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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca73ee89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:09.118495Z",
     "iopub.status.busy": "2025-03-25T06:18:09.118394Z",
     "iopub.status.idle": "2025-03-25T06:18:09.277374Z",
     "shell.execute_reply": "2025-03-25T06:18:09.277031Z"
    }
   },
   "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 = \"Acute_Myeloid_Leukemia\"\n",
    "cohort = \"GSE222616\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Acute_Myeloid_Leukemia\"\n",
    "in_cohort_dir = \"../../input/GEO/Acute_Myeloid_Leukemia/GSE222616\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/GSE222616.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222616.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE222616.csv\"\n",
    "json_path = \"../../output/preprocess/Acute_Myeloid_Leukemia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f733e18",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da2f54a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:09.278773Z",
     "iopub.status.busy": "2025-03-25T06:18:09.278630Z",
     "iopub.status.idle": "2025-03-25T06:18:09.388480Z",
     "shell.execute_reply": "2025-03-25T06:18:09.388183Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Treatment with HKMTI-1-005, a dual inhibitor of EZH2 and G9a/GLP histone methyltransferases, promotes differentiation of acute myeloid leukaemialeukemia\"\n",
      "!Series_summary\t\"The canonical function of EZH2 is methylation of H3K27, although important non-canonical roles have recently been described. EZH2 mutation or deregulated expression has been conclusively demonstrated in the pathogenesis and response to treatment of acute myeloid leukemia (AML), thus making it an attractive therapeutic target. In this study, we therefore investigated whether inhibition of EZH2 might also improve the response of non-APL AML cells to ATRA-based therapy. We focused on GSK-343, a pyridone-containing S-adenosyl-L-methionine (SAM) cofactor-competitive EZH2 inhibitor that is representative of its class, and HKMTI-1-005, a substrate-competitive dual inhibitor targeting EZH2 and the closely related G9A/GLP H3K9 methyltransferases. We found that treatment with HKMTI-1-005 phenocopied EZH2 knockdown and was more effective in inducing myeloid differentiation (both in the absence and presence of ATRA) than GSK-343, despite the efficacy of GSK-343 in terms of abolishing H3K27 trimethylation. Furthermore, transcriptomic analysis revealed that in contrast to treatment with GSK-343, HKMTI-1-005 upregulated the expression of myeloid differentiation pathway genes with and without ATRA, while downregulating genes associated with a hematopoietic stem cell phenotype.\"\n",
      "!Series_summary\t\"Expression data (by HuGene 1.0 ST Affymetrix arrays) from HL-60 AML cells treated for 3 days with EZH2 inhibitors (10µM GSK343 or 2.5µM HKMTI-1-005) with or without AtRA (0.1µM) as well as data from HL-60 cells stabelly transduced with EZH2 KD (shEZH2#1 and shEZH2#2) or control (Scr) lentiviruses, selected and expanded for ~20 days with or without treatment with 0.1µM AtRA for 3 days.\"\n",
      "!Series_overall_design\t\"Data from biological triplicates from each of the following conditions: GSK343, HKMTI-1-005, EZH2 KD shA, EZH2 KD shC, non-targeting control Scr and control cells (untreated and untransduced) +/- AtRA was normalised (GC-RMA and batch effect remove with Partek) and differentially expressed genes were identified in all conditions compared to control (-AtRA). The top 40th percentile of expressed genes according to normalised values was taken and an FDR or 0.05 was used as a cut-off between all conditions. Genes with at least 1.3 fold change in expression were taken for further GO and GSEA analysis.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['cell line: HL-60'], 1: ['cell type: Acute myeloid leukaemia'], 2: ['treatment: untreated', 'treatment: GSK343', 'treatment: HKMTI-1-005', 'treatment: ATRA', 'treatment: GSK343+ATRA', 'treatment: HKMTI-1-005+ATRA', 'treatment: EZH2_KD_shEZH2#1+ATRA', 'treatment: EZH2_KD_shEZH2#1', 'treatment: EZH2_KD_shEZH2#2+ATRA', 'treatment: EZH2_KD_shEZH2#2', 'treatment: non-targeting_control_Scr+ATRA', 'treatment: non-targeting_control_Scr', 'treatment: WT+ATRA', 'treatment: WT']}\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": "36d60f59",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "115747da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:09.389550Z",
     "iopub.status.busy": "2025-03-25T06:18:09.389444Z",
     "iopub.status.idle": "2025-03-25T06:18:09.396083Z",
     "shell.execute_reply": "2025-03-25T06:18:09.395791Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical DataFrame Preview:\n",
      "{'GSM6927801': [1.0], 'GSM6927802': [1.0], 'GSM6927803': [1.0], 'GSM6927804': [1.0], 'GSM6927805': [1.0], 'GSM6927806': [1.0], 'GSM6927807': [1.0], 'GSM6927808': [1.0], 'GSM6927809': [1.0], 'GSM6927810': [1.0], 'GSM6927811': [1.0], 'GSM6927812': [1.0], 'GSM6927813': [1.0], 'GSM6927814': [1.0], 'GSM6927815': [1.0], 'GSM6927816': [1.0], 'GSM6927817': [1.0], 'GSM6927818': [1.0], 'GSM6927819': [1.0], 'GSM6927820': [1.0], 'GSM6927821': [1.0], 'GSM6927822': [1.0], 'GSM6927823': [1.0], 'GSM6927824': [1.0], 'GSM6927825': [1.0], 'GSM6927826': [1.0], 'GSM6927827': [1.0], 'GSM6927828': [1.0], 'GSM6927829': [1.0], 'GSM6927830': [1.0], 'GSM6927831': [1.0], 'GSM6927832': [1.0], 'GSM6927833': [1.0], 'GSM6927834': [1.0], 'GSM6927835': [1.0], 'GSM6927836': [1.0], 'GSM6927837': [1.0], 'GSM6927838': [1.0], 'GSM6927839': [1.0], 'GSM6927840': [1.0], 'GSM6927841': [1.0], 'GSM6927842': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE222616.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# From the background information, this dataset contains expression data from HuGene 1.0 ST Affymetrix arrays,\n",
    "# which indicates gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (Acute Myeloid Leukemia): \n",
    "# All samples are from HL-60 AML cells, so all samples have AML. Row 1 indicates \"cell type: Acute myeloid leukaemia\"\n",
    "trait_row = 1\n",
    "\n",
    "# For age:\n",
    "# There's no age information available in the sample characteristics dictionary\n",
    "age_row = None\n",
    "\n",
    "# For gender:\n",
    "# There's no gender information available in the sample characteristics dictionary\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "# For trait (AML): binary conversion (all samples are AML)\n",
    "def convert_trait(value):\n",
    "    # All samples have AML according to the sample characteristics\n",
    "    if \"acute myeloid\" in value.lower():\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Since age data is not available, we still define a placeholder function\n",
    "def convert_age(value):\n",
    "    return None\n",
    "\n",
    "# Since gender data is not available, we still define a placeholder function\n",
    "def convert_gender(value):\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Initial filtering on usability\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",
    "    # Using the library function to extract clinical features\n",
    "    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 clinical dataframe\n",
    "    print(\"Clinical DataFrame Preview:\")\n",
    "    print(preview_df(clinical_df))\n",
    "    \n",
    "    # Save the clinical data to a CSV file\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_df.to_csv(out_clinical_data_file, index=True)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d708e6",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09c94b28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:09.397062Z",
     "iopub.status.busy": "2025-03-25T06:18:09.396957Z",
     "iopub.status.idle": "2025-03-25T06:18:09.510714Z",
     "shell.execute_reply": "2025-03-25T06:18:09.510379Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['7892501', '7892502', '7892503', '7892504', '7892505', '7892506',\n",
      "       '7892507', '7892508', '7892509', '7892510', '7892511', '7892512',\n",
      "       '7892513', '7892514', '7892515', '7892516', '7892517', '7892518',\n",
      "       '7892519', '7892520'],\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": "4080ae8b",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "882e7a13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:09.511980Z",
     "iopub.status.busy": "2025-03-25T06:18:09.511872Z",
     "iopub.status.idle": "2025-03-25T06:18:09.513684Z",
     "shell.execute_reply": "2025-03-25T06:18:09.513416Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers don't appear to be human gene symbols\n",
    "# They look like probe IDs or other microarray identifiers (numeric format)\n",
    "# Human gene symbols typically follow patterns like BRCA1, TP53, APOE, etc.\n",
    "# Therefore, these identifiers would need to be mapped to proper gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e2ba28",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f9a9298",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:09.514770Z",
     "iopub.status.busy": "2025-03-25T06:18:09.514674Z",
     "iopub.status.idle": "2025-03-25T06:18:12.269437Z",
     "shell.execute_reply": "2025-03-25T06:18:12.269105Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['7896736', '7896738', '7896740', '7896742', '7896744'], 'GB_LIST': [nan, nan, 'NM_001004195,NM_001005240,NM_001005484,BC136848,BC136867,BC136907,BC136908', 'NR_024437,XM_006711854,XM_006726377,XR_430662,AK298283,AL137655,BC032332,BC118988,BC122537,BC131690,NM_207366,AK301928,BC071667', 'NM_001005221,NM_001005224,NM_001005277,NM_001005504,BC137547,BC137568'], 'SPOT_ID': ['chr1:53049-54936', 'chr1:63015-63887', 'chr1:69091-70008', 'chr1:334129-334296', 'chr1:367659-368597'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': ['53049', '63015', '69091', '334129', '367659'], 'RANGE_STOP': ['54936', '63887', '70008', '334296', '368597'], 'total_probes': [7.0, 31.0, 24.0, 6.0, 36.0], 'gene_assignment': ['---', 'ENST00000328113 // OR4G2P // olfactory receptor, family 4, subfamily G, member 2 pseudogene // --- // --- /// ENST00000492842 // OR4G11P // olfactory receptor, family 4, subfamily G, member 11 pseudogene // --- // --- /// ENST00000588632 // OR4G1P // olfactory receptor, family 4, subfamily G, member 1 pseudogene // --- // ---', 'NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000318050 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// ENST00000326183 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000585993 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136848 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136867 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136907 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// BC136908 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682', 'NR_024437 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// XM_006711854 // LOC101060626 // F-box only protein 25-like // --- // 101060626 /// XM_006726377 // LOC101060626 // F-box only protein 25-like // --- // 101060626 /// XR_430662 // LOC101927097 // uncharacterized LOC101927097 // --- // 101927097 /// ENST00000279067 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// ENST00000431812 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000431812 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000433444 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000436899 // LINC00266-3 // long intergenic non-protein coding RNA 266-3 // --- // --- /// ENST00000445252 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// ENST00000455207 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000455207 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000455464 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000455464 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000456398 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000601814 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000601814 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// AK298283 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// AL137655 // LOC100134822 // uncharacterized LOC100134822 // --- // 100134822 /// BC032332 // PCMTD2 // protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing 2 // 20q13.33 // 55251 /// BC118988 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// BC122537 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// BC131690 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// NM_207366 // SEPT14 // septin 14 // 7p11.2 // 346288 /// ENST00000388975 // SEPT14 // septin 14 // 7p11.2 // 346288 /// ENST00000427373 // LINC00266-4P // long intergenic non-protein coding RNA 266-4, pseudogene // --- // --- /// ENST00000431796 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000509776 // LINC00266-2P // long intergenic non-protein coding RNA 266-2, pseudogene // --- // --- /// ENST00000570230 // LOC101929008 // uncharacterized LOC101929008 // --- // 101929008 /// ENST00000570230 // LOC101929038 // uncharacterized LOC101929038 // --- // 101929038 /// ENST00000570230 // LOC101930130 // uncharacterized LOC101930130 // --- // 101930130 /// ENST00000570230 // LOC101930567 // uncharacterized LOC101930567 // --- // 101930567 /// AK301928 // SEPT14 // septin 14 // 7p11.2 // 346288', 'NM_001005221 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// NM_001005224 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// NM_001005277 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// NM_001005504 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// ENST00000320901 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// ENST00000332831 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000332831 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000332831 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000402444 // OR4F7P // olfactory receptor, family 4, subfamily F, member 7 pseudogene // --- // --- /// ENST00000405102 // OR4F1P // olfactory receptor, family 4, subfamily F, member 1 pseudogene // --- // --- /// ENST00000424047 // OR4F2P // olfactory receptor, family 4, subfamily F, member 2 pseudogene // --- // --- /// ENST00000426406 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000426406 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000426406 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000456475 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000456475 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000456475 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000559128 // OR4F28P // olfactory receptor, family 4, subfamily F, member 28 pseudogene // --- // --- /// BC137547 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137547 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137547 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// BC137568 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137568 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137568 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000589943 // OR4F8P // olfactory receptor, family 4, subfamily F, member 8 pseudogene // --- // ---'], 'mrna_assignment': ['NONHSAT060105 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 7 // 7 // 0', 'ENST00000328113 // ENSEMBL // havana:known chromosome:GRCh38:15:101926805:101927707:-1 gene:ENSG00000183909 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000492842 // ENSEMBL // havana:known chromosome:GRCh38:1:62948:63887:1 gene:ENSG00000240361 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000588632 // ENSEMBL // havana:known chromosome:GRCh38:19:104535:105471:1 gene:ENSG00000267310 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT000016 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT051704 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT060106 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0', 'NM_001004195 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 4 (OR4F4), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005240 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 17 (OR4F17), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000318050 // ENSEMBL // ensembl:known chromosome:GRCh38:19:110643:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000326183 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:15:101922042:101923095:-1 gene:ENSG00000177693 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000335137 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000585993 // ENSEMBL // havana:known chromosome:GRCh38:19:107461:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136848 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136867 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168481 IMAGE:9020858), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136907 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136908 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168522 IMAGE:9020899), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000618231 // ENSEMBL // havana:known chromosome:GRCh38:19:110613:111417:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:retained_intron // chr1 // 100 // 88 // 21 // 21 // 0', 'NR_024437 // RefSeq // Homo sapiens uncharacterized LOC728323 (LOC728323), long non-coding RNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XM_006711854 // RefSeq // PREDICTED: Homo sapiens F-box only protein 25-like (LOC101060626), partial mRNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XM_006726377 // RefSeq // PREDICTED: Homo sapiens F-box only protein 25-like (LOC101060626), partial mRNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XR_430662 // RefSeq // PREDICTED: Homo sapiens uncharacterized LOC101927097 (LOC101927097), misc_RNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000279067 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:20:64290385:64303559:1 gene:ENSG00000149656 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000431812 // ENSEMBL // havana:known chromosome:GRCh38:1:485066:489553:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000433444 // ENSEMBL // havana:putative chromosome:GRCh38:2:242122293:242138888:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000436899 // ENSEMBL // havana:known chromosome:GRCh38:6:131910:144885:-1 gene:ENSG00000170590 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000445252 // ENSEMBL // havana:known chromosome:GRCh38:20:64294897:64311371:1 gene:ENSG00000149656 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455207 // ENSEMBL // havana:known chromosome:GRCh38:1:373182:485208:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455464 // ENSEMBL // havana:known chromosome:GRCh38:1:476531:497259:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000456398 // ENSEMBL // havana:known chromosome:GRCh38:2:242088633:242140638:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000601814 // ENSEMBL // havana:known chromosome:GRCh38:1:484832:495476:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// AK298283 // GenBank // Homo sapiens cDNA FLJ60027 complete cds, moderately similar to F-box only protein 25. // chr1 // 100 // 100 // 6 // 6 // 0 /// AL137655 // GenBank // Homo sapiens mRNA; cDNA DKFZp434B2016 (from clone DKFZp434B2016). // chr1 // 100 // 100 // 6 // 6 // 0 /// BC032332 // GenBank // Homo sapiens protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing 2, mRNA (cDNA clone MGC:40288 IMAGE:5169056), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC118988 // GenBank // Homo sapiens chromosome 20 open reading frame 69, mRNA (cDNA clone MGC:141807 IMAGE:40035995), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC122537 // GenBank // Homo sapiens chromosome 20 open reading frame 69, mRNA (cDNA clone MGC:141808 IMAGE:40035996), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC131690 // GenBank // Homo sapiens similar to bA476I15.3 (novel protein similar to septin), mRNA (cDNA clone IMAGE:40119684), partial cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// NM_207366 // RefSeq // Homo sapiens septin 14 (SEPT14), mRNA. // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000388975 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:7:55793544:55862789:-1 gene:ENSG00000154997 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000427373 // ENSEMBL // havana:known chromosome:GRCh38:Y:25378300:25394719:-1 gene:ENSG00000228786 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000431796 // ENSEMBL // havana:known chromosome:GRCh38:2:242088693:242122405:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 60 // 83 // 3 // 5 // 0 /// ENST00000509776 // ENSEMBL // havana:known chromosome:GRCh38:Y:24278681:24291346:1 gene:ENSG00000248792 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000570230 // ENSEMBL // havana:known chromosome:GRCh38:16:90157932:90178344:1 gene:ENSG00000260528 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 67 // 100 // 4 // 6 // 0 /// AK301928 // GenBank // Homo sapiens cDNA FLJ59065 complete cds, moderately similar to Septin-10. // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000413839 // ENSEMBL // havana:known chromosome:GRCh38:7:45816557:45821064:1 gene:ENSG00000226838 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000414688 // ENSEMBL // havana:known chromosome:GRCh38:1:711342:720200:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000419394 // ENSEMBL // havana:known chromosome:GRCh38:1:703685:720194:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000420830 // ENSEMBL // havana:known chromosome:GRCh38:1:243031272:243047869:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000428915 // ENSEMBL // havana:known chromosome:GRCh38:10:38453181:38466176:1 gene:ENSG00000203496 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000439401 // ENSEMBL // havana:known chromosome:GRCh38:3:198228194:198228376:1 gene:ENSG00000226008 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000440200 // ENSEMBL // havana:known chromosome:GRCh38:1:601436:720200:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000441245 // ENSEMBL // havana:known chromosome:GRCh38:1:701936:720150:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 67 // 4 // 4 // 0 /// ENST00000445840 // ENSEMBL // havana:known chromosome:GRCh38:1:485032:485211:-1 gene:ENSG00000224813 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000447954 // ENSEMBL // havana:known chromosome:GRCh38:1:720058:724550:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000450226 // ENSEMBL // havana:known chromosome:GRCh38:1:243038914:243047875:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000453405 // ENSEMBL // havana:known chromosome:GRCh38:2:242122287:242122469:1 gene:ENSG00000244528 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000477740 // ENSEMBL // havana:known chromosome:GRCh38:1:92230:129217:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000508026 // ENSEMBL // havana:known chromosome:GRCh38:8:200385:200562:-1 gene:ENSG00000255464 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000509192 // ENSEMBL // havana:known chromosome:GRCh38:5:181329241:181342213:1 gene:ENSG00000250765 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000513445 // ENSEMBL // havana:known chromosome:GRCh38:4:118640673:118640858:1 gene:ENSG00000251155 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000523795 // ENSEMBL // havana:known chromosome:GRCh38:8:192091:200563:-1 gene:ENSG00000250210 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000529266 // ENSEMBL // havana:known chromosome:GRCh38:11:121279:125784:-1 gene:ENSG00000254468 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000587432 // ENSEMBL // havana:known chromosome:GRCh38:19:191212:195696:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000610542 // ENSEMBL // ensembl:known chromosome:GRCh38:1:120725:133723:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000612088 // ENSEMBL // ensembl:known chromosome:GRCh38:10:38453181:38466176:1 gene:ENSG00000203496 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000612214 // ENSEMBL // havana:known chromosome:GRCh38:19:186371:191429:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000613471 // ENSEMBL // ensembl:known chromosome:GRCh38:1:476738:489710:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000615295 // ENSEMBL // ensembl:known chromosome:GRCh38:5:181329241:181342213:1 gene:ENSG00000250765 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000616585 // ENSEMBL // ensembl:known chromosome:GRCh38:1:711715:724707:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000618096 // ENSEMBL // havana:known chromosome:GRCh38:19:191178:191354:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000618222 // ENSEMBL // ensembl:known chromosome:GRCh38:6:131910:144885:-1 gene:ENSG00000170590 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000622435 // ENSEMBL // havana:known chromosome:GRCh38:2:242088684:242159382:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000622626 // ENSEMBL // ensembl:known chromosome:GRCh38:11:112967:125927:-1 gene:ENSG00000254468 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000007486 // ENSEMBL // cdna:genscan chromosome:GRCh38:2:242089132:242175655:1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000023775 // ENSEMBL // cdna:genscan chromosome:GRCh38:7:45812479:45856081:1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000045845 // ENSEMBL // cdna:genscan chromosome:GRCh38:8:166086:213433:-1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// BC071667 // GenBank HTC // Homo sapiens cDNA clone IMAGE:4384656, **** WARNING: chimeric clone ****. // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000053 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000055 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000063 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT000064 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000065 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000086 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000097 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 67 // 4 // 4 // 0 /// NONHSAT000098 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT010578 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT012829 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT017180 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT060112 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078034 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078035 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078039 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078040 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078041 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT081035 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT081036 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT094494 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT094497 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT098010 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT105956 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT105968 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT120472 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT124571 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00001800-XLOC_l2_001331 // Broad TUCP // linc-TP53BP2-4 chr1:-:224133091-224222680 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00001926-XLOC_l2_000004 // Broad TUCP // linc-OR4F16-1 chr1:+:329783-334271 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00001927-XLOC_l2_000004 // Broad TUCP // linc-OR4F16-1 chr1:+:334139-342806 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002370-XLOC_l2_000720 // Broad TUCP // linc-ZNF692-5 chr1:-:92229-129217 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00002386-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:637315-655530 // chr1 // 100 // 67 // 4 // 4 // 0 /// TCONS_l2_00002387-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:639064-655574 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002388-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:646721-655580 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002389-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:655437-659930 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00002812-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243194573-243211171 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00003949-XLOC_l2_001561 // Broad TUCP // linc-BMS1-9 chr10:+:38742108-38755311 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00003950-XLOC_l2_001561 // Broad TUCP // linc-BMS1-9 chr10:+:38742265-38764837 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014349-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030831-243101574 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014350-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030855-243102147 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014351-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030868-243101569 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014352-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030886-243064759 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014354-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030931-243067562 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014355-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030941-243102157 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014357-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243037045-243101538 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014358-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243058329-243064628 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015637-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030783-243082789 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015638-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243065243 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015639-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015640-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015641-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015643-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243064443-243081039 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00016828-XLOC_l2_008724 // Broad TUCP // linc-HNF1B-4 chr20:+:62921737-62934707 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00020055-XLOC_l2_010084 // Broad TUCP // linc-MCMBP-2 chr3:+:197937115-197955676 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025304-XLOC_l2_012836 // Broad TUCP // linc-PDCD2-1 chr6:-:131909-144885 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025849-XLOC_l2_013383 // Broad TUCP // linc-IGFBP1-1 chr7:+:45831387-45863181 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025850-XLOC_l2_013383 // Broad TUCP // linc-IGFBP1-1 chr7:+:45836951-45863174 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000437691 // ENSEMBL // havana:known chromosome:GRCh38:1:243047737:243052252:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000447236 // ENSEMBL // havana:known chromosome:GRCh38:7:56360362:56360541:-1 gene:ENSG00000231299 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000453576 // ENSEMBL // havana:known chromosome:GRCh38:1:129081:133566:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000611754 // ENSEMBL // ensembl:known chromosome:GRCh38:Y:25378671:25391610:-1 gene:ENSG00000228786 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000617978 // ENSEMBL // havana:known chromosome:GRCh38:1:227980051:227980227:1 gene:ENSG00000274886 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000621799 // ENSEMBL // ensembl:known chromosome:GRCh38:16:90173217:90186204:1 gene:ENSG00000260528 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT000022 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT010579 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT010580 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT120743 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 50 // 100 // 3 // 6 // 0 /// NONHSAT139746 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT144650 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT144655 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002372-XLOC_l2_000720 // Broad TUCP // linc-ZNF692-5 chr1:-:129080-133566 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002813-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243202215-243211826 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002814-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243211038-243215554 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00010440-XLOC_l2_005352 // Broad TUCP // linc-RBM11-5 chr16:+:90244124-90289080 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00031062-XLOC_l2_015962 // Broad TUCP // linc-BPY2B-4 chrY:-:27524446-27540866 // chr1 // 67 // 100 // 4 // 6 // 0', 'NM_001005221 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 29 (OR4F29), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005224 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 3 (OR4F3), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005277 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 16 (OR4F16), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005504 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 21 (OR4F21), mRNA. // chr1 // 89 // 100 // 32 // 36 // 0 /// ENST00000320901 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:8:166049:167043:-1 gene:ENSG00000176269 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 89 // 100 // 32 // 36 // 0 /// ENST00000332831 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:685716:686654:-1 gene:ENSG00000273547 gene_biotype:protein_coding transcript_biotype:protein_coding gene:ENSG00000185097 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000402444 // ENSEMBL // havana:known chromosome:GRCh38:6:170639606:170640536:1 gene:ENSG00000217874 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 78 // 100 // 28 // 36 // 0 /// ENST00000405102 // ENSEMBL // havana:known chromosome:GRCh38:6:105919:106856:-1 gene:ENSG00000220212 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 81 // 100 // 29 // 36 // 0 /// ENST00000424047 // ENSEMBL // havana:known chromosome:GRCh38:11:86649:87586:-1 gene:ENSG00000224777 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 78 // 100 // 28 // 36 // 0 /// ENST00000426406 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:450740:451678:-1 gene:ENSG00000278566 gene_biotype:protein_coding transcript_biotype:protein_coding gene:ENSG00000235249 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000456475 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:5:181367268:181368262:1 gene:ENSG00000230178 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000559128 // ENSEMBL // havana:known chromosome:GRCh38:15:101875964:101876901:1 gene:ENSG00000257109 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 100 // 30 // 36 // 0 /// BC137547 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169170 IMAGE:9021547), complete cds. // chr1 // 100 // 100 // 36 // 36 // 0 /// BC137568 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169191 IMAGE:9021568), complete cds. // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000589943 // ENSEMBL // havana:known chromosome:GRCh38:19:156279:157215:-1 gene:ENSG00000266971 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 72 // 100 // 26 // 36 // 0 /// GENSCAN00000011446 // ENSEMBL // cdna:genscan chromosome:GRCh38:5:181367527:181368225:1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000017675 // ENSEMBL // cdna:genscan chromosome:GRCh38:1:685716:686414:-1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000017679 // ENSEMBL // cdna:genscan chromosome:GRCh38:1:450740:451438:-1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000045845 // ENSEMBL // cdna:genscan chromosome:GRCh38:8:166086:213433:-1 transcript_biotype:protein_coding // chr1 // 87 // 83 // 26 // 30 // 0 /// NONHSAT051700 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 30 // 36 // 0 /// NONHSAT051701 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 30 // 36 // 0 /// NONHSAT105966 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 81 // 100 // 29 // 36 // 0 /// NONHSAT060109 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 72 // 100 // 26 // 36 // 0'], 'category': ['main', 'main', 'main', 'main', 'main']}\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": "4fcc4b77",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f21f8050",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:12.270709Z",
     "iopub.status.busy": "2025-03-25T06:18:12.270585Z",
     "iopub.status.idle": "2025-03-25T06:18:16.558743Z",
     "shell.execute_reply": "2025-03-25T06:18:16.558183Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data after mapping (first 5 genes):\n",
      "      GSM6927801  GSM6927802  GSM6927803  GSM6927804  GSM6927805  GSM6927806  \\\n",
      "Gene                                                                           \n",
      "A-     34.693598   34.346650   34.622698   34.094624   34.625311   35.018075   \n",
      "A-3-    0.626052    0.622455    0.656334    0.648091    0.654457    0.655447   \n",
      "A-52    1.692371    1.723843    1.715014    1.696943    1.710114    1.713357   \n",
      "A-E     1.093967    1.113555    1.112054    1.156460    1.138387    1.090821   \n",
      "A-I     2.139723    2.086243    2.113866    2.037589    2.067522    2.107221   \n",
      "\n",
      "      GSM6927807  GSM6927808  GSM6927809  GSM6927810  ...  GSM6927833  \\\n",
      "Gene                                                  ...               \n",
      "A-     34.909643   34.965002   35.091768   35.054553  ...   34.523325   \n",
      "A-3-    0.631293    0.626834    0.617430    0.629572  ...    0.633328   \n",
      "A-52    1.698214    1.710671    1.688657    1.681600  ...    1.687471   \n",
      "A-E     1.120468    1.128437    1.108099    1.174397  ...    1.140594   \n",
      "A-I     2.129579    2.106680    2.131133    2.067500  ...    2.103621   \n",
      "\n",
      "      GSM6927834  GSM6927835  GSM6927836  GSM6927837  GSM6927838  GSM6927839  \\\n",
      "Gene                                                                           \n",
      "A-     34.764841   34.923276   34.775315   34.397705   35.253824   34.273728   \n",
      "A-3-    0.624100    0.627152    0.630459    0.635709    0.658340    0.654335   \n",
      "A-52    1.715857    1.715814    1.724786    1.732629    1.722329    1.695771   \n",
      "A-E     1.118930    1.107913    1.103643    1.156933    1.109727    1.155707   \n",
      "A-I     2.085986    2.122440    2.128236    2.081015    2.075629    2.098936   \n",
      "\n",
      "      GSM6927840  GSM6927841  GSM6927842  \n",
      "Gene                                      \n",
      "A-     35.206021   34.694656   34.867413  \n",
      "A-3-    0.645130    0.646839    0.631524  \n",
      "A-52    1.717986    1.733300    1.734014  \n",
      "A-E     1.129266    1.089195    1.100671  \n",
      "A-I     2.092212    2.102201    2.087001  \n",
      "\n",
      "[5 rows x 42 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222616.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the columns for gene identifier and gene symbol mapping\n",
    "# The 'ID' column in gene_annotation matches the IDs in gene_data\n",
    "# The 'gene_assignment' column contains gene symbols that need to be extracted\n",
    "\n",
    "# 2. Create the gene mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n",
    "\n",
    "# 3. Convert probe-level measurements to gene expression data by applying the gene mapping\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Preview the first few rows of the gene expression data after mapping\n",
    "print(\"Gene expression data after mapping (first 5 genes):\")\n",
    "print(gene_data.head(5))\n",
    "\n",
    "# Save the gene expression data\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\"Gene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6f99c0",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bfcca62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:18:16.560734Z",
     "iopub.status.busy": "2025-03-25T06:18:16.560599Z",
     "iopub.status.idle": "2025-03-25T06:18:29.856558Z",
     "shell.execute_reply": "2025-03-25T06:18:29.856213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222616.csv\n",
      "Clinical data shape: (3, 43)\n",
      "Sample characteristics dictionary:\n",
      "{0: ['cell line: HL-60'], 1: ['cell type: Acute myeloid leukaemia'], 2: ['treatment: untreated', 'treatment: GSK343', 'treatment: HKMTI-1-005', 'treatment: ATRA', 'treatment: GSK343+ATRA', 'treatment: HKMTI-1-005+ATRA', 'treatment: EZH2_KD_shEZH2#1+ATRA', 'treatment: EZH2_KD_shEZH2#1', 'treatment: EZH2_KD_shEZH2#2+ATRA', 'treatment: EZH2_KD_shEZH2#2', 'treatment: non-targeting_control_Scr+ATRA', 'treatment: non-targeting_control_Scr', 'treatment: WT+ATRA', 'treatment: WT']}\n",
      "Clinical data preview:\n",
      "{'GSM6927801': [0.0], 'GSM6927802': [0.0], 'GSM6927803': [0.0], 'GSM6927804': [1.0], 'GSM6927805': [1.0], 'GSM6927806': [1.0], 'GSM6927807': [0.0], 'GSM6927808': [0.0], 'GSM6927809': [0.0], 'GSM6927810': [1.0], 'GSM6927811': [1.0], 'GSM6927812': [1.0], 'GSM6927813': [1.0], 'GSM6927814': [0.0], 'GSM6927815': [1.0], 'GSM6927816': [0.0], 'GSM6927817': [1.0], 'GSM6927818': [0.0], 'GSM6927819': [1.0], 'GSM6927820': [0.0], 'GSM6927821': [1.0], 'GSM6927822': [0.0], 'GSM6927823': [1.0], 'GSM6927824': [1.0], 'GSM6927825': [1.0], 'GSM6927826': [0.0], 'GSM6927827': [0.0], 'GSM6927828': [0.0], 'GSM6927829': [1.0], 'GSM6927830': [1.0], 'GSM6927831': [0.0], 'GSM6927832': [0.0], 'GSM6927833': [1.0], 'GSM6927834': [1.0], 'GSM6927835': [0.0], 'GSM6927836': [0.0], 'GSM6927837': [1.0], 'GSM6927838': [1.0], 'GSM6927839': [1.0], 'GSM6927840': [0.0], 'GSM6927841': [0.0], 'GSM6927842': [0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE222616.csv\n",
      "Linked data shape: (42, 24230)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (42, 24230)\n",
      "For the feature 'Acute_Myeloid_Leukemia', the least common label is '0.0' with 21 occurrences. This represents 50.00% of the dataset.\n",
      "The distribution of the feature 'Acute_Myeloid_Leukemia' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed dataset saved to ../../output/preprocess/Acute_Myeloid_Leukemia/GSE222616.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Check what's in the clinical data\n",
    "print(\"Clinical data shape:\", clinical_data.shape)\n",
    "print(\"Sample characteristics dictionary:\")\n",
    "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "print(sample_characteristics_dict)\n",
    "\n",
    "# This dataset contains AML cell lines with different treatments rather than a case-control study\n",
    "# We can use the treatment information in row 2 as our trait of interest instead\n",
    "trait_row = 2  # The row containing treatment information\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert treatment to binary (1 = treated with ATRA or combinations, 0 = untreated or control)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part (after colon if present)\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Define groups: 1 = treated with ATRA (alone or combination), 0 = others\n",
    "    if 'ATRA' in value:\n",
    "        return 1\n",
    "    elif 'untreated' in value or 'WT' in value or 'control' in value or 'Scr' in value:\n",
    "        return 0\n",
    "    else:\n",
    "        # Other treatments (GSK343, HKMTI-1-005, EZH2_KD without ATRA)\n",
    "        return 0  # Classifying all non-ATRA treatments as 0\n",
    "\n",
    "# Extract clinical features with the corrected trait definition\n",
    "selected_clinical_data = 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=None,\n",
    "    convert_age=None,\n",
    "    gender_row=None,\n",
    "    convert_gender=None\n",
    ")\n",
    "\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_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. Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait and some demographic features are severely biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct quality check and save the cohort information\n",
    "note = \"Dataset contains AML cell lines with different treatments. Trait was redefined to compare ATRA treatment (1) vs other treatments/controls (0).\"\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=note\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it\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\"Processed dataset saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset not usable due to bias in trait distribution. Data not saved.\")"
   ]
  }
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