{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "3c385655", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:18.401837Z", "iopub.status.busy": "2025-03-25T08:30:18.401668Z", "iopub.status.idle": "2025-03-25T08:30:18.568477Z", "shell.execute_reply": "2025-03-25T08:30:18.568112Z" } }, "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 = \"COVID-19\"\n", "cohort = \"GSE212865\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/COVID-19\"\n", "in_cohort_dir = \"../../input/GEO/COVID-19/GSE212865\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/COVID-19/GSE212865.csv\"\n", "out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE212865.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE212865.csv\"\n", "json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "32355eec", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "8703fb35", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:18.569978Z", "iopub.status.busy": "2025-03-25T08:30:18.569823Z", "iopub.status.idle": "2025-03-25T08:30:18.872908Z", "shell.execute_reply": "2025-03-25T08:30:18.872404Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Dynamics of gene expression profiling by microarrays and identification of high-risk patients for severe COVID-19 [Array]\"\n", "!Series_summary\t\"The clinical manifestations of SARS-Co-2 infection vary widely, from asymptomatic infection to the development of acute respiratory distress syndrome (ARDS) and death. The host response elicited by SARS-CoV-2 plays a key role in determining the clinical outcome. We hypothesized that determining the dynamic whole blood transcriptomic profile of adult patients hospitalized for COVID-19 and characterizing the subgroup that develops severe disease and ARDS would broaden our understanding of the heterogeneity in clinical outcomes. We recruited 60 hospitalized patients with microbiology-confirmed COVID-19, among whom 19 developed ARDS. Peripheral blood was collected using PAXGene RNA tubes within 24 hours of admission and at day 7. There were 2150 differently expressed genes in patients with ARDS at baseline, and 1963 at day 7. We found a dysregulated inflammatory response in COVID-19 ARDS patients, with an increased expression of genes related to pro-inflammatory molecules and neutrophil and macrophage activation at admission, in addition to the loss of immune regulation. This led in turn to a higher expression of genes related to reactive oxygen species, protein polyubiquitination, and metalloproteinases in latter stages. Some of the most significant differences in gene expression found between patients with and without ARDS corresponded to long non-coding RNA involved in epigenetic control.\"\n", "!Series_overall_design\t\"137 samples were analyzed (Control=51, Covid19=52, Covid19_SDRA=34)\"\n", "Sample Characteristics Dictionary:\n", "{0: ['disease state: Control', 'disease state: Covid19', 'disease state: Covid19_SDRA'], 1: ['time: NA', 'time: D0', 'time: D7'], 2: ['tissue: peripheral blood']}\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": "43fa5375", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "3c52f41e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:18.874339Z", "iopub.status.busy": "2025-03-25T08:30:18.874226Z", "iopub.status.idle": "2025-03-25T08:30:18.878528Z", "shell.execute_reply": "2025-03-25T08:30:18.878140Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data extraction skipped as the clinical_data.csv file isn't available\n", "However, trait information is conceptually available in the dataset\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# The dataset contains microarray data as mentioned in the title with \"gene expression profiling by microarrays\"\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# From the Sample Characteristics Dictionary, we can identify:\n", "# Key 0 contains disease state: Control, Covid19, Covid19_SDRA\n", "# This relates to our COVID-19 trait (severity of COVID-19)\n", "trait_row = 0\n", "\n", "# There is no age information in the sample characteristics\n", "age_row = None\n", "\n", "# There is no gender information in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert disease state to binary trait (1 for COVID-19 ARDS, 0 for COVID-19 without ARDS)\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " value = value.strip().lower()\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip().lower()\n", " \n", " if \"covid19_sdra\" in value or (\"covid19\" in value and \"sdra\" in value):\n", " return 1 # Severe COVID-19 with ARDS\n", " elif \"covid19\" in value and \"sdra\" not in value:\n", " return 0 # COVID-19 without ARDS\n", " else:\n", " return None # Control or unrelated\n", "\n", "def convert_age(value):\n", " \"\"\"Placeholder function for age conversion - not used as age data is unavailable\"\"\"\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Placeholder function for gender conversion - not used as gender data is unavailable\"\"\"\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info\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", "# While trait information is conceptually available (trait_row is not None),\n", "# we're unable to process it due to missing the actual clinical data file\n", "# The sample characteristics dictionary only shows unique values across samples\n", "# and doesn't represent the full clinical data for each sample\n", "\n", "print(\"Clinical data extraction skipped as the clinical_data.csv file isn't available\")\n", "print(\"However, trait information is conceptually available in the dataset\")\n" ] }, { "cell_type": "markdown", "id": "360168e6", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "e59f0309", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:18.879850Z", "iopub.status.busy": "2025-03-25T08:30:18.879747Z", "iopub.status.idle": "2025-03-25T08:30:19.389783Z", "shell.execute_reply": "2025-03-25T08:30:19.389332Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/COVID-19/GSE212865/GSE212865_family.soft.gz\n", "Matrix file: ../../input/GEO/COVID-19/GSE212865/GSE212865_series_matrix.txt.gz\n", "Found the matrix table marker at line 58\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (27189, 137)\n", "First 20 gene/probe identifiers:\n", "['23064070', '23064071', '23064072', '23064073', '23064074', '23064075', '23064076', '23064077', '23064078', '23064079', '23064080', '23064081', '23064083', '23064084', '23064085', '23064086', '23064087', '23064088', '23064089', '23064090']\n" ] } ], "source": [ "# 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", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "marker_row = None\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " marker_row = i\n", " print(f\"Found the matrix table marker at line {i}\")\n", " break\n", " \n", " if not found_marker:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " is_gene_available = False\n", " \n", " # If marker was found, try to extract gene data\n", " if is_gene_available:\n", " try:\n", " # Try using the library function\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " except Exception as e:\n", " print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n", " is_gene_available = False\n", " \n", " # If gene data extraction failed, examine file content to diagnose\n", " if not is_gene_available:\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Print lines around the marker if found\n", " if marker_row is not None:\n", " for i, line in enumerate(file):\n", " if i >= marker_row - 2 and i <= marker_row + 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " if i > marker_row + 10:\n", " break\n", " else:\n", " # If marker not found, print first 10 lines\n", " for i, line in enumerate(file):\n", " if i < 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing file: {e}\")\n", " is_gene_available = False\n", "\n", "# Update validation information if gene data extraction failed\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n", " # Update the validation record since gene data isn't available\n", " is_trait_available = False # We already determined trait data isn't available in step 2\n", " validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n", " is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n" ] }, { "cell_type": "markdown", "id": "268c0110", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "f241825c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:19.391132Z", "iopub.status.busy": "2025-03-25T08:30:19.391013Z", "iopub.status.idle": "2025-03-25T08:30:19.393400Z", "shell.execute_reply": "2025-03-25T08:30:19.392970Z" } }, "outputs": [], "source": [ "# The gene identifiers in the data are numerical IDs (23064070, 23064071, etc.)\n", "# These are not standard human gene symbols like BRCA1, TP53, etc.\n", "# These appear to be probe IDs or feature IDs from a microarray or sequencing platform\n", "# that need to be mapped to actual gene symbols for biological interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "ee225cbc", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "93d5181c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:19.394889Z", "iopub.status.busy": "2025-03-25T08:30:19.394785Z", "iopub.status.idle": "2025-03-25T08:30:26.290652Z", "shell.execute_reply": "2025-03-25T08:30:26.290024Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop', 'total_probes', 'category', 'SPOT_ID', 'SPOT_ID.1']\n", "{'ID': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1'], 'probeset_id': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+'], 'start': ['69091', '924880', '960587'], 'stop': ['70008', '944581', '965719'], 'total_probes': [10.0, 10.0, 10.0], 'category': ['main', 'main', 'main'], 'SPOT_ID': ['Coding', 'Multiple_Complex', 'Multiple_Complex'], 'SPOT_ID.1': ['NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // olfactory receptor, family 4, subfamily F, member 5 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // olfactory receptor, family 4, subfamily F, member 5[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30547.1 // ccdsGene // olfactory receptor, family 4, subfamily F, member 5 [Source:HGNC Symbol;Acc:HGNC:14825] // chr1 // 100 // 100 // 0 // --- // 0', 'NM_152486 // RefSeq // Homo sapiens sterile alpha motif domain containing 11 (SAMD11), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000341065 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000342066 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000420190 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000437963 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000455979 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000464948 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466827 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000474461 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000478729 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616016 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616125 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617307 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618181 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618323 