{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c9ee51f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:29:34.962892Z",
     "iopub.status.busy": "2025-03-25T06:29:34.962702Z",
     "iopub.status.idle": "2025-03-25T06:29:35.127107Z",
     "shell.execute_reply": "2025-03-25T06:29:35.126723Z"
    }
   },
   "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 = \"Aniridia\"\n",
    "cohort = \"GSE137996\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Aniridia\"\n",
    "in_cohort_dir = \"../../input/GEO/Aniridia/GSE137996\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Aniridia/GSE137996.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Aniridia/gene_data/GSE137996.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Aniridia/clinical_data/GSE137996.csv\"\n",
    "json_path = \"../../output/preprocess/Aniridia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f9a908",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0a00aee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:29:35.128310Z",
     "iopub.status.busy": "2025-03-25T06:29:35.128168Z",
     "iopub.status.idle": "2025-03-25T06:29:35.305645Z",
     "shell.execute_reply": "2025-03-25T06:29:35.305281Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Conjunctival mRNA and miRNA expression profiles in congenital aniridia are genotype and phenotype dependent (AKK mRNA)\"\n",
      "!Series_summary\t\"Purpose:\"\n",
      "!Series_summary\t\"To evaluate conjunctival cell microRNA and mRNA expression in relation to observed phenotype and genotype of aniridia-associated keratopathy (AAK) in a cohort of subjects with congenital aniridia.\"\n",
      "!Series_summary\t\"Methods:\"\n",
      "!Series_summary\t\"Using impression cytology, bulbar conjunctival cells were sampled from 20 subjects with congenital aniridia and 20 age and sex-matched healthy control subjects. RNA was extracted and microRNA and mRNA analysis was performed using microarrays. Results were related to the presence and severity of AAK determined by a standardized clinical grading scale and to the genotype (PAX6 mutation?) determined by clinical genetics.\"\n",
      "!Series_summary\t\"Results:\"\n",
      "!Series_summary\t\"Of the 2549 microRNAs analyzed, 21 were differentially expressed relative to controls. Among these miR-204-5p, an inhibitor of corneal neovascularization, was downregulated 26.8-fold, while miR-5787 and miR-224-5p were upregulated 2.8 and 2.4-fold relative to controls, respectively. At the mRNA level, 539 transcripts were differentially expressed, among these FOSB and FOS were upregulated 17.5 and 9.7-fold respectively, and JUN by 2.9-fold, all components of the AP-1 transcription factor complex. Pathway analysis revealed dysregulation of several enriched pathways including PI3K-Akt, MAPK, and Ras signaling pathways in aniridia. For several microRNAs and transcripts, expression levels aligned with AAK severity, while in very mild cases with missense or non-PAX6 coding mutations, gene expression was only minimally altered.\"\n",
      "!Series_summary\t\"Conclusion:\"\n",
      "!Series_summary\t\"In aniridia, specific factors and pathways are strongly dysregulated in conjunctival cells, suggesting that the conjunctiva in aniridia is abnormally maintained in a pro-angiogenic and proliferative state, promoting the aggressivity of AAK in a mutation-dependent manner. Transcriptional profiling of conjunctival cells at the microRNA and mRNA levels presents a powerful, minimally-invasive means to assess the regulation of cell dysfunction at the ocular surface.\"\n",
      "!Series_overall_design\t\"MiRNA and mRNA expression profiles of conjunctival cells from 20 patients with aniridia associated keratopathy compared to controls\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['age: 20', 'age: 28', 'age: 38', 'age: 57', 'age: 26', 'age: 18', 'age: 36', 'age: 42', 'age: 55', 'age: 54', 'age: 34', 'age: 51', 'age: 46', 'age: 52', 'age: 53', 'age: 40', 'age: 39', 'age: 59', 'age: 32', 'age: 37', 'age: 29', 'age: 19', 'age: 25', 'age: 22'], 1: ['gender: F', 'gender: M', 'gender: W'], 2: ['disease: AAK', 'disease: healthy control'], 3: ['Stage: Severe', 'Stage: Mild', 'Stage: NA'], 4: ['tissue: conjunctival 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": "b967c988",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c7e073f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:29:35.306881Z",
     "iopub.status.busy": "2025-03-25T06:29:35.306770Z",
     "iopub.status.idle": "2025-03-25T06:29:35.313900Z",
     "shell.execute_reply": "2025-03-25T06:29:35.313513Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot extract clinical features: clinical data file not found.\n",
      "Empty clinical data saved to ../../output/preprocess/Aniridia/clinical_data/GSE137996.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on background information, this dataset contains mRNA expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait (Aniridia/AAK): From key 2 and 3 we can determine if person has AAK and severity\n",
    "trait_row = 2  # 'disease: AAK' or 'disease: healthy control'\n",
    "\n",
    "# For age: Available at key 0\n",
    "age_row = 0  # Age information is available\n",
    "\n",
    "# For gender: Available at key 1\n",
    "gender_row = 1  # Gender information is available\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert the trait information to binary (0: control, 1: AAK).\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    value = value.lower().strip()\n",
    "    if 'disease:' in value:\n",
    "        value = value.split('disease:')[-1].strip()\n",
    "    \n",
    "    if 'healthy control' in value or 'control' in value:\n",
    "        return 0\n",
    "    elif 'aak' in value or 'aniridia' in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Extract and convert age to continuous value.\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    if 'age:' in value:\n",
    "        try:\n",
    "            age = int(value.split('age:')[-1].strip())\n",
    "            return age\n",
    "        except:\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0: female, 1: male).\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    value = value.lower().strip()\n",
    "    if 'gender:' in value:\n",
    "        value = value.split('gender:')[-1].strip()\n",
    "    \n",
    "    if value == 'f' or value == 'w':  # Assuming 'W' means woman\n",
    "        return 0\n",
    "    elif value == 'm':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Trait data is available if trait_row is not None\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort, \n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Since we don't have the clinical_data.csv file, we need to create a clinical_df\n",
    "    # The expected format for geo_select_clinical_features is a DataFrame where each row\n",
    "    # represents a feature type and each column represents a sample\n",
    "    \n",
    "    # Create dummy data for demonstration purposes - the actual function will \n",
    "    # expect data in the proper format\n",
    "    sample_ids = [f\"GSM{i}\" for i in range(1, 21)]  # 20 samples based on background info\n",
    "    \n",
    "    # Create empty DataFrame with sample IDs as columns\n",
    "    clinical_df = pd.DataFrame(index=range(5), columns=sample_ids)\n",
    "    \n",
    "    # This is a placeholder to satisfy the function call\n",
    "    # In a real scenario, we would need actual clinical data arranged properly\n",
    "    \n",
    "    # Since we don't have the actual clinical data, we should skip this part\n",
    "    # and just acknowledge that clinical data extraction cannot be performed\n",
    "    print(\"Cannot extract clinical features: clinical data file not found.\")\n",
    "    \n",
    "    # Write an empty DataFrame to the output file to maintain workflow\n",
    "    empty_clinical_df = pd.DataFrame(columns=['Sample', 'Aniridia', 'Age', 'Gender'])\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    empty_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Empty clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d7efe5",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "531a75ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:29:35.315195Z",
     "iopub.status.busy": "2025-03-25T06:29:35.315088Z",
     "iopub.status.idle": "2025-03-25T06:29:35.566604Z",
     "shell.execute_reply": "2025-03-25T06:29:35.566076Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['A_19_P00315452', 'A_19_P00315492', 'A_19_P00315493', 'A_19_P00315502',\n",
      "       'A_19_P00315506', 'A_19_P00315518', 'A_19_P00315519', 'A_19_P00315529',\n",
      "       'A_19_P00315541', 'A_19_P00315543', 'A_19_P00315551', 'A_19_P00315581',\n",
      "       'A_19_P00315584', 'A_19_P00315593', 'A_19_P00315603', 'A_19_P00315625',\n",
      "       'A_19_P00315627', 'A_19_P00315631', 'A_19_P00315641', 'A_19_P00315647'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 58201 genes × 40 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": "82029ca3",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "631bd0f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:29:35.568200Z",
     "iopub.status.busy": "2025-03-25T06:29:35.568043Z",
     "iopub.status.idle": "2025-03-25T06:29:35.570489Z",
     "shell.execute_reply": "2025-03-25T06:29:35.570014Z"
    }
   },
   "outputs": [],
   "source": [
    "# The gene identifiers appear to be Agilent microarray probe IDs (starting with \"A_19_P\"),\n",
    "# not standard human gene symbols. These will need to be mapped to gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f814a2d",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c37f11e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:29:35.572065Z",
     "iopub.status.busy": "2025-03-25T06:29:35.571949Z",
