicle Processes and GABAergic transmission genes. Finally, applying the Weighted Correlation Network Analysis (WGCNA) we identified 4 expression modules enriched for neuronal and synaptic genes, mitochondria-associated membrane (MAM), chemical stimulus and olfactory receptor and non-coding RNA metabolism genes.    Our results represent an extensive description of MTG mRNA profiling in a large sample of AD and ND.  These data provide a list of genes associated with AD, and correlated to neurofibrillary tangles density. In addition, these data emphasize the importance of mitochondrial membranes and transcripts related to olfactory receptors in AD.\"\n",
      "!Series_overall_design\t\"We compared RNA expression of middle temporal gyrus (MTG; BA21) between Alzheimer’s Disease (AD = 97) and non-demented controls (ND = 98) using Illumina Human HT-12 v4 arrays\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: middle temporal gyrus'], 1: ['Sex: female', 'Sex: male'], 2: ['expired_age (years): 90+', 'expired_age (years): 82', 'expired_age (years): 88', 'expired_age (years): 92', 'expired_age (years): 91', 'expired_age (years): 87', 'expired_age (years): 86', 'expired_age (years): 78', 'expired_age (years): 79', 'expired_age (years): 77', 'expired_age (years): 85', 'expired_age (years): 95', 'expired_age (years): 102', 'expired_age (years): 89', 'expired_age (years): 70', 'expired_age (years): 73', 'expired_age (years): 94', 'expired_age (years): 96', 'expired_age (years): 84', 'expired_age (years): 83', 'expired_age (years): 98', 'expired_age (years): 100', 'expired_age (years): 75', 'expired_age (years): 80', 'expired_age (years): 74', 'expired_age (years): 76', 'expired_age (years): 71', 'expired_age (years): 97', 'expired_age (years): 81', 'expired_age (years): 72'], 3: ['diagnosis: ND', 'diagnosis: AD']}\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": "e75cac64",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6117b31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:26:09.486316Z",
     "iopub.status.busy": "2025-03-25T06:26:09.486203Z",
     "iopub.status.idle": "2025-03-25T06:26:09.491074Z",
     "shell.execute_reply": "2025-03-25T06:26:09.490748Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trait row identified: 3\n",
      "Age row identified: 2\n",
      "Gender row identified: 1\n",
      "The actual clinical data extraction requires the full clinical dataset.\n",
      "We've identified the relevant rows and created the conversion functions.\n",
      "Metadata saved to ../../output/preprocess/Alzheimers_Disease/cohort_info.json, indicating trait data is available: True\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine gene expression data availability\n",
    "# From the background info, we see Illumina Human HT-12 v4 arrays were used\n",
    "# for RNA expression, which indicates gene expression data.\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (Alzheimer's Disease), we can use the 'diagnosis' field\n",
    "trait_row = 3  # 'diagnosis: ND', 'diagnosis: AD'\n",
    "\n",
    "# For age, we have 'expired_age (years)' field\n",
    "age_row = 2  # Contains ages of participants\n",
    "\n",
    "# For gender, we have 'Sex' field\n",
    "gender_row = 1  # 'Sex: female', 'Sex: male'\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert diagnosis to binary trait value.\"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    diagnosis = value.split(':', 1)[1].strip()\n",
    "    if diagnosis == 'AD':\n",
    "        return 1  # Alzheimer's Disease\n",
    "    elif diagnosis == 'ND':\n",
    "        return 0  # Non-demented control\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age string to continuous numeric value.\"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    age_str = value.split(':', 1)[1].strip()\n",
    "    try:\n",
    "        if age_str.endswith('+'):\n",
    "            # For 90+, use 90 as the base age\n",
    "            return float(age_str.replace('+', ''))\n",
    "        else:\n",
    "            return float(age_str)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender string to binary.\"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    gender = value.split(':', 1)[1].strip().lower()\n",
    "    if gender == 'female':\n",
    "        return 0\n",
    "    elif gender == 'male':\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\n",
    "# We have only the sample characteristics dictionary showing unique values,\n",
    "# but we need the actual clinical data to perform the extraction.\n",
    "# We'll print the information we've gathered for debugging purposes\n",
    "if trait_row is not None:\n",
    "    print(f\"Trait row identified: {trait_row}\")\n",
    "    print(f\"Age row identified: {age_row}\")\n",
    "    print(f\"Gender row identified: {gender_row}\")\n",
    "    print(\"The actual clinical data extraction requires the full clinical dataset.\")\n",
    "    print(\"We've identified the relevant rows and created the conversion functions.\")\n",
    "    \n",
    "    # Since we can't perform the actual extraction without the clinical data,\n",
    "    # we'll just note that we've saved the metadata indicating the dataset\n",
    "    # has the necessary trait information\n",
    "    print(f\"Metadata saved to {json_path}, indicating trait data is available: {is_trait_available}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105081e9",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e2b60b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:26:09.492223Z",
     "iopub.status.busy": "2025-03-25T06:26:09.491949Z",
     "iopub.status.idle": "2025-03-25T06:26:10.306417Z",
     "shell.execute_reply": "2025-03-25T06:26:10.305970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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_1651249', 'ILMN_1651254', 'ILMN_1651259',\n",
      "       'ILMN_1651260', 'ILMN_1651262', 'ILMN_1651268', 'ILMN_1651278'],\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": "8d212a12",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "767abff7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:26:10.307753Z",
     "iopub.status.busy": "2025-03-25T06:26:10.307641Z",
     "iopub.status.idle": "2025-03-25T06:26:10.309642Z",
     "shell.execute_reply": "2025-03-25T06:26:10.309340Z"
    }
   },
   "outputs": [],
   "source": [
    "# Analyzing the gene identifiers\n",
    "# The identifiers follow the \"ILMN_\" prefix pattern, which indicates they are Illumina probe IDs\n",
    "# These are not human gene symbols but Illumina BeadArray probe identifiers that need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73d7a1a",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "101fd4c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:26:10.310760Z",
     "iopub.status.busy": "2025-03-25T06:26:10.310656Z",
     "iopub.status.idle": "2025-03-25T06:26:25.249620Z",
     "shell.execute_reply": "2025-03-25T06:26:25.248954Z"
    }
   },
   "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": "5954ce72",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47b5fa78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:26:25.251668Z",
     "iopub.status.busy": "2025-03-25T06:26:25.251512Z",
     "iopub.status.idle": "2025-03-25T06:26:25.816918Z",
     "shell.execute_reply": "2025-03-25T06:26:25.816282Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping (first 5 rows):\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",
      "\n",
      "Gene expression data after mapping (first 5 genes):\n",
      "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2M'], dtype='object', name='Gene')\n",
      "Total number of genes after mapping: 19788\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine which columns to use for gene identifier mapping\n",
    "# From the gene annotation preview, 'ID' column contains the probe identifiers matching the gene expression data index\n",
    "# The 'Symbol' column contains the gene symbols we want to map to\n",
    "probe_col = 'ID'\n",
    "gene_col = 'Symbol'\n",
    "\n",
    "# 2. Get the gene mapping dataframe by extracting the identifier and symbol columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
    "\n",
    "# Print first few rows of the mapping\n",
    "print(\"Gene mapping (first 5 rows):\")\n",
    "print(gene_mapping.head())\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print the first few genes \n",
    "print(\"\\nGene expression data after mapping (first 5 genes):\")\n",
    "print(gene_data.index[:5])\n",
    "print(f\"Total number of genes after mapping: {len(gene_data)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c65742",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa6d05f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:26:25.818815Z",
     "iopub.status.busy": "2025-03-25T06:26:25.818689Z",
     "iopub.status.idle": "2025-03-25T06:26:45.510809Z",
     "shell.execute_reply": "2025-03-25T06:26:45.510103Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape after normalization: (18799, 195)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Alzheimers_Disease/gene_data/GSE132903.csv\n",
      "Loading the original clinical data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting clinical features...\n",
      "Clinical data preview:\n",
      "{'GSM3895951': [0.0, 90.0, 0.0], 'GSM3895952': [0.0, 82.0, 1.0], 'GSM3895953': [0.0, 88.0, 0.0], 'GSM3895954': [0.0, 92.0, 0.0], 'GSM3895955': [0.0, 91.0, 1.0], 'GSM3895956': [0.0, 87.0, 0.0], 'GSM3895957': [0.0, 86.0, 1.0], 'GSM3895958': [0.0, 78.0, 1.0], 'GSM3895959': [0.0, 87.0, 1.0], 'GSM3895960': [0.0, 79.0, 1.0], 'GSM3895961': [0.0, 77.0, 0.0], 'GSM3895962': [0.0, 77.0, 1.0], 'GSM3895963': [0.0, 88.0, 0.0], 'GSM3895964': [0.0, 85.0, 1.0], 'GSM3895965': [0.0, 95.0, 0.0], 'GSM3895966': [0.0, 102.0, 0.0], 'GSM3895967': [0.0, 89.0, 1.0], 'GSM3895968': [0.0, 70.0, 1.0], 'GSM3895969': [0.0, 82.0, 0.0], 'GSM3895970': [0.0, 73.0, 0.0], 'GSM3895971': [0.0, 90.0, 1.0], 'GSM3895972': [0.0, 94.0, 1.0], 'GSM3895973': [0.0, 96.0, 0.0], 'GSM3895974': [0.0, 85.0, 0.0], 'GSM3895975': [0.0, 84.0, 1.0], 'GSM3895976': [0.0, 83.0, 1.0], 'GSM3895977': [0.0, 90.0, 0.0], 'GSM3895978': [0.0, 87.0, 0.0], 'GSM3895979': [0.0, 85.0, 1.0], 'GSM3895980': [0.0, 83.0, 0.0], 'GSM3895981': [0.0, 84.0, 1.0], 'GSM3895982': [0.0, 88.0, 1.0], 'GSM3895983': [0.0, 98.0, 0.0], 'GSM3895984': [0.0, 85.0, 1.0], 'GSM3895985': [0.0, 86.0, 0.0], 'GSM3895986': [0.0, 87.0, 0.0], 'GSM3895987': [0.0, 89.0, 1.0], 'GSM3895988': [0.0, 92.0, 1.0], 'GSM3895989': [0.0, 78.0, 0.0], 'GSM3895990': [0.0, 77.0, 0.0], 'GSM3895991': [0.0, 91.0, 1.0], 'GSM3895992': [0.0, 100.0, 1.0], 'GSM3895993': [0.0, 82.0, 1.0], 'GSM3895994': [0.0, 87.0, 0.0], 'GSM3895995': [0.0, 73.0, 1.0], 'GSM3895996': [0.0, 75.0, 1.0], 'GSM3895997': [0.0, 82.0, 1.0], 'GSM3895998': [0.0, 90.0, 0.0], 'GSM3895999': [0.0, 96.0, 0.0], 'GSM3896000': [0.0, 84.0, 0.0], 'GSM3896001': [0.0, 80.0, 1.0], 'GSM3896002': [0.0, 86.0, 1.0], 'GSM3896003': [0.0, 91.0, 0.0], 'GSM3896004': [0.0, 91.0, 0.0], 'GSM3896005': [0.0, 94.0, 0.0], 'GSM3896006': [0.0, 87.0, 1.0], 'GSM3896007': [0.0, 75.0, 0.0], 'GSM3896008': [0.0, 74.0, 1.0], 'GSM3896009': [0.0, 76.0, 1.0], 'GSM3896010': [0.0, 71.0, 1.0], 'GSM3896011': [0.0, 87.0, 1.0], 'GSM3896012': [0.0, 90.0, 1.0], 'GSM3896013': [0.0, 80.0, 1.0], 'GSM3896014': [0.0, 84.0, 1.0], 'GSM3896015': [0.0, 80.0, 1.0], 'GSM3896016': [0.0, 89.0, 1.0], 'GSM3896017': [0.0, 86.0, 0.0], 'GSM3896018': [0.0, 80.0, 0.0], 'GSM3896019': [0.0, 92.0, 1.0], 'GSM3896020': [0.0, 83.0, 0.0], 'GSM3896021': [0.0, 86.0, 0.0], 'GSM3896022': [0.0, 91.0, 0.0], 'GSM3896023': [0.0, 95.0, 0.0], 'GSM3896024': [0.0, 95.0, 0.0], 'GSM3896025': [0.0, 82.0, 0.0], 'GSM3896026': [0.0, 85.0, 0.0], 'GSM3896027': [0.0, 87.0, 0.0], 'GSM3896028': [0.0, 95.0, 1.0], 'GSM3896029': [0.0, 85.0, 0.0], 'GSM3896030': [0.0, 91.0, 0.0], 'GSM3896031': [0.0, 89.0, 0.0], 'GSM3896032': [1.0, 80.0, 1.0], 'GSM3896033': [1.0, 87.0, 0.0], 'GSM3896034': [1.0, 92.0, 0.0], 'GSM3896035': [1.0, 77.0, 0.0], 'GSM3896036': [1.0, 84.0, 0.0], 'GSM3896037': [1.0, 91.0, 0.0], 'GSM3896038': [1.0, 87.0, 0.0], 'GSM3896039': [1.0, 97.0, 0.0], 'GSM3896040': [1.0, 87.0, 0.0], 'GSM3896041': [1.0, 78.0, 1.0], 'GSM3896042': [1.0, 76.0, 1.0], 'GSM3896043': [1.0, 81.0, 1.0], 'GSM3896044': [1.0, 80.0, 1.0], 'GSM3896045': [1.0, 86.0, 0.0], 'GSM3896046': [1.0, 81.0, 0.0], 'GSM3896047': [1.0, 79.0, 1.0], 'GSM3896048': [1.0, 91.0, 0.0], 'GSM3896049': [1.0, 91.0, 0.0], 'GSM3896050': [1.0, 89.0, 0.0], 'GSM3896051': [1.0, 82.0, 0.0], 'GSM3896052': [1.0, 92.0, 0.0], 'GSM3896053': [1.0, 86.0, 1.0], 'GSM3896054': [1.0, 82.0, 0.0], 'GSM3896055': [1.0, 86.0, 0.0], 'GSM3896056': [1.0, 80.0, 1.0], 'GSM3896057': [1.0, 87.0, 0.0], 'GSM3896058': [1.0, 92.0, 1.0], 'GSM3896059': [1.0, 90.0, 0.0], 'GSM3896060': [1.0, 88.0, 0.0], 'GSM3896061': [1.0, 90.0, 1.0], 'GSM3896062': [1.0, 90.0, 1.0], 'GSM3896063': [1.0, 72.0, 1.0], 'GSM3896064': [1.0, 87.0, 1.0], 'GSM3896065': [1.0, 75.0, 1.0], 'GSM3896066': [1.0, 86.0, 0.0], 'GSM3896067': [1.0, 95.0, 0.0], 'GSM3896068': [1.0, 95.0, 1.0], 'GSM3896069': [1.0, 88.0, 0.0], 'GSM3896070': [1.0, 87.0, 1.0], 'GSM3896071': [1.0, 81.0, 0.0], 'GSM3896072': [1.0, 83.0, 1.0], 'GSM3896073': [1.0, 85.0, 0.0], 'GSM3896074': [1.0, 95.0, 0.0], 'GSM3896075': [1.0, 81.0, 1.0], 'GSM3896076': [1.0, 83.0, 1.0], 'GSM3896077': [1.0, 85.0, 1.0], 'GSM3896078': [1.0, 85.0, 0.0], 'GSM3896079': [1.0, 94.0, 1.0], 'GSM3896080': [1.0, 97.0, 1.0], 'GSM3896081': [1.0, 82.0, 0.0], 'GSM3896082': [1.0, 91.0, 1.0], 'GSM3896083': [1.0, 92.0, 1.0], 'GSM3896084': [1.0, 70.0, 1.0], 'GSM3896085': [1.0, 84.0, 1.0], 'GSM3896086': [1.0, 86.0, 1.0], 'GSM3896087': [1.0, 95.0, 0.0], 'GSM3896088': [1.0, 88.0, 1.0], 'GSM3896089': [1.0, 79.0, 1.0], 'GSM3896090': [1.0, 87.0, 1.0], 'GSM3896091': [1.0, 73.0, 0.0], 'GSM3896092': [1.0, 90.0, 0.0], 'GSM3896093': [1.0, 83.0, 1.0], 'GSM3896094': [1.0, 85.0, 0.0], 'GSM3896095': [1.0, 74.0, 1.0], 'GSM3896096': [1.0, 71.0, 1.0], 'GSM3896097': [1.0, 78.0, 0.0], 'GSM3896098': [1.0, 82.0, 1.0], 'GSM3896099': [1.0, 85.0, 1.0], 'GSM3896100': [1.0, 96.0, 0.0], 'GSM3896101': [1.0, 70.0, 0.0], 'GSM3896102': [1.0, 78.0, 0.0], 'GSM3896103': [1.0, 77.0, 0.0], 'GSM3896104': [1.0, 87.0, 0.0], 'GSM3896105': [1.0, 84.0, 1.0], 'GSM3896106': [1.0, 98.0, 1.0], 'GSM3896107': [1.0, 75.0, 1.0], 'GSM3896108': [1.0, 76.0, 1.0], 'GSM3896109': [1.0, 94.0, 0.0], 'GSM3896110': [1.0, 84.0, 1.0], 'GSM3896111': [1.0, 75.0, 0.0], 'GSM3896112': [0.0, 75.0, 1.0], 'GSM3896113': [0.0, 92.0, 0.0], 'GSM3896114': [0.0, 81.0, 0.0], 'GSM3896115': [0.0, 77.0, 0.0], 'GSM3896116': [1.0, 88.0, 1.0], 'GSM3896117': [1.0, 87.0, 0.0], 'GSM3896118': [1.0, 77.0, 0.0], 