{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af5796a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:33:53.718591Z",
     "iopub.status.busy": "2025-03-25T07:33:53.718425Z",
     "iopub.status.idle": "2025-03-25T07:33:53.884394Z",
     "shell.execute_reply": "2025-03-25T07:33:53.883939Z"
    }
   },
   "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 = \"Liver_cirrhosis\"\n",
    "cohort = \"GSE66843\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Liver_cirrhosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Liver_cirrhosis/GSE66843\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Liver_cirrhosis/GSE66843.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Liver_cirrhosis/gene_data/GSE66843.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Liver_cirrhosis/clinical_data/GSE66843.csv\"\n",
    "json_path = \"../../output/preprocess/Liver_cirrhosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633c70d8",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c414872b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:33:53.885686Z",
     "iopub.status.busy": "2025-03-25T07:33:53.885544Z",
     "iopub.status.idle": "2025-03-25T07:33:53.983248Z",
     "shell.execute_reply": "2025-03-25T07:33:53.982769Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"A cell-based model unravels drivers for hepatocarcinogenesis and targets for clinical chemoprevention\"\n",
      "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "!Series_overall_design\t\"Refer to individual Series\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['time post infection: Day 3 post infection', 'time post infection: Day 7 post infection', 'time post infection: Day 10 post infection'], 1: ['infection: Mock infection (control)', 'infection: HCV Jc1 infection'], 2: ['cell line: Huh7.5.1']}\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": "dedf27b1",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44c6a242",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:33:53.984939Z",
     "iopub.status.busy": "2025-03-25T07:33:53.984825Z",
     "iopub.status.idle": "2025-03-25T07:33:53.991306Z",
     "shell.execute_reply": "2025-03-25T07:33:53.990881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this is a SuperSeries studying cells with HCV infection\n",
    "# over several days. This appears to be a viral infection cell line model rather than \n",
    "# a clinical study of liver cirrhosis patients with gene expression data.\n",
    "is_gene_available = False\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# This dataset contains HCV infection data, but it's an acute infection model in cell lines,\n",
    "# which is not equivalent to liver cirrhosis (which develops over years).\n",
    "# Since this isn't appropriate for our cirrhosis study, we'll mark trait as unavailable.\n",
    "trait_row = None  # Not appropriate for cirrhosis study\n",
    "age_row = None  # No age data available (cell line model)\n",
    "gender_row = None  # No gender data available (cell line model)\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert infection status to binary values (0: Mock, 1: HCV)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if \"mock\" in value.lower() or \"control\" in value.lower():\n",
    "        return 0\n",
    "    elif \"hcv\" in value.lower() or \"jc1\" in value.lower():\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    # Not used, but defined for compatibility\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Not used, but defined for compatibility\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability based on trait_row being not None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save the metadata using the function from the library\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 None, we skip this substep\n",
    "# No clinical data will be extracted or saved\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0f09d8",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "102303c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:33:53.992776Z",
     "iopub.status.busy": "2025-03-25T07:33:53.992667Z",
     "iopub.status.idle": "2025-03-25T07:33:54.093814Z",
     "shell.execute_reply": "2025-03-25T07:33:54.093178Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Liver_cirrhosis/GSE66843/GSE66843-GPL10558_series_matrix.txt.gz\n",
      "Gene data shape: (46116, 17)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n",
      "       'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n",
      "       'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n",
      "       'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n",
      "       'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. Get the SOFT and matrix file paths again \n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "print(f\"Matrix file found: {matrix_file}\")\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(f\"Gene data shape: {gene_data.shape}\")\n",
    "    \n",
    "    # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a13c8b",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c8315a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:33:54.095553Z",
     "iopub.status.busy": "2025-03-25T07:33:54.095431Z",