     "iopub.status.idle": "2025-03-25T06:29:39.095227Z",
     "shell.execute_reply": "2025-03-25T06:29:39.094870Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760'], 'CONTROL_TYPE': ['pos', 'pos', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_001105533', nan], 'GB_ACC': [nan, nan, nan, 'NM_001105533', nan], 'LOCUSLINK_ID': [nan, nan, nan, 79974.0, 54880.0], 'GENE_SYMBOL': [nan, nan, nan, 'CPED1', 'BCOR'], 'GENE_NAME': [nan, nan, nan, 'cadherin-like and PC-esterase domain containing 1', 'BCL6 corepressor'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.189652', nan], 'ENSEMBL_ID': [nan, nan, nan, nan, 'ENST00000378463'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_001105533|gb|AK025639|gb|BC030538|tc|THC2601673', 'ens|ENST00000378463'], 'CHROMOSOMAL_LOCATION': [nan, nan, 'unmapped', 'chr7:120901888-120901947', 'chrX:39909128-39909069'], 'CYTOBAND': [nan, nan, nan, 'hs|7q31.31', 'hs|Xp11.4'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens cadherin-like and PC-esterase domain containing 1 (CPED1), transcript variant 2, mRNA [NM_001105533]', 'BCL6 corepressor [Source:HGNC Symbol;Acc:HGNC:20893] [ENST00000378463]'], 'GO_ID': [nan, nan, nan, 'GO:0005783(endoplasmic reticulum)', 'GO:0000122(negative regulation of transcription from RNA polymerase II promoter)|GO:0000415(negative regulation of histone H3-K36 methylation)|GO:0003714(transcription corepressor activity)|GO:0004842(ubiquitin-protein ligase activity)|GO:0005515(protein binding)|GO:0005634(nucleus)|GO:0006351(transcription, DNA-dependent)|GO:0007507(heart development)|GO:0008134(transcription factor binding)|GO:0030502(negative regulation of bone mineralization)|GO:0031072(heat shock protein binding)|GO:0031519(PcG protein complex)|GO:0035518(histone H2A monoubiquitination)|GO:0042476(odontogenesis)|GO:0042826(histone deacetylase binding)|GO:0044212(transcription regulatory region DNA binding)|GO:0045892(negative regulation of transcription, DNA-dependent)|GO:0051572(negative regulation of histone H3-K4 methylation)|GO:0060021(palate development)|GO:0065001(specification of axis polarity)|GO:0070171(negative regulation of tooth mineralization)'], 'SEQUENCE': [nan, nan, 'AATACATGTTTTGGTAAACACTCGGTCAGAGCACCCTCTTTCTGTGGAATCAGACTGGCA', 'GCTTATCTCACCTAATACAGGGACTATGCAACCAAGAAACTGGAAATAAAAACAAAGATA', 'CATCAAAGCTACGAGAGATCCTACACACCCAGATTTAAAAAATAATAAAAACTTAAGGGC'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760']}\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": "0e28d211",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6047a338",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:29:39.096551Z",
     "iopub.status.busy": "2025-03-25T06:29:39.096426Z",
     "iopub.status.idle": "2025-03-25T06:29:39.356237Z",
     "shell.execute_reply": "2025-03-25T06:29:39.355886Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Original probe data dimensions: 29222 probes × 40 samples\n",
      "Gene mapping dataframe dimensions: 48862 rows × 2 columns\n",
      "Mapped gene data dimensions: 29222 genes × 40 samples\n",
      "\n",
      "First 10 gene symbols after mapping:\n",
      "['A1BG', 'A1BG-AS1', 'A1CF', 'A1CF-2', 'A1CF-3', 'A2M', 'A2M-1', 'A2M-AS1', 'A2ML1', 'A2MP1']\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the columns with probe IDs and gene symbols\n",
    "# From the output, we can see:\n",
    "# - Probe IDs in gene expression data are like 'A_19_P00315452'\n",
    "# - In gene annotation, 'ID' contains probe IDs and 'GENE_SYMBOL' contains gene symbols\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the ID and GENE_SYMBOL columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level expression to gene expression\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print information about the mapping process\n",
    "print(f\"\\nOriginal probe data dimensions: {len(gene_data.index)} probes × {gene_data.shape[1]} samples\")\n",
    "print(f\"Gene mapping dataframe dimensions: {gene_mapping.shape[0]} rows × {gene_mapping.shape[1]} columns\")\n",
    "print(f\"Mapped gene data dimensions: {len(gene_data.index)} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Preview the first few genes after mapping\n",
    "print(\"\\nFirst 10 gene symbols after mapping:\")\n",
    "print(list(gene_data.index[:10]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba70067",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08b65098",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:29:39.357598Z",
     "iopub.status.busy": "2025-03-25T06:29:39.357482Z",
     "iopub.status.idle": "2025-03-25T06:29:49.945972Z",
     "shell.execute_reply": "2025-03-25T06:29:49.945492Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols in the gene expression data...\n",
      "Original gene data shape: 29222 genes × 40 samples\n",
      "Normalized gene data shape: 20778 genes × 40 samples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene expression data saved to ../../output/preprocess/Aniridia/gene_data/GSE137996.csv\n",
      "Extracting clinical features from original clinical data...\n",
      "Clinical features saved to ../../output/preprocess/Aniridia/clinical_data/GSE137996.csv\n",
      "Clinical features preview:\n",
      "{'GSM4096389': [1.0, 20.0, 0.0], 'GSM4096390': [1.0, 20.0, 0.0], 'GSM4096391': [1.0, 28.0, 0.0], 'GSM4096392': [1.0, 20.0, 0.0], 'GSM4096393': [1.0, 38.0, 0.0], 'GSM4096394': [1.0, 57.0, 1.0], 'GSM4096395': [1.0, 26.0, 0.0], 'GSM4096396': [1.0, 18.0, 1.0], 'GSM4096397': [1.0, 36.0, 0.0], 'GSM4096398': [1.0, 42.0, 0.0], 'GSM4096399': [1.0, 18.0, 0.0], 'GSM4096400': [1.0, 42.0, 0.0], 'GSM4096401': [1.0, 36.0, 1.0], 'GSM4096402': [1.0, 28.0, 0.0], 'GSM4096403': [1.0, 55.0, 0.0], 'GSM4096404': [1.0, 54.0, 1.0], 'GSM4096405': [1.0, 34.0, 1.0], 'GSM4096406': [1.0, 51.0, 0.0], 'GSM4096407': [1.0, 46.0, 0.0], 'GSM4096408': [1.0, 52.0, 0.0], 'GSM4096409': [0.0, 53.0, 0.0], 'GSM4096410': [0.0, 54.0, 1.0], 'GSM4096411': [0.0, 40.0, 0.0], 'GSM4096412': [0.0, 55.0, 0.0], 'GSM4096413': [0.0, 57.0, 0.0], 'GSM4096414': [0.0, 28.0, 0.0], 'GSM4096415': [0.0, 39.0, 0.0], 'GSM4096416': [0.0, 59.0, 0.0], 'GSM4096417': [0.0, 20.0, 0.0], 'GSM4096418': [0.0, 32.0, 1.0], 'GSM4096419': [0.0, 37.0, 1.0], 'GSM4096420': [0.0, 34.0, 0.0], 'GSM4096421': [0.0, 28.0, 0.0], 'GSM4096422': [0.0, 28.0, 0.0], 'GSM4096423': [0.0, 29.0, 1.0], 'GSM4096424': [0.0, 19.0, 0.0], 'GSM4096425': [0.0, 25.0, 0.0], 'GSM4096426': [0.0, 25.0, 1.0], 'GSM4096427': [0.0, 34.0, 0.0], 'GSM4096428': [0.0, 22.0, 0.0]}\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (40, 20781)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (40, 20781)\n",
      "\n",
      "Checking for bias in feature variables:\n",
      "For the feature 'Aniridia', the least common label is '1.0' with 20 occurrences. This represents 50.00% of the dataset.\n",
      "The distribution of the feature 'Aniridia' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 25.75\n",
      "  50% (Median): 34.0\n",
      "  75%: 47.25\n",
      "Min: 18.0\n",
      "Max: 59.0\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "For the feature 'Gender', the least common label is '1.0' with 10 occurrences. This represents 25.00% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is fine.\n",
      "\n",
      "A new JSON file was created at: ../../output/preprocess/Aniridia/cohort_info.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Aniridia/GSE137996.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols in the gene expression data...\")\n",
    "# From the previous step output, we can see the data already contains gene symbols\n",
    "# like 'A1BG', 'A1CF', 'A2M' which need to be normalized\n",
    "gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Original gene data shape: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "print(f\"Normalized gene data shape: {gene_data_normalized.shape[0]} genes × {gene_data_normalized.shape[1]} samples\")\n",
    "\n",
    "# Save the normalized gene data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data_normalized.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Extract clinical features from scratch instead of loading the empty file\n",
    "print(\"Extracting clinical features from original clinical data...\")\n",
    "clinical_features = 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",
    "# Save the extracted clinical features\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",
    "\n",
    "print(\"Clinical features preview:\")\n",
    "print(preview_df(clinical_features))\n",
    "\n",
    "# Check if clinical features were successfully extracted\n",
    "if clinical_features.empty:\n",
    "    print(\"Failed to extract clinical features. Dataset cannot be processed further.\")\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,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Clinical features could not be extracted from the dataset.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to lack of clinical features.\")\n",
    "else:\n",
    "    # 2. Link clinical and genetic data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data_normalized)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "    # 3. Handle missing values systematically\n",
    "    linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
    "    print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "    # 4. Check if the dataset is biased\n",
    "    print(\"\\nChecking for bias in feature variables:\")\n",
    "    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "    # 5. Conduct final quality validation\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 aniridia patients and healthy controls.\"\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 trait association studies, linked data not saved.\")"
   ]
  }
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