{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e1978157",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:27:53.378968Z",
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     "shell.execute_reply": "2025-03-25T06:27:53.550668Z"
    }
   },
   "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 = \"Amyotrophic_Lateral_Sclerosis\"\n",
    "cohort = \"GSE212131\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Amyotrophic_Lateral_Sclerosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Amyotrophic_Lateral_Sclerosis/GSE212131\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/GSE212131.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212131.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE212131.csv\"\n",
    "json_path = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2541d9b",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "42088e0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:27:53.552448Z",
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     "iopub.status.idle": "2025-03-25T06:27:53.625987Z",
     "shell.execute_reply": "2025-03-25T06:27:53.625641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Establishing mRNA and miRNA interactions driving disease heterogeneity in ALS patient survival (microarray)\"\n",
      "!Series_summary\t\"Transcriptomic analysis of lymphoblastoid cell lines from ALS patients with varying disease duration\"\n",
      "!Series_summary\t\"Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, associated with the degeneration of both upper and lower motor neurons of the motor cortex, brainstem and spinal cord. Death in most patients results from respiratory failure within 3-4 years from symptom onset. However, due to disease heterogeneity some individuals survive only months from symptom onset while others live for several years. Identifying specific biomarkers that aid in establishing disease prognosis, particularly in terms of predicting disease progression, will help our understanding of ALS pathophysiology and could be used to monitor a patient’s response to drugs and therapeutic agents.  Transcriptomic profiling technologies are continually evolving, enabling us to identify key gene changes in biological processes associated with disease. MicroRNAs (miRNAs) are small non-coding RNAs typically associated with regulating gene expression, by degrading mRNA or reducing levels of gene expression. Being able to associate gene expression changes with corresponding miRNA changes would help to distinguish a more complex biomarker signature enabling us to address key challenges associated with complex diseases such as ALS.\"\n",
      "!Series_overall_design\t\"The present study aimed to investigate the transcriptomic profile (mRNA and miRNA) of lymphoblastoid cell lines (LCLs) from ALS patients to identify key signatures that are distinguishable in those patients who suffered a short disease duration (< 12 months) compared to those that had a longer disease duration (>6 years). Affymetrix Human Exon 1.0ST GeneChip microarrays were used to assess mRNA/gene changes, while small RNA sequencing of miRNA extracted from peripheral LCL’s from ALS patients with short and long disease was performed using the Illumina TruSeq Small RNA library preparation kit and Illumina HiScanSQ.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['gender: Female', 'gender: Male']}\n"
     ]
    }
   ],
   "source": [
    "from tools.preprocess import *\n",
    "# 1. Identify the paths to the SOFT file and the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Read the matrix file to obtain background information and sample characteristics data\n",
    "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
    "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "\n",
    "# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "\n",
    "# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
    "print(\"Background Information:\")\n",
    "print(background_info)\n",
    "print(\"Sample Characteristics Dictionary:\")\n",
    "print(sample_characteristics_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec1c5a4",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed0f9c6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:27:53.627310Z",
     "iopub.status.busy": "2025-03-25T06:27:53.627197Z",
     "iopub.status.idle": "2025-03-25T06:27:53.633087Z",
     "shell.execute_reply": "2025-03-25T06:27:53.632752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression data from microarray analysis\n",
    "is_gene_available = True  # Affymetrix Human Exon 1.0ST GeneChip microarrays were used\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait: The dataset is comparing ALS patients with short duration (<12 months) vs long duration (>6 years)\n",
    "# This can be inferred from the background information, but not directly available in the sample characteristics\n",
    "trait_row = None  # Not directly available in sample characteristics\n",
    "\n",
    "# For age: Not provided in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# For gender: Available in the sample characteristics at index 0\n",
    "gender_row = 0\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "# For trait (not used since trait_row is None, but defined for completeness)\n",
    "def convert_trait(value):\n",
    "    return None  # Not directly available\n",
    "\n",
    "# For age (not used since age_row is None, but defined for completeness)\n",
    "def convert_age(value):\n",
    "    return None\n",
    "\n",
    "# For gender\n",
    "def convert_gender(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        gender = value.split(\":\")[1].strip().lower()\n",
    "        if \"female\" in gender:\n",
    "            return 0\n",
    "        elif \"male\" in gender:\n",
    "            return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Trait data availability is determined by whether trait_row is None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial validation\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 - Skip this step since trait_row is None\n",
    "# No clinical feature extraction needed as trait_row is None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c92c5cb",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc3b51e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:27:53.634184Z",
     "iopub.status.busy": "2025-03-25T06:27:53.634079Z",
     "iopub.status.idle": "2025-03-25T06:27:53.719059Z",
     "shell.execute_reply": "2025-03-25T06:27:53.718718Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['2315554', '2315633', '2315674', '2315739', '2315894', '2315918',\n",
      "       '2315951', '2316218', '2316245', '2316379', '2316558', '2316605',\n",
      "       '2316746', '2316905', '2316953', '2317246', '2317317', '2317434',\n",
      "       '2317472', '2317512'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 22011 genes × 42 samples\n"
     ]
    }
   ],
   "source": [
    "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract the gene expression data from the matrix file\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "print(\"\\nFirst 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# 4. Print the dimensions of the gene expression data\n",
    "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a01cd03",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "034483df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:27:53.720412Z",
     "iopub.status.busy": "2025-03-25T06:27:53.720300Z",
     "iopub.status.idle": "2025-03-25T06:27:53.722310Z",
     "shell.execute_reply": "2025-03-25T06:27:53.722017Z"
    }
   },
   "outputs": [],
   "source": [
    "# Review the gene identifiers in the data\n",
    "# Looking at the identifiers '2315554', '2315633', etc., these appear to be probe IDs \n",
    "# from a microarray platform rather than standard human gene symbols.\n",
    "# Standard human gene symbols would be alphanumeric like GAPDH, TP53, etc.\n",
    "# These purely numeric identifiers likely need to be mapped to gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4af1676",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69267cc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:27:53.723506Z",
     "iopub.status.busy": "2025-03-25T06:27:53.723400Z",
     "iopub.status.idle": "2025-03-25T06:27:56.269893Z",
     "shell.execute_reply": "2025-03-25T06:27:56.269506Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['2315100', '2315106', '2315109', '2315111', '2315113'], 'GB_LIST': ['NR_024005,NR_034090,NR_024004,AK093685', 'DQ786314', nan, nan, 'DQ786265'], 'SPOT_ID': ['chr1:11884-14409', 'chr1:14760-15198', 'chr1:19408-19712', 'chr1:25142-25532', 'chr1:27563-27813'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': ['11884', '14760', '19408', '25142', '27563'], 'RANGE_STOP': ['14409', '15198', '19712', '25532', '27813'], 'total_probes': ['20', '8', '4', '4', '4'], 'gene_assignment': ['NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 // 15q26.3 // 100288486 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// AK093685 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771', '---', '---', '---', '---'], 'mrna_assignment': ['NR_024005 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 (DDX11L2), transcript variant 2, non-coding RNA. // chr1 // 100 // 80 // 16 // 16 // 0 /// NR_034090 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 (DDX11L9), non-coding RNA. // chr1 // 100 // 80 // 16 // 16 // 0 /// NR_024004 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 (DDX11L2), transcript variant 1, non-coding RNA. // chr1 // 100 // 75 // 15 // 15 // 0 /// AK093685 // GenBank // Homo sapiens cDNA FLJ36366 fis, clone THYMU2007824. // chr1 // 94 // 80 // 15 // 16 // 0 /// ENST00000513886 // ENSEMBL // cdna:known chromosome:GRCh37:16:61555:64090:1 gene:ENSG00000233614 // chr1 // 100 // 80 // 16 // 16 // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 // chr1 // 100 // 80 // 16 // 16 // 0 /// ENST00000518655 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000253101 // chr1 // 100 // 80 // 16 // 16 // 0', 'DQ786314 // GenBank // Homo sapiens clone HLS_IMAGE_811138 mRNA sequence. // chr1 // 100 // 38 // 3 // 3 // 0', '---', '---', 'DQ786265 // GenBank // Homo sapiens clone HLS_IMAGE_298685 mRNA sequence. // chr1 // 100 // 100 // 4 // 4 // 0'], 'category': ['main', 'main', '---', '---', 'main']}\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": "0d881b65",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "889fb86e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:27:56.271253Z",
     "iopub.status.busy": "2025-03-25T06:27:56.271130Z",
     "iopub.status.idle": "2025-03-25T06:27:56.700734Z",
     "shell.execute_reply": "2025-03-25T06:27:56.700332Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping preview:\n",
      "{'ID': ['2315100', '2315106', '2315109', '2315111', '2315113'], 'Gene': ['NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 // 15q26.3 // 100288486 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// AK093685 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771', '---', '---', '---', '---']}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene data dimensions after mapping: 48895 genes × 42 samples\n",
      "\n",
      "First 20 gene symbols after mapping:\n",
      "Index(['A-', 'A-2', 'A-52', 'A-E', 'A-I', 'A-II', 'A-IV', 'A-V', 'A0', 'A1',\n",
      "       'A1-', 'A10', 'A11', 'A12', 'A13', 'A14', 'A16', 'A1BG', 'A1BG-AS',\n",
      "       'A1CF'],\n",
      "      dtype='object', name='Gene')\n",
      "\n",
      "Number of genes after mapping: 48895\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the relevant columns in the gene annotation dataframe\n",
    "# From the preview, we can see:\n",
    "# - 'ID' column contains the numeric identifiers (like 2315100) which match the gene expression indices\n",
    "# - 'gene_assignment' column contains the gene symbols and additional information\n",
    "\n",
    "# 2. Extract gene mapping from gene annotation\n",
    "# We'll use the 'ID' and 'gene_assignment' columns for mapping\n",
    "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n",
    "\n",
    "# Print a preview of the gene mapping\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "# This handles many-to-many relationships between probes and genes\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print the dimensions of the gene expression data after mapping\n",
    "print(f\"\\nGene data dimensions after mapping: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Print the first 20 gene symbols after mapping\n",
    "print(\"\\nFirst 20 gene symbols after mapping:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# Check if any genes were filtered out during mapping (genes with no symbols)\n",
    "print(f\"\\nNumber of genes after mapping: {len(gene_data)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52b26b7b",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3fe03a50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:27:56.702129Z",
     "iopub.status.busy": "2025-03-25T06:27:56.702007Z",
     "iopub.status.idle": "2025-03-25T06:27:57.310662Z",
     "shell.execute_reply": "2025-03-25T06:27:57.310273Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape after normalization: (18418, 42)\n",
      "First 5 gene symbols after normalization: Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1'], dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212131.csv\n",
      "Sample IDs in clinical data:\n",
      "Index(['!Sample_geo_accession', 'GSM6509811', 'GSM6509812', 'GSM6509813',\n",
      "       'GSM6509814'],\n",
      "      dtype='object') ...\n",
      "Sample IDs in gene expression data:\n",
      "Index(['GSM6509811', 'GSM6509812', 'GSM6509813', 'GSM6509814', 'GSM6509815'], dtype='object') ...\n",
      "Trait data is unavailable in this dataset.\n",
      "Abnormality detected in the cohort: GSE212131. Preprocessing failed.\n",
      "Dataset deemed not usable for associational studies.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the index of gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(f\"First 5 gene symbols after normalization: {normalized_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",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Check if clinical data was properly loaded\n",
    "# First, reload the clinical_data to make sure we're using the original 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 the sample IDs to understand the data structure\n",
    "print(\"Sample IDs in clinical data:\")\n",
    "print(clinical_data.columns[:5], \"...\")  # Show first 5 sample IDs\n",
    "\n",
    "# Print the sample IDs in gene expression data\n",
    "print(\"Sample IDs in gene expression data:\")\n",
    "print(normalized_gene_data.columns[:5], \"...\")  # Show first 5 sample IDs\n",
    "\n",
    "# Explicitly set trait availability based on findings from previous steps\n",
    "is_trait_available = False  # From step 2, we determined trait_row was None\n",
    "linked_data = None\n",
    "\n",
    "if is_trait_available:\n",
    "    # This block won't execute because is_trait_available=False\n",
    "    # But keeping it here for completeness\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=0,  # Placeholder, won't be used\n",
    "        convert_trait=lambda x: None,  # Placeholder, won't be used\n",
    "        age_row=None,\n",
    "        convert_age=None,\n",
    "        gender_row=0,\n",
    "        convert_gender=lambda x: None  # Placeholder, won't be used\n",
    "    )\n",
    "    \n",
    "    print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "    print(f\"Clinical data preview: {preview_df(selected_clinical_df, n=3)}\")\n",
    "    \n",
    "    # Save the 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",
    "    # Link clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "    print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\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. 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",
    "else:\n",
    "    print(\"Trait data is unavailable in this dataset.\")\n",
    "    is_biased = True  # Dataset can't be used without trait data\n",
    "    linked_data = pd.DataFrame()  # Empty DataFrame\n",
    "\n",
    "# 5. 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=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data if not linked_data.empty else pd.DataFrame(index=normalized_gene_data.columns),\n",
    "    note=\"Dataset contains gene expression data from ALS patients, but lacks trait information (disease status) required for associational studies.\"\n",
    ")\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 deemed not usable for associational studies.\")"
   ]
  }
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