     "iopub.status.idle": "2025-03-25T07:33:54.097698Z",
     "shell.execute_reply": "2025-03-25T07:33:54.097272Z"
    }
   },
   "outputs": [],
   "source": [
    "# The gene identifiers in the data are ILMN_* format, which are Illumina probe IDs \n",
    "# (from the GPL10558 Illumina HumanHT-12 V4.0 platform).\n",
    "# These are not standard human gene symbols and need to be mapped to gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0580f839",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdfe28c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:33:54.099413Z",
     "iopub.status.busy": "2025-03-25T07:33:54.099304Z",
     "iopub.status.idle": "2025-03-25T07:34:05.397109Z",
     "shell.execute_reply": "2025-03-25T07:34:05.396445Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene annotation preview:\n",
      "Columns in gene annotation: ['ID', 'Species', 'Source', 'Search_Key', 'Transcript', 'ILMN_Gene', 'Source_Reference_ID', 'RefSeq_ID', 'Unigene_ID', 'Entrez_Gene_ID', 'GI', 'Accession', 'Symbol', 'Protein_Product', 'Probe_Id', 'Array_Address_Id', 'Probe_Type', 'Probe_Start', 'SEQUENCE', 'Chromosome', 'Probe_Chr_Orientation', 'Probe_Coordinates', 'Cytoband', 'Definition', 'Ontology_Component', 'Ontology_Process', 'Ontology_Function', 'Synonyms', 'Obsolete_Probe_Id', 'GB_ACC']\n",
      "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0, 1450438.0, 1240647.0], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': [nan, nan, nan, nan, nan], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan]}\n",
      "\n",
      "Analyzing SPOT_ID.1 column for gene symbols:\n",
      "\n",
      "Gene data ID prefix: ILMN\n",
      "Column 'ID' contains values matching gene data ID pattern\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'Species' contains values matching gene data ID pattern\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'Source' contains values matching gene data ID pattern\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'Transcript' contains values matching gene data ID pattern\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'Probe_Id' contains values matching gene data ID pattern\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Checking for columns containing transcript or gene related terms:\n",
      "Column 'Transcript' may contain gene-related information\n",
      "Sample values: [nan, nan, nan]\n",
      "Column 'ILMN_Gene' may contain gene-related information\n",
      "Sample values: [nan, nan, nan]\n",
      "Column 'Unigene_ID' may contain gene-related information\n",
      "Sample values: [nan, nan, nan]\n",
      "Column 'Entrez_Gene_ID' may contain gene-related information\n",
      "Sample values: [nan, nan, nan]\n",
      "Column 'Symbol' may contain gene-related information\n",
      "Sample values: ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low']\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Check for gene information in the SPOT_ID.1 column which appears to contain gene names\n",
    "print(\"\\nAnalyzing SPOT_ID.1 column for gene symbols:\")\n",
    "if 'SPOT_ID.1' in gene_annotation.columns:\n",
    "    # Extract a few sample values\n",
    "    sample_values = gene_annotation['SPOT_ID.1'].head(3).tolist()\n",
    "    for i, value in enumerate(sample_values):\n",
    "        print(f\"Sample {i+1} excerpt: {value[:200]}...\")  # Print first 200 chars\n",
    "        # Test the extract_human_gene_symbols function on these values\n",
    "        symbols = extract_human_gene_symbols(value)\n",
    "        print(f\"  Extracted gene symbols: {symbols}\")\n",
    "\n",
    "# Try to find the probe IDs in the gene annotation\n",
    "gene_data_id_prefix = gene_data.index[0].split('_')[0]  # Get prefix of first gene ID\n",
    "print(f\"\\nGene data ID prefix: {gene_data_id_prefix}\")\n",
    "\n",
    "# Look for columns that might match the gene data IDs\n",
    "for col in gene_annotation.columns:\n",
    "    if gene_annotation[col].astype(str).str.contains(gene_data_id_prefix).any():\n",
    "        print(f\"Column '{col}' contains values matching gene data ID pattern\")\n",
    "\n",
    "# Check if there's any column that might contain transcript or gene IDs\n",
    "print(\"\\nChecking for columns containing transcript or gene related terms:\")\n",
    "for col in gene_annotation.columns:\n",
    "    if any(term in col.upper() for term in ['GENE', 'TRANSCRIPT', 'SYMBOL', 'NAME', 'DESCRIPTION']):\n",
    "        print(f\"Column '{col}' may contain gene-related information\")\n",
    "        # Show sample values\n",
    "        print(f\"Sample values: {gene_annotation[col].head(3).tolist()}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c1ba75",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89be2682",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:34:05.399051Z",
     "iopub.status.busy": "2025-03-25T07:34:05.398925Z",
     "iopub.status.idle": "2025-03-25T07:34:05.607755Z",
     "shell.execute_reply": "2025-03-25T07:34:05.607135Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping dataframe shape: (44837, 2)\n",
      "Sample of gene mapping:\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data shape after mapping: (21125, 17)\n",
      "First few gene symbols after mapping:\n",
      "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n",
      "       'A4GALT', 'A4GNT'],\n",
      "      dtype='object', name='Gene')\n",
      "Number of genes with data: 21125\n",
      "Number of samples: 17\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify columns for identifier and gene symbol mapping\n",
    "# Based on the output analysis, 'ID' column in gene_annotation contains ILMN identifiers\n",
    "# that match the gene expression data index, and 'Symbol' contains gene symbols\n",
    "id_column = 'ID'\n",
    "gene_symbol_column = 'Symbol'\n",
    "\n",
    "# 2. Get a gene mapping dataframe by extracting these two columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, id_column, gene_symbol_column)\n",
    "print(f\"Gene mapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"Sample of gene mapping:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First few gene symbols after mapping:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Verify we have actual genes (not just empty or null values)\n",
    "print(f\"Number of genes with data: {len(gene_data)}\")\n",
    "print(f\"Number of samples: {gene_data.shape[1]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ac91f0",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c6fc5bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:34:05.609774Z",
     "iopub.status.busy": "2025-03-25T07:34:05.609502Z",
     "iopub.status.idle": "2025-03-25T07:34:05.957576Z",
     "shell.execute_reply": "2025-03-25T07:34:05.957022Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape before normalization: (21125, 17)\n",
      "Gene data shape after normalization: (19956, 17)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene expression data saved to ../../output/preprocess/Liver_cirrhosis/gene_data/GSE66843.csv\n",
      "No clinical data available for this dataset, skipping clinical data processing.\n",
      "Abnormality detected in the cohort: GSE66843. Preprocessing failed.\n",
      "Dataset is not usable for liver cirrhosis analysis due to lack of clinical data. No linked data file saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "# Use normalize_gene_symbols_in_index to standardize gene symbols\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Check if trait_row is None (indicating no clinical data is available)\n",
    "if trait_row is None:\n",
    "    print(\"No clinical data available for this dataset, skipping clinical data processing.\")\n",
    "    \n",
    "    # Validate and save cohort information with trait_available=False\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,\n",
    "        is_biased=True,  # Set to True since we can't use this data without clinical features\n",
    "        df=pd.DataFrame(),  # Empty DataFrame since we have no linked data\n",
    "        note=\"Dataset contains gene expression data from cell lines with HCV infection, which is not appropriate for liver cirrhosis trait analysis.\"\n",
    "    )\n",
    "    \n",
    "    print(\"Dataset is not usable for liver cirrhosis analysis due to lack of clinical data. No linked data file saved.\")\n",
    "else:\n",
    "    # If clinical data is available, proceed with the linking and processing\n",
    "    # 2. Link the clinical and genetic data\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(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(selected_clinical_df.head())\n",
    "\n",
    "    # Link the 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 processing: {linked_data.shape}\")\n",
    "    print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "    print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty dataframe\")\n",
    "\n",
    "    # 3. Handle missing values\n",
    "    try:\n",
    "        linked_data = handle_missing_values(linked_data, trait)\n",
    "        print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error handling missing values: {e}\")\n",
    "        linked_data = pd.DataFrame()  # Create empty dataframe if error occurs\n",
    "\n",
    "    # 4. Check for bias in features\n",
    "    if not linked_data.empty:\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",
    "        is_biased = True\n",
    "        print(\"Cannot check for bias as dataframe is empty after missing value handling\")\n",
    "\n",
    "    # 5. Validate and save cohort information\n",
    "    is_usable = validate_and_save_cohort_info(\n",
    "        is_final=True,\n",
    "        cohort=cohort,\n",
    "        info_path=json_path,\n",
    "        is_gene_available=True,\n",
    "        is_trait_available=True,\n",
    "        is_biased=is_biased,\n",
    "        df=linked_data,\n",
    "        note=\"Dataset contains gene expression data for liver fibrosis progression, which is relevant to liver cirrhosis research.\"\n",
    "    )\n",
    "\n",
    "    # 6. Save the linked data if usable\n",
    "    if is_usable:\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        linked_data.to_csv(out_data_file)\n",
    "        print(f\"Linked data saved to {out_data_file}\")\n",
    "    else:\n",
    "        print(\"Dataset is not usable for analysis. No linked data file saved.\")"
   ]
  }
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