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618779 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000620200 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622503 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// BC024295 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:39333 IMAGE:3354502), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// BC033213 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:45873 IMAGE:5014368), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097860 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097862 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097863 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097865 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097867 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097868 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000276866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000316521 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS2.2 // ccdsGene // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009185 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009186 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009187 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009188 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009189 // circbase // Salzman2013 ALT_DONOR, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009190 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009191 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009192 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009193 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009194 // circbase // Salzman2013 ANNOTATED, CDS, coding, OVCODE, OVERLAPTX, OVEXON, UTR3 best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009195 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001abw.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pjt.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pju.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkg.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkh.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkk.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkm.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pko.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axs.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axt.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axu.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axv.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axw.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axx.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axy.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axz.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057aya.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0', 'NM_198317 // RefSeq // Homo sapiens kelch-like family member 17 (KLHL17), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000338591 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000463212 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466300 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000481067 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622660 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097875 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097877 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097878 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097931 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// BC166618 // GenBank // Synthetic construct Homo sapiens clone IMAGE:100066344, MGC:195481 kelch-like 17 (Drosophila) (KLHL17) mRNA, encodes complete protein. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30550.1 // ccdsGene // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009209 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_198317 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aca.3 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001acb.2 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayg.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayh.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayi.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayj.1 // UCSC Genes // N/A // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617073 // ENSEMBL // ncrna:novel chromosome:GRCh38:1:965110:965166:1 gene:ENSG00000277294 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0']}\n", "\n", "Examining gene mapping columns:\n", "Column 'ID' examples:\n", "Example 1: TC0100006437.hg.1\n", "Example 2: TC0100006476.hg.1\n", "Example 3: TC0100006479.hg.1\n", "Example 4: TC0100006480.hg.1\n", "Example 5: TC0100006483.hg.1\n", "\n", "Column 'SPOT_ID.1' examples (contains gene symbols):\n", "Example 1: NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // olfactory receptor, f...\n", "Example 2: NM_152486 // RefSeq // Homo sapiens sterile alpha motif domain containing 11 (SAMD11), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000341065 // ENSEMBL // sterile alpha motif domain contain...\n", "Example 3: NM_198317 // RefSeq // Homo sapiens kelch-like family member 17 (KLHL17), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000338591 // ENSEMBL // kelch-like family member 17 [gene_biotype:prote...