{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97565195",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:17.491567Z",
     "iopub.status.busy": "2025-03-25T03:51:17.491459Z",
     "iopub.status.idle": "2025-03-25T03:51:17.650548Z",
     "shell.execute_reply": "2025-03-25T03:51:17.650232Z"
    }
   },
   "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 = \"Rheumatoid_Arthritis\"\n",
    "cohort = \"GSE176440\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Rheumatoid_Arthritis\"\n",
    "in_cohort_dir = \"../../input/GEO/Rheumatoid_Arthritis/GSE176440\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/GSE176440.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/gene_data/GSE176440.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE176440.csv\"\n",
    "json_path = \"../../output/preprocess/Rheumatoid_Arthritis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "475a35f1",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b633e93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:17.651936Z",
     "iopub.status.busy": "2025-03-25T03:51:17.651796Z",
     "iopub.status.idle": "2025-03-25T03:51:17.854215Z",
     "shell.execute_reply": "2025-03-25T03:51:17.853873Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression profiles of CD4+ T cells before and after methotrexate treatment in rheumatoid arthritis patients [Microarray]\"\n",
      "!Series_summary\t\"To understand the molecular mechanisms by which methotraxate improves the disease activity in rheumatoid arthritis, CD4+ T cells were obtained before and 3month after methotrexate treatment.\"\n",
      "!Series_overall_design\t\"28 treatment naïve rheumatoid arthritis patients participated in the study. Blood samples were obtained before and 3 months after methotrexate treatment. CD4+ T cells were magnetically purified and subjected the DNA microarray analyses.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['individual: A29', 'individual: A30', 'individual: A34', 'individual: C14', 'individual: C16', 'individual: C19', 'individual: C43', 'individual: C49', 'individual: C71', 'individual: C80', 'individual: C85', 'individual: C87', 'individual: C91', 'individual: C92', 'individual: C93', 'individual: C95', 'individual: C96', 'individual: C100', 'individual: C102', 'individual: C103', 'individual: C107', 'individual: C108', 'individual: C109', 'individual: C111', 'individual: C115', 'individual: C116', 'individual: C117', 'individual: K20'], 1: ['disease state: rheumatoid arthritis patient'], 2: ['treatment: before methotrexate', 'treatment: 3 months after  methotrexate'], 3: ['cell type: CD4+ T 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": "3a650c57",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e67176c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:17.855444Z",
     "iopub.status.busy": "2025-03-25T03:51:17.855338Z",
     "iopub.status.idle": "2025-03-25T03:51:17.862265Z",
     "shell.execute_reply": "2025-03-25T03:51:17.862013Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical data:\n",
      "{0: [nan], 1: [1.0], 2: [nan], 3: [nan]}\n",
      "Clinical data saved to ../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE176440.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Callable, Dict, Any, List\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains microarray data of CD4+ T cells\n",
    "# which implies gene expression data, not just miRNA or methylation\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Identify keys in the sample characteristics dictionary\n",
    "\n",
    "# Trait (Rheumatoid Arthritis)\n",
    "# From the sample characteristics, all samples are from RA patients (key 1)\n",
    "trait_row = 1  # \"disease state: rheumatoid arthritis patient\"\n",
    "\n",
    "# Treatment status (before/after methotrexate) at key 2 - this could be useful clinical information\n",
    "# but it's not age or gender\n",
    "\n",
    "# Age - Not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender - Not available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"Convert trait value to binary (0 for control, 1 for RA).\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # All samples are RA patients based on the data\n",
    "    if \"rheumatoid arthritis\" in value.lower():\n",
    "        return 1\n",
    "    return None  # Default case for unknown values\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age value to continuous numeric.\"\"\"\n",
    "    # Not used since age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male).\"\"\"\n",
    "    # Not used since gender data is not available\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort information\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "# Since trait_row is not None, we need to extract clinical features\n",
    "if trait_row is not None:\n",
    "    # Load the clinical data (assuming it was saved from a previous step)\n",
    "    clinical_data = pd.DataFrame(\n",
    "        {0: ['individual: A29', 'individual: A30', 'individual: A34', 'individual: C14', 'individual: C16', 'individual: C19', 'individual: C43', 'individual: C49', 'individual: C71', 'individual: C80', 'individual: C85', 'individual: C87', 'individual: C91', 'individual: C92', 'individual: C93', 'individual: C95', 'individual: C96', 'individual: C100', 'individual: C102', 'individual: C103', 'individual: C107', 'individual: C108', 'individual: C109', 'individual: C111', 'individual: C115', 'individual: C116', 'individual: C117', 'individual: K20'], \n",
    "         1: ['disease state: rheumatoid arthritis patient'] * 28,\n",
    "         2: ['treatment: before methotrexate', 'treatment: 3 months after  methotrexate'] * 14,\n",
    "         3: ['cell type: CD4+ T cells'] * 28}\n",
    "    )\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 selected clinical data\n",
    "    print(\"Preview of selected clinical data:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    \n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save the selected clinical data to CSV\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": "35fea9d6",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f227655b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:17.863298Z",
     "iopub.status.busy": "2025-03-25T03:51:17.863201Z",
     "iopub.status.idle": "2025-03-25T03:51:18.129568Z",
     "shell.execute_reply": "2025-03-25T03:51:18.129204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056',\n",
      "       'A_23_P100074', 'A_23_P100092', 'A_23_P100103', 'A_23_P100111',\n",
      "       'A_23_P100127', 'A_23_P100133', 'A_23_P100141', 'A_23_P100156',\n",
      "       'A_23_P100177', 'A_23_P100189', 'A_23_P100196', 'A_23_P100203',\n",
      "       'A_23_P100220', 'A_23_P100240', 'A_23_P10025', 'A_23_P100263'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54573b79",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6cecc13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:18.130841Z",
     "iopub.status.busy": "2025-03-25T03:51:18.130718Z",
     "iopub.status.idle": "2025-03-25T03:51:18.132626Z",
     "shell.execute_reply": "2025-03-25T03:51:18.132346Z"
    }
   },
   "outputs": [],
   "source": [
    "# The identifiers in the gene expression data (A_23_P100001, A_23_P100011, etc.) are Agilent microarray \n",
    "# probe identifiers, not human gene symbols.\n",
    "# These are probe IDs from an Agilent microarray platform and need to be mapped to human gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ccddc43",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "825dcac6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:18.133704Z",
     "iopub.status.busy": "2025-03-25T03:51:18.133605Z",
     "iopub.status.idle": "2025-03-25T03:51:22.153557Z",
     "shell.execute_reply": "2025-03-25T03:51:22.153192Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'SPOT_ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'CONTROL_TYPE': ['FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GB_ACC': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GENE': [400451.0, 10239.0, 9899.0, 348093.0, 57099.0], 'GENE_SYMBOL': ['FAM174B', 'AP3S2', 'SV2B', 'RBPMS2', 'AVEN'], 'GENE_NAME': ['family with sequence similarity 174, member B', 'adaptor-related protein complex 3, sigma 2 subunit', 'synaptic vesicle glycoprotein 2B', 'RNA binding protein with multiple splicing 2', 'apoptosis, caspase activation inhibitor'], 'UNIGENE_ID': ['Hs.27373', 'Hs.632161', 'Hs.21754', 'Hs.436518', 'Hs.555966'], 'ENSEMBL_ID': ['ENST00000557398', nan, 'ENST00000557410', 'ENST00000300069', 'ENST00000306730'], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': ['ref|NM_207446|ens|ENST00000557398|ens|ENST00000553393|ens|ENST00000327355', 'ref|NM_005829|ref|NM_001199058|ref|NR_023361|ref|NR_037582', 'ref|NM_014848|ref|NM_001167580|ens|ENST00000557410|ens|ENST00000330276', 'ref|NM_194272|ens|ENST00000300069|gb|AK127873|gb|AK124123', 'ref|NM_020371|ens|ENST00000306730|gb|AF283508|gb|BC010488'], 'CHROMOSOMAL_LOCATION': ['chr15:93160848-93160789', 'chr15:90378743-90378684', 'chr15:91838329-91838388', 'chr15:65032375-65032316', 'chr15:34158739-34158680'], 'CYTOBAND': ['hs|15q26.1', 'hs|15q26.1', 'hs|15q26.1', 'hs|15q22.31', 'hs|15q14'], 'DESCRIPTION': ['Homo sapiens family with sequence similarity 174, member B (FAM174B), mRNA [NM_207446]', 'Homo sapiens adaptor-related protein complex 3, sigma 2 subunit (AP3S2), transcript variant 1, mRNA [NM_005829]', 'Homo sapiens synaptic vesicle glycoprotein 2B (SV2B), transcript variant 1, mRNA [NM_014848]', 'Homo sapiens RNA binding protein with multiple splicing 2 (RBPMS2), mRNA [NM_194272]', 'Homo sapiens apoptosis, caspase activation inhibitor (AVEN), mRNA [NM_020371]'], 'GO_ID': ['GO:0016020(membrane)|GO:0016021(integral to membrane)', 'GO:0005794(Golgi apparatus)|GO:0006886(intracellular protein transport)|GO:0008565(protein transporter activity)|GO:0016020(membrane)|GO:0016192(vesicle-mediated transport)|GO:0030117(membrane coat)|GO:0030659(cytoplasmic vesicle membrane)|GO:0031410(cytoplasmic vesicle)', 'GO:0001669(acrosomal vesicle)|GO:0006836(neurotransmitter transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0022857(transmembrane transporter activity)|GO:0030054(cell junction)|GO:0030672(synaptic vesicle membrane)|GO:0031410(cytoplasmic vesicle)|GO:0045202(synapse)', 'GO:0000166(nucleotide binding)|GO:0003676(nucleic acid binding)', 'GO:0005515(protein binding)|GO:0005622(intracellular)|GO:0005624(membrane fraction)|GO:0006915(apoptosis)|GO:0006916(anti-apoptosis)|GO:0012505(endomembrane system)|GO:0016020(membrane)'], 'SEQUENCE': ['ATCTCATGGAAAAGCTGGATTCCTCTGCCTTACGCAGAAACACCCGGGCTCCATCTGCCA', 'TCAAGTATTGGCCTGACATAGAGTCCTTAAGACAAGCAAAGACAAGCAAGGCAAGCACGT', 'ATGTCGGCTGTGGAGGGTTAAAGGGATGAGGCTTTCCTTTGTTTAGCAAATCTGTTCACA', 'CCCTGTCAGATAAGTTTAATGTTTAGTTTGAGGCATGAAGAAGAAAAGGGTTTCCATTCT', 'GACCAGCCAGTTTACAAGCATGTCTCAAGCTAGTGTGTTCCATTATGCTCACAGCAGTAA']}\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. 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": "10880b81",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89dc6c60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:22.154852Z",
