{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0eec103d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:55.292127Z", "iopub.status.busy": "2025-03-25T05:14:55.291833Z", "iopub.status.idle": "2025-03-25T05:14:55.485141Z", "shell.execute_reply": "2025-03-25T05:14:55.484699Z" } }, "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 = \"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" ] }, { "cell_type": "code", "execution_count": 2, "id": "08113114", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:55.486748Z", "iopub.status.busy": "2025-03-25T05:14:55.486600Z", "iopub.status.idle": "2025-03-25T05:14:55.666177Z", "shell.execute_reply": "2025-03-25T05:14:55.665737Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "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", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:55.667469Z", "iopub.status.busy": "2025-03-25T05:14:55.667354Z", "iopub.status.idle": "2025-03-25T05:14:55.678350Z", "shell.execute_reply": "2025-03-25T05:14:55.678010Z" } }, "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" ] }, { "cell_type": "code", "execution_count": 4, "id": "2eb90b19", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:55.679462Z", "iopub.status.busy": "2025-03-25T05:14:55.679353Z", "iopub.status.idle": "2025-03-25T05:14:55.933297Z", "shell.execute_reply": "2025-03-25T05:14:55.932809Z" } }, "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" ] }, { "cell_type": "code", "execution_count": 5, "id": "8f71fe96", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:55.934622Z", "iopub.status.busy": "2025-03-25T05:14:55.934497Z", "iopub.status.idle": "2025-03-25T05:14:55.936603Z", "shell.execute_reply": "2025-03-25T05:14:55.936184Z" } }, "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" ] }, { "cell_type": "code", "execution_count": 6, "id": "93b3096f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:55.937802Z", "iopub.status.busy": "2025-03-25T05:14:55.937691Z", "iopub.status.idle": "2025-03-25T05:15:03.625099Z", "shell.execute_reply": "2025-03-25T05:15:03.624509Z" } }, "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.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }