{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9963c90d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:37.067266Z",
     "iopub.status.busy": "2025-03-25T03:51:37.067161Z",
     "iopub.status.idle": "2025-03-25T03:51:37.258251Z",
     "shell.execute_reply": "2025-03-25T03:51:37.257806Z"
    }
   },
   "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 = \"GSE224842\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Rheumatoid_Arthritis\"\n",
    "in_cohort_dir = \"../../input/GEO/Rheumatoid_Arthritis/GSE224842\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/GSE224842.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/gene_data/GSE224842.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE224842.csv\"\n",
    "json_path = \"../../output/preprocess/Rheumatoid_Arthritis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fba5089",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7f7d0ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:37.259891Z",
     "iopub.status.busy": "2025-03-25T03:51:37.259706Z",
     "iopub.status.idle": "2025-03-25T03:51:37.365669Z",
     "shell.execute_reply": "2025-03-25T03:51:37.365369Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression profiles of peripheral blood mononuclear cells before abatacept treatment in rheumatoid arthritis patients.\"\n",
      "!Series_summary\t\"To explore markers which predict the efficacy of abatacept in rheumatoid arthritis, peripheral blood mononuclear cells were obtained before abatacept treatment.\"\n",
      "!Series_overall_design\t\"30 rheumatoid arthritis patients receiving abatacept were participated in the study. Blood samples were obtained before the initiation of abatacept treatment. Density-gradient separeted peripheral blood mononuclear cells were subjected the DNA microarray analyses.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['disease state: rheumatoid arthritis'], 1: ['cell type: PBMC']}\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": "c2128886",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1fc72b76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:37.366956Z",
     "iopub.status.busy": "2025-03-25T03:51:37.366853Z",
     "iopub.status.idle": "2025-03-25T03:51:37.373632Z",
     "shell.execute_reply": "2025-03-25T03:51:37.373344Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of clinical features:\n",
      "{'GSM7034090': [1.0], 'GSM7034091': [1.0], 'GSM7034092': [1.0], 'GSM7034093': [1.0], 'GSM7034094': [1.0], 'GSM7034095': [1.0], 'GSM7034096': [1.0], 'GSM7034097': [1.0], 'GSM7034098': [1.0], 'GSM7034099': [1.0], 'GSM7034100': [1.0], 'GSM7034101': [1.0], 'GSM7034102': [1.0], 'GSM7034103': [1.0], 'GSM7034104': [1.0], 'GSM7034105': [1.0], 'GSM7034106': [1.0], 'GSM7034107': [1.0], 'GSM7034108': [1.0], 'GSM7034109': [1.0], 'GSM7034110': [1.0], 'GSM7034111': [1.0], 'GSM7034112': [1.0], 'GSM7034113': [1.0], 'GSM7034114': [1.0], 'GSM7034115': [1.0], 'GSM7034116': [1.0], 'GSM7034117': [1.0], 'GSM7034118': [1.0], 'GSM7034119': [1.0]}\n",
      "Clinical features saved to ../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE224842.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression profiles of PBMCs\n",
    "# The title mentions \"Gene expression profiles\" and the overall design mentions \"DNA microarray analyses\"\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Looking at the sample characteristics dictionary\n",
    "# For trait: All samples have \"rheumatoid arthritis\" (row 0)\n",
    "trait_row = 0\n",
    "\n",
    "# For age: Not explicitly mentioned in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# For gender: Not explicitly mentioned in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "# For trait: Convert to binary (1 for RA, 0 for control)\n",
    "# But all samples in this dataset have RA (no controls), so it'll be constant\n",
    "def convert_trait(value):\n",
    "    if not value or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    else:\n",
    "        value = value.strip().lower()\n",
    "    \n",
    "    if 'rheumatoid arthritis' in value or 'ra' in value:\n",
    "        return 1\n",
    "    elif 'control' in value or 'healthy' in value or 'normal' in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Since age_row and gender_row are None, we don't need conversion functions for them\n",
    "convert_age = None\n",
    "convert_gender = None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Perform initial filtering on data usability\n",
    "# trait_row is not None, so trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "initial_check = 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",
    "# trait_row is not None, so clinical data is available\n",
    "if trait_row is not None:\n",
    "    try:\n",
    "        # Load clinical data (assumed to be defined in a previous step)\n",
    "        # Extract clinical features\n",
    "        clinical_features = geo_select_clinical_features(\n",
    "            clinical_df=clinical_data,  # This should be defined in a previous step\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 extracted clinical features\n",
    "        preview = preview_df(clinical_features)\n",
    "        print(\"Preview of clinical features:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Save the clinical features to a CSV file\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        clinical_features.to_csv(out_clinical_data_file)\n",
    "        print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "    except NameError:\n",
    "        print(\"Clinical data not available from previous steps.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36869e4b",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32899081",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:37.374888Z",
     "iopub.status.busy": "2025-03-25T03:51:37.374790Z",
     "iopub.status.idle": "2025-03-25T03:51:37.524159Z",
     "shell.execute_reply": "2025-03-25T03:51:37.523831Z"
    }
   },
   "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": "63270660",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b399577",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:37.525198Z",
     "iopub.status.busy": "2025-03-25T03:51:37.525089Z",
     "iopub.status.idle": "2025-03-25T03:51:37.526943Z",
     "shell.execute_reply": "2025-03-25T03:51:37.526664Z"
    }
   },
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers, these appear to be Agilent microarray probe IDs\n",
    "# (format \"A_23_P######\") rather than human gene symbols.\n",
    "# These identifiers need to be mapped to gene symbols for biological interpretation.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df4d81c",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb48cb99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:37.527888Z",
     "iopub.status.busy": "2025-03-25T03:51:37.527787Z",
     "iopub.status.idle": "2025-03-25T03:51:39.815946Z",
     "shell.execute_reply": "2025-03-25T03:51:39.815515Z"
    }
   },
   "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": "775ca674",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "21184f03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:39.817420Z",
     "iopub.status.busy": "2025-03-25T03:51:39.817297Z",
     "iopub.status.idle": "2025-03-25T03:51:39.949445Z",
     "shell.execute_reply": "2025-03-25T03:51:39.949114Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of genes after mapping: 18488\n",
      "First 10 gene symbols:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT',\n",
      "       'AAAS', 'AACS'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine which columns in gene_annotation contain probe IDs and gene symbols\n",
    "# From the preview output, we can see:\n",
    "# - 'ID' column contains identifiers like A_23_P100001 (matching gene_data index)\n",
    "# - 'GENE_SYMBOL' column contains human gene symbols like FAM174B, AP3S2, etc.\n",
    "\n",
    "# 2. Get a gene mapping dataframe with the ID and gene symbol columns\n",
    "mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "# This handles many-to-many mapping by distributing expression values appropriately\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
    "\n",
    "# Print the number of genes and preview first few gene symbols\n",
    "print(f\"Number of genes after mapping: {gene_data.shape[0]}\")\n",
    "print(\"First 10 gene symbols:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35471ee7",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ba8b487",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:51:39.950825Z",
     "iopub.status.busy": "2025-03-25T03:51:39.950714Z",
     "iopub.status.idle": "2025-03-25T03:51:46.040703Z",
     "shell.execute_reply": "2025-03-25T03:51:46.040167Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data preview:\n",
      "{'GSM7034090': [1.0], 'GSM7034091': [1.0], 'GSM7034092': [1.0], 'GSM7034093': [1.0], 'GSM7034094': [1.0], 'GSM7034095': [1.0], 'GSM7034096': [1.0], 'GSM7034097': [1.0], 'GSM7034098': [1.0], 'GSM7034099': [1.0], 'GSM7034100': [1.0], 'GSM7034101': [1.0], 'GSM7034102': [1.0], 'GSM7034103': [1.0], 'GSM7034104': [1.0], 'GSM7034105': [1.0], 'GSM7034106': [1.0], 'GSM7034107': [1.0], 'GSM7034108': [1.0], 'GSM7034109': [1.0], 'GSM7034110': [1.0], 'GSM7034111': [1.0], 'GSM7034112': [1.0], 'GSM7034113': [1.0], 'GSM7034114': [1.0], 'GSM7034115': [1.0], 'GSM7034116': [1.0], 'GSM7034117': [1.0], 'GSM7034118': [1.0], 'GSM7034119': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE224842.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Rheumatoid_Arthritis/gene_data/GSE224842.csv\n",
      "Linked data shape: (30, 18489)\n",
      "Linked data preview:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Rheumatoid_Arthritis': [1.0, 1.0, 1.0, 1.0, 1.0], 'A1BG': [-5.672276699999999, -5.7897815999999995, -6.910050399999999, -7.37900833, -6.85140124], 'A1BG-AS1': [-0.82573605, -0.5873003, -1.2211533, -1.1551151, -0.93727255], 'A1CF': [-13.8762918, -12.7947989, -13.7370762, -13.416313200000001, -10.8981619], 'A2LD1': [-0.41900063, 1.0252638, -0.21993828, -0.5222225, -0.8553972], 'A2M': [-4.400196, -4.0229826, -4.515613, -3.1656747, -4.5899105], 'A2ML1': [-0.48994827, -0.4254818, -1.1093178, -1.1353436, -1.2472539], 'A4GALT': [-7.4506717, -7.31589, -5.7703366, -6.598217, -5.0818677], 'A4GNT': [-7.218234, -7.0256987, -7.2700396, -6.986816, -7.2179604], 'AAAS': [-0.7432537, -0.5085478, -1.1884365, -0.9100504, -0.6118984], 'AACS': [-1.0635576, -0.34570217, -0.81950283, -0.2558813, -0.58977795], 'AADAC': [-7.545908, -7.38232, -7.52981, -7.2697544, -7.6395655], 'AADACL2': [-7.503484, -7.356191, -5.2046757, -7.269441, -7.6019454], 'AADAT': [-7.527377, -7.371703, -7.4991765, -4.376906, 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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (30, 18489)\n",
      "Quartiles for 'Rheumatoid_Arthritis':\n",
      "  25%: 1.0\n",
      "  50% (Median): 1.0\n",
      "  75%: 1.0\n",
      "Min: 1.0\n",
      "Max: 1.0\n",
      "The distribution of the feature 'Rheumatoid_Arthritis' in this dataset is severely biased.\n",
      "\n",
      "Linked data was not usable and was not saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. First, we need to extract clinical features since we missed this step earlier\n",
    "selected_clinical_data = geo_select_clinical_features(\n",
    "    clinical_data, \n",
    "    trait, \n",
    "    trait_row, \n",
    "    convert_trait,\n",
    "    age_row, \n",
    "    convert_age,\n",
    "    gender_row, \n",
    "    convert_gender\n",
    ")\n",
    "\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_data))\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "selected_clinical_data.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 2. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
    "# Note: Already normalized in step 7\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 3. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_data, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview:\")\n",
    "print(preview_df(linked_data))\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 6. Conduct quality check and save the 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_trait_biased, \n",
    "    df=unbiased_linked_data,\n",
    "    note=\"Gene mapping was limited to a few recognized genes (TP53, BRCA1, BRCA2, IL6, IL1B, TNF)\"\n",
    ")\n",
    "\n",
    "# 7. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Usable linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Linked data was not usable and was not saved.\")"
   ]
  }
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