code/Essential_Thrombocythemia/GSE103237.ipynb

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   "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 = \"Essential_Thrombocythemia\"\n",
    "cohort = \"GSE103237\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Essential_Thrombocythemia\"\n",
    "in_cohort_dir = \"../../input/GEO/Essential_Thrombocythemia/GSE103237\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Essential_Thrombocythemia/GSE103237.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE103237.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE103237.csv\"\n",
    "json_path = \"../../output/preprocess/Essential_Thrombocythemia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf032c1",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
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   "id": "08113114",
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     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene and miRNA expression profiles in Polycythemia Vera and Essential Thrombocythemia according to CALR and JAK2 mutations [GEP]\"\n",
      "!Series_summary\t\"Polycythemia vera (PV) and essential thrombocythemia (ET) are Philadelphia-negative myeloproliferative neoplasms (MPNs) characterized by erythrocytosis and thrombocytosis, respectively. Approximately 95% of PV and 50–70% of ET patients harbour the V617F mutation in the exon 14 of JAK2 gene, while about 20-30% of ET patients carry CALRins5 or CALRdel52 mutations. These ET CARL-mutated subjects show higher platelet count and lower thrombotic risk compared to JAK2-mutated patients. Here we showed that CALR-mutated and JAK2V617F-positive CD34+ cells have different gene and miRNA expression profiles. Indeed, we highlighted several pathways differentially activated between JAK2V617F- and CALR-mutated progenitors, i.e. mTOR, MAPK/PI3K and MYC pathways. Furthermore, we unveiled that the expression of several genes involved in DNA repair, chromatin remodelling, splicing and chromatid cohesion are decreased in CALR-mutated cells. According to the low risk of thrombosis in CALR-mutated patients, we also found the down-regulation of several genes involved in thrombin signalling and platelet activation.   As a whole, these data support the model in which CALR-mutated ET could be considered as a distinct disease entity from JAK2V617F-positive MPNs and may provide the molecular basis supporting the different clinical features of these patients.\"\n",
      "!Series_overall_design\t\"Gene expression profile (GEP) and miRNA expression profile (miEP) were performed starting from the same total RNA of CD34+ cells from 50 MPN patients (1 replicate for each sample). In particular, GEP and miEP were performed on 26 PV and 24 ET (n=17 JAK2V617F-positive ET, n=7 CALR-mutated ET). In addition, 15 bone marrow (BM) samples collected from normal donors were included in the study (GSE53482).  These re-analyzed samples have been included in this series for completeness.  This series includes only the GEP samples.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['supplier: Vannucchi', 'supplier: Cazzola'], 1: ['Sex: M', 'Sex: F', 'Sex: not provided'], 2: ['condition: myeloproliferative neoplasm (MPN)', 'condition: Control (CTR)'], 3: ['disease: ET', 'disease: PV', 'disease: healthy control'], 4: ['jak2v617f: neg', 'jak2v617f: pos'], 5: ['mpl-mutated: neg', 'mpl-mutated: ND', 'tissue: Bone marrow'], 6: ['calr-mutated: pos', 'calr-mutated: neg', 'calr-mutated: ND', 'cell marker: CD34+'], 7: ['calr mutation: L367FS52 (tipo I)', 'calr mutation: 385insTTGTC (tipo II)', 'calr mutation: E386del AGGA', 'calr mutation: K391fs51 (tipo II)', 'calr mutation: del52 (tipo I)', 'gene mutation: V617F', nan], 8: ['gene mutation: CALR', 'tissue: Bone marrow', nan], 9: ['tissue: Bone marrow', 'cell marker: CD34+', nan], 10: ['cell marker: CD34+', nan]}\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": "a9c66187",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8832c85b",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of extracted clinical features:\n",
      "{3: [0.0, nan], 1: [nan, 0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE103237.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression profiles (GEP) from CD34+ cells\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Trait - Essential Thrombocythemia\n",
    "# Looking at the sample characteristics, disease status is in row 3\n",
    "trait_row = 3\n",
    "\n",
    "# Age - Not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender - Available in row 1\n",
    "gender_row = 1\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (0: control, 1: Essential Thrombocythemia)\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if value.lower() == 'et' or 'thrombocythemia' in value.lower():\n",
    "        return 1  # Essential Thrombocythemia\n",
    "    elif 'control' in value.lower() or 'healthy' in value.lower() or 'ctr' in value.lower():\n",
    "        return 0  # Control\n",
    "    elif value.lower() == 'pv' or 'polycythemia' in value.lower():\n",
    "        return None  # Not relevant for this trait\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous\"\"\"\n",
    "    # Age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0: female, 1: male)\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if value.lower() == 'm':\n",
    "        return 1  # Male\n",
    "    elif value.lower() == 'f':\n",
    "        return 0  # Female\n",
    "    else:\n",
    "        return None  # Unknown or not provided\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial filtering for usability\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 (if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    try:\n",
    "        # Create a DataFrame from the sample characteristics dictionary\n",
    "        # Since we don't have the clinical_data.csv file, we'll create the DataFrame \n",
    "        # based on the Sample Characteristics Dictionary from the previous step\n",
    "        \n",
    "        # Sample characteristics dictionary from previous step\n",
    "        sample_chars = {\n",
    "            0: ['supplier: Vannucchi', 'supplier: Cazzola'], \n",
    "            1: ['Sex: M', 'Sex: F', 'Sex: not provided'], \n",
    "            2: ['condition: myeloproliferative neoplasm (MPN)', 'condition: Control (CTR)'], \n",
    "            3: ['disease: ET', 'disease: PV', 'disease: healthy control'], \n",
    "            4: ['jak2v617f: neg', 'jak2v617f: pos'], \n",
    "            5: ['mpl-mutated: neg', 'mpl-mutated: ND', 'tissue: Bone marrow'], \n",
    "            6: ['calr-mutated: pos', 'calr-mutated: neg', 'calr-mutated: ND', 'cell marker: CD34+'], \n",
    "            7: ['calr mutation: L367FS52 (tipo I)', 'calr mutation: 385insTTGTC (tipo II)', 'calr mutation: E386del AGGA', \n",
    "                'calr mutation: K391fs51 (tipo II)', 'calr mutation: del52 (tipo I)', 'gene mutation: V617F', None], \n",
    "            8: ['gene mutation: CALR', 'tissue: Bone marrow', None], \n",
    "            9: ['tissue: Bone marrow', 'cell marker: CD34+', None], \n",
    "            10: ['cell marker: CD34+', None]\n",
    "        }\n",
    "        \n",
    "        # Convert the dictionary to a DataFrame suitable for geo_select_clinical_features\n",
    "        # We need to create a DataFrame where rows represent features and columns represent samples\n",
    "        # First, let's identify all unique values for each characteristic row\n",
    "        \n",
    "        # For demonstration purposes, we'll create a mock clinical DataFrame\n",
    "        # This is a placeholder since we don't have actual sample data\n",
    "        # In a real scenario, you would need the actual sample data to populate this correctly\n",
    "        \n",
    "        # Let's assume we have samples and create a placeholder DataFrame\n",
    "        mock_samples = ['Sample_1', 'Sample_2', 'Sample_3', 'Sample_4', 'Sample_5']\n",
    "        mock_data = {}\n",
    "        \n",
    "        for i in range(len(mock_samples)):\n",
    "            # Assign random values from each characteristic row for demonstration\n",
    "            sample_name = mock_samples[i]\n",
    "            mock_data[sample_name] = {}\n",
    "            \n",
    "            # For trait row (disease status)\n",
    "            if i < 2:  # First two samples are ET\n",
    "                mock_data[sample_name][trait_row] = 'disease: ET'\n",
    "            elif i < 4:  # Next two are healthy controls\n",
    "                mock_data[sample_name][trait_row] = 'disease: healthy control'\n",
    "            else:  # Last one is PV (will be converted to None for this trait)\n",
    "                mock_data[sample_name][trait_row] = 'disease: PV'\n",
    "            \n",
    "            # For gender row\n",
    "            if i % 2 == 0:  # Even indices are male\n",
    "                mock_data[sample_name][gender_row] = 'Sex: M'\n",
    "            else:  # Odd indices are female\n",
    "                mock_data[sample_name][gender_row] = 'Sex: F'\n",
    "        \n",
    "        # Convert the mock data to a DataFrame\n",
    "        clinical_data = pd.DataFrame(mock_data).T\n",
    "        \n",
    "        # Extract clinical features\n",
    "        selected_clinical_df = geo_select_clinical_features(\n",
    "            clinical_df=clinical_data,\n",
    "            trait=trait,\n",
    "            trait_row=trait_row,\n",
    "            convert_trait=convert_trait,\n",
    "            age_row=age_row,\n",
    "            convert_age=convert_age,\n",
    "            gender_row=gender_row,\n",
    "            convert_gender=convert_gender\n",
    "        )\n",
    "        \n",
    "        # Preview the extracted data\n",
    "        print(\"Preview of extracted clinical features:\")\n",
    "        print(preview_df(selected_clinical_df))\n",
    "        \n",
    "        # Save to CSV\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error in clinical feature extraction: {str(e)}\")\n",
    "        # Even if extraction fails, we've already saved the initial metadata\n",
    "else:\n",
    "    print(\"Trait data is not available. Skipping clinical feature extraction.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe0df415",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene/probe identifiers:\n",
      "Index(['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at',\n",
      "       '11715104_s_at', '11715105_at', '11715106_x_at', '11715107_s_at',\n",
      "       '11715108_x_at', '11715109_at', '11715110_at', '11715111_s_at',\n",
      "       '11715112_at', '11715113_x_at', '11715114_x_at', '11715115_s_at',\n",
      "       '11715116_s_at', '11715117_x_at', '11715118_s_at', '11715119_s_at'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths again to access the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_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) for future observation\n",
    "print(\"First 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10286f9c",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
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   "id": "8f71fe96",
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   "outputs": [],
   "source": [
    "# These identifiers like '11715100_at' are not human gene symbols but probe IDs from a microarray platform.\n",
    "# They need to be mapped to human gene symbols for biological interpretation.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6b0cf9",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at', '11715104_s_at'], 'GeneChip Array': ['Human Genome HG-U219 Array', 'Human Genome HG-U219 Array', 'Human Genome HG-U219 Array', 'Human Genome HG-U219 Array', 'Human Genome HG-U219 Array'], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['20-Aug-10', '20-Aug-10', '20-Aug-10', '20-Aug-10', '20-Aug-10'], 'Sequence Type': ['Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database'], 'Transcript ID(Array Design)': ['g21264570', 'g21264570', 'g21264570', 'g22748780', 'g30039713'], 'Target Description': ['g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g22748780 /TID=g22748780 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g22748780 /REP_ORG=Homo sapiens', 'g30039713 /TID=g30039713 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g30039713 /REP_ORG=Homo sapiens'], 'Representative Public ID': ['g21264570', 'g21264570', 'g21264570', 'g22748780', 'g30039713'], 'Archival UniGene Cluster': ['---', '---', '---', '---', '---'], 'UniGene ID': ['Hs.247813', 'Hs.247813', 'Hs.247813', 'Hs.465643', 'Hs.352515'], 'Genome Version': ['February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)'], 'Alignments': ['chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr19:4639529-5145579 (+) // 48.53 // p13.3', 'chr17:72920369-72929640 (+) // 100.0 // q25.1'], 'Gene Title': ['histone cluster 1, H3g', 'histone cluster 1, H3g', 'histone cluster 1, H3g', 'tumor necrosis factor, alpha-induced protein 8-like 1', 'otopetrin 2'], 'Gene Symbol': ['HIST1H3G', 'HIST1H3G', 'HIST1H3G', 'TNFAIP8L1', 'OTOP2'], 'Chromosomal Location': ['chr6p21.3', 'chr6p21.3', 'chr6p21.3', 'chr19p13.3', 'chr17q25.1'], 'GB_LIST': ['NM_003534', 'NM_003534', 'NM_003534', 'NM_001167942,NM_152362', 'NM_178160'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Unigene Cluster Type': ['full length', 'full length', 'full length', 'full length', 'full length'], 'Ensembl': ['---', 'ENSG00000178458', '---', 'ENSG00000185361', 'ENSG00000183034'], 'Entrez Gene': ['8355', '8355', '8355', '126282', '92736'], 'SwissProt': ['P68431', 'P68431', 'P68431', 'Q8WVP5', 'Q7RTS6'], 'EC': ['---', '---', '---', '---', '---'], 'OMIM': ['602815', '602815', '602815', '---', '607827'], 'RefSeq Protein ID': ['NP_003525', 'NP_003525', 'NP_003525', 'NP_001161414 /// NP_689575', 'NP_835454'], 'RefSeq Transcript ID': ['NM_003534', 'NM_003534', 'NM_003534', 'NM_001167942 /// NM_152362', 'NM_178160'], 'FlyBase': ['---', '---', '---', '---', '---'], 'AGI': ['---', '---', '---', '---', '---'], 'WormBase': ['---', '---', '---', '---', '---'], 'MGI Name': ['---', '---', '---', '---', '---'], 'RGD Name': ['---', '---', '---', '---', '---'], 'SGD accession number': ['---', '---', '---', '---', '---'], 'Gene Ontology Biological Process': ['0006334 // nucleosome assembly // inferred from electronic annotation', '0006334 // nucleosome assembly // inferred from electronic annotation', '0006334 // nucleosome assembly // inferred from electronic annotation', '---', '---'], 'Gene Ontology Cellular Component': ['0000786 // nucleosome // inferred from electronic annotation /// 0005634 // nucleus // inferred from electronic annotation /// 0005694 // chromosome // inferred from electronic annotation', '0000786 // nucleosome // inferred from electronic annotation /// 0005634 // nucleus // inferred from electronic annotation /// 0005694 // chromosome // inferred from electronic annotation', '0000786 // nucleosome // inferred from electronic annotation /// 0005634 // nucleus // inferred from electronic annotation /// 0005694 // chromosome // inferred from electronic annotation', '---', '0016020 // membrane // inferred from electronic annotation /// 0016021 // integral to membrane // inferred from electronic annotation'], 'Gene Ontology Molecular Function': ['0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction', '0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction', '0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction', '---', '---'], 'Pathway': ['---', '---', '---', '---', '---'], 'InterPro': ['---', '---', '---', '---', 'IPR004878 // Protein of unknown function DUF270 // 1.0E-6 /// IPR004878 // Protein of unknown function DUF270 // 1.0E-13'], 'Trans Membrane': ['---', '---', '---', '---', 'NP_835454.1 // span:30-52,62-81,101-120,135-157,240-262,288-310,327-349,369-391,496-515,525-547 // numtm:10'], 'QTL': ['---', '---', '---', '---', '---'], 'Annotation Description': ['This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 1 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 2 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 1 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 5 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 3 transcripts. // false // Matching Probes // A'], 'Annotation Transcript Cluster': ['NM_003534(11)', 'BC079835(11),NM_003534(11)', 'NM_003534(11)', 'BC017672(11),BC044250(9),ENST00000327473(11),NM_001167942(11),NM_152362(11)', 'ENST00000331427(11),ENST00000426069(11),NM_178160(11)'], 'Transcript Assignments': ['NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // ---', 'BC079835 // Homo sapiens histone cluster 1, H3g, mRNA (cDNA clone IMAGE:5935692). // gb_htc // 11 // --- /// ENST00000321285 // cdna:known chromosome:GRCh37:6:26271202:26271612:-1 gene:ENSG00000178458 // ensembl // 11 // --- /// GENSCAN00000044911 // cdna:Genscan chromosome:GRCh37:6:26271202:26271612:-1 // ensembl // 11 // --- /// NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // ---', 'NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // ---', 'BC017672 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1, mRNA (cDNA clone MGC:17791 IMAGE:3885999), complete cds. // gb // 11 // --- /// BC044250 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1, mRNA (cDNA clone IMAGE:5784807). // gb // 9 // --- /// ENST00000327473 // cdna:known chromosome:GRCh37:19:4639530:4653952:1 gene:ENSG00000185361 // ensembl // 11 // --- /// NM_001167942 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1 (TNFAIP8L1), transcript variant 1, mRNA. // refseq // 11 // --- /// NM_152362 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1 (TNFAIP8L1), transcript variant 2, mRNA. // refseq // 11 // ---', 'ENST00000331427 // cdna:known chromosome:GRCh37:17:72920370:72929640:1 gene:ENSG00000183034 // ensembl // 11 // --- /// ENST00000426069 // cdna:known chromosome:GRCh37:17:72920370:72929640:1 gene:ENSG00000183034 // ensembl // 11 // --- /// NM_178160 // Homo sapiens otopetrin 2 (OTOP2), mRNA. // refseq // 11 // ---'], 'Annotation Notes': ['BC079835 // gb_htc // 6 // Cross Hyb Matching Probes', '---', 'GENSCAN00000044911 // ensembl // 4 // Cross Hyb Matching Probes /// ENST00000321285 // ensembl // 4 // Cross Hyb Matching Probes /// BC079835 // gb_htc // 7 // Cross Hyb Matching Probes', '---', 'GENSCAN00000031612 // ensembl // 8 // Cross Hyb Matching Probes']}\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": "54d01de4",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cce8db1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:03.626596Z",
     "iopub.status.busy": "2025-03-25T05:15:03.626475Z",
     "iopub.status.idle": "2025-03-25T05:15:03.933180Z",
     "shell.execute_reply": "2025-03-25T05:15:03.932642Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data shape after mapping: (19521, 65)\n",
      "Sample of gene symbols after mapping:\n",
      "['A1BG', 'A1CF', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1']\n"
     ]
    }
   ],
   "source": [
    "# 1. Observe the gene identifiers in gene expression data and gene annotation data\n",
    "# From previous steps, we see that gene expression data uses IDs like '11715100_at'\n",
    "# The gene annotation dictionary has 'ID' column that matches these identifiers\n",
    "# and 'Gene Symbol' column that contains actual gene symbols like 'HIST1H3G'\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the relevant columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, \"ID\", \"Gene Symbol\")\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print the shape of the resulting gene expression dataframe and a few gene symbols\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"Sample of gene symbols after mapping:\")\n",
    "print(list(gene_data.index[:10]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7523122f",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "42430c52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:15:03.934603Z",
     "iopub.status.busy": "2025-03-25T05:15:03.934485Z",
     "iopub.status.idle": "2025-03-25T05:15:14.119716Z",
     "shell.execute_reply": "2025-03-25T05:15:14.119232Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n",
      "Gene data shape after normalization: (19298, 65)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE103237.csv\n",
      "Loading the original clinical data...\n",
      "Extracting clinical features...\n",
      "Clinical data preview:\n",
      "{'GSM2758679': [1.0, 1.0], 'GSM2758680': [1.0, 0.0], 'GSM2758681': [1.0, 1.0], 'GSM2758682': [1.0, 1.0], 'GSM2758683': [1.0, 1.0], 'GSM2758684': [1.0, 1.0], 'GSM2758685': [1.0, 1.0], 'GSM2758686': [1.0, 0.0], 'GSM2758687': [1.0, 0.0], 'GSM2758688': [1.0, 0.0], 'GSM2758689': [1.0, 0.0], 'GSM2758690': [1.0, 1.0], 'GSM2758691': [1.0, 0.0], 'GSM2758692': [1.0, 1.0], 'GSM2758693': [1.0, 0.0], 'GSM2758694': [1.0, 1.0], 'GSM2758695': [1.0, 1.0], 'GSM2758696': [1.0, 0.0], 'GSM2758697': [1.0, 0.0], 'GSM2758698': [1.0, 0.0], 'GSM2758699': [1.0, 0.0], 'GSM2758700': [1.0, 0.0], 'GSM2758701': [1.0, 0.0], 'GSM2758702': [1.0, 1.0], 'GSM2758703': [nan, 0.0], 'GSM2758704': [nan, 0.0], 'GSM2758705': [nan, 1.0], 'GSM2758706': [nan, 1.0], 'GSM2758707': [nan, 1.0], 'GSM2758708': [nan, 1.0], 'GSM2758709': [nan, 0.0], 'GSM2758710': [nan, 1.0], 'GSM2758711': [nan, 1.0], 'GSM2758712': [nan, 1.0], 'GSM2758713': [nan, 0.0], 'GSM2758714': [nan, 1.0], 'GSM2758715': [nan, 1.0], 'GSM2758716': [nan, 1.0], 'GSM2758717': [nan, 0.0], 'GSM2758718': [nan, 1.0], 'GSM2758719': [nan, 0.0], 'GSM2758720': [nan, 0.0], 'GSM2758721': [nan, 0.0], 'GSM2758722': [nan, 0.0], 'GSM2758723': [nan, 1.0], 'GSM2758724': [nan, 1.0], 'GSM2758725': [nan, 0.0], 'GSM2758726': [nan, 1.0], 'GSM2758727': [nan, 1.0], 'GSM2758728': [nan, 1.0], 'GSM2758729': [0.0, nan], 'GSM2758730': [0.0, nan], 'GSM2758731': [0.0, nan], 'GSM2758732': [0.0, nan], 'GSM2758733': [0.0, nan], 'GSM2758734': [0.0, nan], 'GSM2758735': [0.0, nan], 'GSM2758736': [0.0, nan], 'GSM2758737': [0.0, nan], 'GSM2758738': [0.0, nan], 'GSM2758739': [0.0, nan], 'GSM2758740': [0.0, nan], 'GSM2758741': [0.0, nan], 'GSM2758742': [0.0, nan], 'GSM2758743': [0.0, nan]}\n",
      "Clinical data saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE103237.csv\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (65, 19300)\n",
      "Handling missing values...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (39, 19300)\n",
      "Checking for bias in trait distribution...\n",
      "For the feature 'Essential_Thrombocythemia', the least common label is '0.0' with 15 occurrences. This represents 38.46% of the dataset.\n",
      "The distribution of the feature 'Essential_Thrombocythemia' in this dataset is fine.\n",
      "\n",
      "For the feature 'Gender', the least common label is '1.0' with 11 occurrences. This represents 28.21% 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/Essential_Thrombocythemia/cohort_info.json\n",
      "Dataset usability: True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Essential_Thrombocythemia/GSE103237.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols...\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to a CSV file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "print(\"Loading the original clinical data...\")\n",
    "# Get the matrix file again to ensure we have the proper data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "print(\"Extracting clinical features...\")\n",
    "# Use the clinical_data obtained directly from the matrix file\n",
    "selected_clinical_df = geo_select_clinical_features(\n",
    "    clinical_df=clinical_data,\n",
    "    trait=trait,\n",
    "    trait_row=trait_row,\n",
    "    convert_trait=convert_trait,\n",
    "    age_row=age_row,\n",
    "    convert_age=convert_age,\n",
    "    gender_row=gender_row,\n",
    "    convert_gender=convert_gender\n",
    ")\n",
    "\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_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",
    "selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Link clinical and genetic data using the normalized gene data\n",
    "print(\"Linking clinical and genetic data...\")\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "print(\"Handling missing values...\")\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check if trait is biased\n",
    "print(\"Checking for bias in trait distribution...\")\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Final validation\n",
    "note = \"Dataset contains gene expression data from patients with Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (PMF).\"\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "print(f\"Dataset usability: {is_usable}\")\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 is not usable for trait-gene association studies due to bias or other issues.\")"
   ]
  }
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