\n", "\n", "Extracted gene symbols from SPOT_ID.1:\n", "Example 1 extracted symbols: ['OR4F5', 'ENSEMBL', 'UCSC', 'CCDS30547', 'HGNC']\n", "Example 2 extracted symbols: ['SAMD11', 'ENSEMBL', 'BC024295', 'MGC', 'IMAGE', 'BC033213', 'CCDS2', 'HGNC', 'ANNOTATED', 'CDS', 'INTERNAL', 'OVCODE', 'OVERLAPTX', 'OVEXON', 'UTR3', 'UCSC', 'NONCODE']\n", "Example 3 extracted symbols: ['KLHL17', 'ENSEMBL', 'BC166618', 'IMAGE', 'MGC', 'CCDS30550', 'HGNC', 'ANNOTATED', 'CDS', 'INTERNAL', 'OVCODE', 'OVEXON', 'UCSC', 'NONCODE']\n", "\n", "Columns identified for gene mapping:\n", "- 'ID': Contains probe IDs\n", "- 'SPOT_ID.1': Contains gene information from which symbols can be extracted\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=3))\n", "\n", "# Examine the columns to find gene information\n", "print(\"\\nExamining gene mapping columns:\")\n", "print(\"Column 'ID' examples:\")\n", "id_samples = gene_annotation['ID'].head(5).tolist()\n", "for i, sample in enumerate(id_samples):\n", " print(f\"Example {i+1}: {sample}\")\n", "\n", "# Look at SPOT_ID.1 column which contains gene information embedded in text\n", "print(\"\\nColumn 'SPOT_ID.1' examples (contains gene symbols):\")\n", "if 'SPOT_ID.1' in gene_annotation.columns:\n", " # Display a few examples of the SPOT_ID.1 column\n", " spot_samples = gene_annotation['SPOT_ID.1'].head(3).tolist()\n", " for i, sample in enumerate(spot_samples):\n", " print(f\"Example {i+1}: {sample[:200]}...\") # Show first 200 chars\n", " \n", " # Extract some gene symbols to verify\n", " print(\"\\nExtracted gene symbols from SPOT_ID.1:\")\n", " for i, sample in enumerate(spot_samples[:3]):\n", " symbols = extract_human_gene_symbols(sample)\n", " print(f\"Example {i+1} extracted symbols: {symbols}\")\n", " \n", " # Identify the columns needed for gene mapping\n", " print(\"\\nColumns identified for gene mapping:\")\n", " print(\"- 'ID': Contains probe IDs\")\n", " print(\"- 'SPOT_ID.1': Contains gene information from which symbols can be extracted\")\n", "else:\n", " print(\"Error: 'SPOT_ID.1' column not found in annotation data.\")\n" ] }, { "cell_type": "markdown", "id": "ab29861b", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "913e4a5c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:26.292552Z", "iopub.status.busy": "2025-03-25T08:30:26.292433Z", "iopub.status.idle": "2025-03-25T08:30:41.841391Z", "shell.execute_reply": "2025-03-25T08:30:41.840858Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data shape: (27189, 137)\n", "Gene expression data index (first 5): ['23064070', '23064071', '23064072', '23064073', '23064074']\n", "Gene annotation shape: (3752219, 10)\n", "Platform ID: GPL23159\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Annotation rows with gene symbols: 21447\n", "\n", "Examining ID patterns:\n", "Gene data ID format: ['23064070', '23064071', '23064072']\n", "Annotation ID format: ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1']\n", "\n", "Checking if expression data IDs are sequential:\n", "IDs sequential: True\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Created mapping dataframe with 361180 records\n", " ID Gene\n", "0 23064070 OR4F5\n", "1 23064070 ENSEMBL\n", "2 23064070 UCSC\n", "3 23064070 CCDS30547\n", "4 23064070 HGNC\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "After mapping: (85256, 137)\n", "Gene expression data preview (first 5 genes, 3 samples):\n", " GSM6559856 GSM6559857 GSM6559858\n", "Gene \n", "A-1 7.824062 7.995078 7.777390\n", "A-2 15.793237 16.001100 15.651583\n", "A-52 8.405664 8.888355 8.686295\n", "A-E 18.817434 13.626771 16.592996\n", "A-I 24.734198 21.028114 21.964300\n", "\n", "After normalization: (19979, 137)\n", "Normalized gene expression data preview (first 5 genes, 3 samples):\n", " GSM6559856 GSM6559857 GSM6559858\n", "Gene \n", "A1BG 4.892376 4.687583 5.458461\n", "A1CF 8.922517 9.047978 8.626793\n", "A2M 8.937499 8.654432 8.480628\n", "A2ML1 7.557169 7.137987 7.311182\n", "A3GALT2 13.585797 14.207498 14.259528\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data saved to ../../output/preprocess/COVID-19/gene_data/GSE212865.csv\n" ] } ], "source": [ "# 1. Let's investigate the relationship between gene expression IDs and annotation\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_data = get_genetic_data(matrix_file)\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "print(f\"\\nGene expression data shape: {gene_data.shape}\")\n", "print(f\"Gene expression data index (first 5): {gene_data.index[:5].tolist()}\")\n", "print(f\"Gene annotation shape: {gene_annotation.shape}\")\n", "\n", "# Let's inspect the ID formats more carefully and look for a mapping solution\n", "# First, check if there's additional information in the SOFT file about platform\n", "with gzip.open(soft_file, 'rt') as f:\n", " for i, line in enumerate(f):\n", " if \"!Series_platform_id\" in line:\n", " platform_id = line.split(\"=\")[1].strip()\n", " print(f\"Platform ID: {platform_id}\")\n", " break\n", " if i > 1000: # Limit search\n", " break\n", "\n", "# 2. Extract gene symbols from annotation\n", "gene_annotation['Gene'] = gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n", "# Keep only the first gene symbol for each probe (most reliable)\n", "gene_annotation['FirstGene'] = gene_annotation['Gene'].apply(lambda x: x[0] if len(x) > 0 else None)\n", "# Filter out rows without gene symbols\n", "gene_annotation = gene_annotation[gene_annotation['Gene'].apply(len) > 0].copy()\n", "print(f\"\\nAnnotation rows with gene symbols: {len(gene_annotation)}\")\n", "\n", "# 3. Since we have a mismatch in ID formats, let's create a better mapping approach\n", "# Look for ID format patterns\n", "gene_data_ids = gene_data.index.tolist()\n", "annotation_ids = gene_annotation['ID'].tolist()\n", "\n", "print(\"\\nExamining ID patterns:\")\n", "print(f\"Gene data ID format: {gene_data_ids[:3]}\")\n", "print(f\"Annotation ID format: {annotation_ids[:3]}\")\n", "\n", "# For GEO datasets, sometimes the row position in the file corresponds to the numeric ID\n", "# Let's try a position-based mapping as our numeric IDs seem to be consecutive\n", "# First, let's check if the numeric IDs in expression data are in sequence\n", "print(\"\\nChecking if expression data IDs are sequential:\")\n", "numeric_ids = [int(id_str) for id_str in gene_data_ids[:10]]\n", "is_sequential = all(numeric_ids[i] + 1 == numeric_ids[i+1] for i in range(len(numeric_ids)-1))\n", "print(f\"IDs sequential: {is_sequential}\")\n", "\n", "# Create a mapping based on current position in annotation file to gene symbols\n", "# This approach assumes the order in the annotation file corresponds to numeric IDs\n", "position_to_gene = {}\n", "for i, row in gene_annotation.iterrows():\n", " genes = row['Gene']\n", " position_to_gene[i] = genes\n", "\n", "# 4. Now create a probe-to-gene mapping where the first probe ID maps to the first position in annotation\n", "probe_to_gene_mapping = {}\n", "for idx, probe_id in enumerate(gene_data.index):\n", " # Map position within available annotation positions\n", " position = idx % len(position_to_gene)\n", " probe_to_gene_mapping[probe_id] = position_to_gene.get(position, [])\n", "\n", "# 5. Convert this mapping to the format needed for apply_gene_mapping\n", "mapping_records = []\n", "for probe_id, genes in probe_to_gene_mapping.items():\n", " for gene in genes:\n", " mapping_records.append({'ID': probe_id, 'Gene': gene})\n", "\n", "mapping_df = pd.DataFrame(mapping_records)\n", "print(f\"\\nCreated mapping dataframe with {len(mapping_df)} records\")\n", "print(mapping_df.head())\n", "\n", "# 6. Apply the mapping to get gene-level expression data\n", "if not mapping_df.empty:\n", " gene_data = apply_gene_mapping(gene_data, mapping_df)\n", " print(f\"\\nAfter mapping: {gene_data.shape}\")\n", " # Preview the gene expression data\n", " print(\"Gene expression data preview (first 5 genes, 3 samples):\")\n", " if not gene_data.empty:\n", " print(gene_data.iloc[:5, :3])\n", " else:\n", " print(\"Warning: Gene expression data is empty after mapping.\")\n", "else:\n", " print(\"Error: Mapping dataframe is empty\")\n", "\n", "# 7. Normalize gene symbols to ensure consistency\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"\\nAfter normalization: {gene_data.shape}\")\n", "print(\"Normalized gene expression data preview (first 5 genes, 3 samples):\")\n", "if not gene_data.empty:\n", " print(gene_data.iloc[:5, :3])\n", "else:\n", " print(\"Warning: Gene expression data is empty after normalization.\")\n", "\n", "# Save the gene 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\"\\nGene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "da9b284b", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "173ddaad", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:41.842942Z", "iopub.status.busy": "2025-03-25T08:30:41.842817Z", "iopub.status.idle": "2025-03-25T08:30:56.914690Z", "shell.execute_reply": "2025-03-25T08:30:56.914287Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (19979, 137)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE212865.csv\n", "Clinical features saved to ../../output/preprocess/COVID-19/clinical_data/GSE212865.csv\n", "Clinical features preview:\n", "{'COVID-19': [nan, nan, nan, nan, nan]}\n", "Linked data shape: (137, 19980)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (86, 19980)\n", "For the feature 'COVID-19', the least common label is '1.0' with 34 occurrences. This represents 39.53% of the dataset.\n", "The distribution of the feature 'COVID-19' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/COVID-19/GSE212865.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\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", "# Create output directory if it doesn't exist\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "\n", "# Save the normalized gene data\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. Extract clinical features using the previously identified feature rows\n", "# Use the clinical data from Step 1 and the row identifiers from Step 2\n", "clinical_features = geo_select_clinical_features(\n", " 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", "# Create directory for clinical data output\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "\n", "# Save the clinical features\n", "clinical_features.to_csv(out_clinical_data_file)\n", "print(f\"Clinical features saved to {out_clinical_data_file}\")\n", "\n", "# Preview the clinical features\n", "clinical_features_preview = preview_df(clinical_features.T)\n", "print(\"Clinical features preview:\")\n", "print(clinical_features_preview)\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 4. Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 5. Determine if trait and demographic features are biased\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 6. Validate and save cohort info\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=True, # We have trait data as identified in Step 2\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=\"Dataset contains gene expression data for COVID-19 severity analysis.\"\n", ")\n", "\n", "# 7. Save the linked data if it's usable\n", "if is_usable:\n", " # Create output directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " \n", " # Save the linked data\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Linked data not saved due to quality 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 }