'GSM3896119': [1.0, 93.0, 1.0], 'GSM3896120': [1.0, 97.0, 0.0], 'GSM3896121': [1.0, 89.0, 1.0], 'GSM3896122': [1.0, 88.0, 1.0], 'GSM3896123': [1.0, 73.0, 1.0], 'GSM3896124': [1.0, 91.0, 0.0], 'GSM3896125': [1.0, 91.0, 0.0], 'GSM3896126': [0.0, 78.0, 0.0], 'GSM3896127': [1.0, 89.0, 1.0], 'GSM3896128': [1.0, 78.0, 0.0], 'GSM3896129': [1.0, 90.0, 0.0], 'GSM3896130': [1.0, 85.0, 1.0], 'GSM3896131': [1.0, 85.0, 0.0], 'GSM3896132': [1.0, 82.0, 1.0], 'GSM3896133': [1.0, 82.0, 1.0], 'GSM3896134': [0.0, 72.0, 1.0], 'GSM3896135': [0.0, 82.0, 0.0], 'GSM3896136': [0.0, 81.0, 0.0], 'GSM3896137': [0.0, 81.0, 1.0], 'GSM3896138': [0.0, 79.0, 1.0], 'GSM3896139': [0.0, 91.0, 1.0], 'GSM3896140': [0.0, 81.0, 1.0], 'GSM3896141': [0.0, 70.0, 0.0], 'GSM3896142': [0.0, 76.0, 1.0], 'GSM3896143': [0.0, 90.0, 1.0], 'GSM3896144': [0.0, 83.0, 1.0], 'GSM3896145': [0.0, 83.0, 1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Alzheimers_Disease/clinical_data/GSE132903.csv\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (195, 18802)\n",
      "Handling missing values...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (195, 18802)\n",
      "Checking for bias in trait distribution...\n",
      "For the feature 'Alzheimers_Disease', the least common label is '1.0' with 97 occurrences. This represents 49.74% of the dataset.\n",
      "The distribution of the feature 'Alzheimers_Disease' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 80.5\n",
      "  50% (Median): 85.0\n",
      "  75%: 90.0\n",
      "Min: 70.0\n",
      "Max: 102.0\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "For the feature 'Gender', the least common label is '0.0' with 96 occurrences. This represents 49.23% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is fine.\n",
      "\n",
      "Dataset usability: True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Alzheimers_Disease/GSE132903.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols...\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to a CSV file\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",
    "# 2. Link the clinical and genetic data\n",
    "print(\"Loading the original clinical data...\")\n",
    "# Get the matrix file again to ensure we have the proper data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "print(\"Extracting clinical features...\")\n",
    "# Use the clinical_data obtained directly from the matrix file\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",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Save the clinical data to a CSV file\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",
    "# Link clinical and genetic data using the normalized gene data\n",
    "print(\"Linking clinical and genetic data...\")\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "print(\"Handling missing values...\")\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check if trait is biased\n",
    "print(\"Checking for bias in trait distribution...\")\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Final validation\n",
    "note = \"Dataset contains gene expression data from bronchial brushings from control individuals and patients with asthma after rhinovirus infection in vivo, as described in the study 'Rhinovirus-induced epithelial RIG-I inflammasome suppresses antiviral immunity and promotes inflammation in asthma and COVID-19'.\"\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "print(f\"Dataset usability: {is_usable}\")\n",
    "\n",
    "# 6. 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 is not usable for trait-gene association studies due to bias or other issues.\")"
   ]
  }
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