     "iopub.status.busy": "2025-03-25T03:51:22.154735Z",
     "iopub.status.idle": "2025-03-25T03:51:22.344821Z",
     "shell.execute_reply": "2025-03-25T03:51:22.344377Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of mapped gene expression data:\n",
      "(18488, 56)\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT',\n",
      "       'AAAS', 'AACS'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the columns in the gene annotation dataframe\n",
    "# From the preview, 'ID' in the gene_annotation corresponds to the probe identifiers in gene_data\n",
    "# 'GENE_SYMBOL' contains the human gene symbols we want to map to\n",
    "prob_col = 'ID'\n",
    "gene_col = 'GENE_SYMBOL'\n",
    "\n",
    "# 2. Extract the mapping between probe IDs and gene symbols\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "# This function handles the many-to-many relationship as specified\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Preview the result to verify the transformation\n",
    "print(\"Preview of mapped gene expression data:\")\n",
    "print(gene_data.shape)\n",
    "print(gene_data.index[:10])  # Print first 10 gene symbols\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb6eb5c",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd1bd097",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:22.346326Z",
     "iopub.status.busy": "2025-03-25T03:51:22.346207Z",
     "iopub.status.idle": "2025-03-25T03:51:23.003979Z",
     "shell.execute_reply": "2025-03-25T03:51:23.003614Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Rheumatoid_Arthritis/gene_data/GSE176440.csv\n",
      "Clinical data columns: ['0', '1', '2', '3']\n",
      "Linked data shape: (61, 18248)\n",
      "Error processing data: ['Rheumatoid_Arthritis']\n",
      "Abnormality detected in the cohort: GSE176440. Preprocessing failed.\n",
      "Dataset not usable for analysis. No linked data saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\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. Load the clinical data that was saved in Step 2\n",
    "clinical_data_df = pd.read_csv(out_clinical_data_file)\n",
    "\n",
    "# Check the structure of the clinical data\n",
    "print(\"Clinical data columns:\", clinical_data_df.columns.tolist())\n",
    "\n",
    "# Since we don't have a proper trait column in the clinical data,\n",
    "# we need to add it before linking\n",
    "if trait not in clinical_data_df.columns:\n",
    "    # Create a proper clinical data structure with the trait column\n",
    "    # From previous steps, we see all values are 1.0 for RA patients\n",
    "    clinical_data_df[trait] = 1.0\n",
    "\n",
    "# Link the clinical and genetic data on sample IDs\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data_df, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "try:\n",
    "    linked_data = handle_missing_values(linked_data, trait)\n",
    "    print(f\"After handling missing values, linked data shape: {linked_data.shape}\")\n",
    "    \n",
    "    # 4. Determine whether the trait and demographic features are severely biased\n",
    "    trait_biased, linked_data_filtered = judge_and_remove_biased_features(linked_data, trait)\n",
    "    \n",
    "    # 5. Conduct final quality validation and save relevant 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=trait_biased, \n",
    "        df=linked_data_filtered,\n",
    "        note=\"Dataset contains gene expression data from CD4+ T cells of rheumatoid arthritis patients before and after methotrexate treatment.\"\n",
    "    )\n",
    "    \n",
    "    # 6. If the linked data is usable, save it as a CSV file\n",
    "    if is_usable:\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        linked_data_filtered.to_csv(out_data_file)\n",
    "        print(f\"Linked data saved to {out_data_file}\")\n",
    "    else:\n",
    "        print(\"Dataset not usable for analysis. No linked data saved.\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error processing data: {e}\")\n",
    "    # If there's an error, mark the dataset as 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,  # Marking as unavailable due to processing error\n",
    "        is_biased=True, \n",
    "        df=pd.DataFrame(),\n",
    "        note=f\"Error during data processing: {e}. Dataset contains only RA patients with constant trait value.\"\n",
    "    )\n",
    "    print(\"Dataset not usable for analysis. No linked data saved.\")"
   ]
  }
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