Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms.

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, mu

SUBMITTER: Tisch A 

PROVIDER: S-EPMC11471582 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature