Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms
Year: 2024
Type: article
Abstract: Introduction: Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) provide valuable insights into the cellular states of kidney cells. However, the annotation of cell types often requires extensive domain expertise and time-consuming manual curation, limiting scalability and generalizability. To facilitate this process, we tested the performance of five supervised classification methods for automatic cell type annotation. Results: We analyzed publicly available sc/snRNA-seq datasets from five expert-annotated studies, comprising 62,120 cells from 79 kidney biopsy samples. Datasets were integrated by harmonizing cell type annotations across studies. Five different supervised machine learning algorithms (support vector machines, random forests, multilayer perceptrons, k-nearest neighbors, and extreme gradient boosting) were applied to automatically annotate cell types using four training datasets and one testing dataset. Performance metrics, including accuracy (F1 score) and rejection rates, were evaluated. All five machine learning algorithms demonstrated high accuracies, with a median F1 score of 0.94 and a median rejection rate of 1.8 %. The algorithms performed equally well across different datasets and successfully rejected cell types that were not present in the training data. However, F1 scores were lower when models trained primarily on scRNA-seq data were tested on snRNA-seq data. Conclusions: Despite limitations including the number of biopsy samples, our findings demonstrate that machine learning algorithms can accurately annotate a wide range of adult kidney cell types in scRNA-seq/snRNA-seq data. This approach has the potential to standardize cell type annotation and facilitate further research on cellular mechanisms underlying kidney disease.
Source: Heliyon
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,100
Awards: Harvard Chronic Kidney Disease Research Biopsy Center, G1580919650, AKI Matched Phenotype Linked Evaluation with Tissue (AMPLE-Tissue), AKI Matched Phenotype Linked Evaluation with Tissue (AMPLE-Tissue), Phenotyping REnal Cases In Sepsis and surgery for Early Acute Kidney Injury (PReCISE AKI), Kidney Precision Medicine Program (KPMP): Columbia AKI Recruitment Site, Spatial Elucidation of Human Acute Kidney Injury and Chronic Kidney Disease using Imaging Mass Cytometry, PREcision Medicine through IntErrogation of Rna in the kidnEy (PREMIERE), Michigan Diabetes Research Center (MDRC), Minnesota Precision Medicine CKD & Resilient Diabetes Recruiting Site: Engagement, Enrollment & Ethics, Integrated spatial interrogation of cellular and molecular signatures of human kidney disease, Single cell multiomic and spatial atlas of acute and chronic kidney injury, Mount Sinai Health System Kidney Precision Medicine Project, University of Illinois at Chicago KPMP CKD Recruitment Site, AKI Matched Phenotype Linked Evaluation with Tissue (AMPLE-Tissue), Central Hub for Kidney Precision Medicine, Multimodal Imaging Mass Spectrometry and Spatial Omics for the Human Kidney, Boston Chronic Kidney Disease Research Biopsy Center, Cleveland Precision Medicine Chronic Kidney Disease Cohort, Kidney Precision Medicine Project - Patient Engagement, KPMP Kidney Mapping and Atlas Project (KMAP), Geographic and Environmental Health Equity in Kidney Precision Medicine, Spatial Multi-Omics to Profile Metabolic Pathways for Kidney Disease, Spatial Mapping of Proteomic and Transcriptional Signatures in Kidney Disease
OpenAlex ID: W4402878594