Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data.

ABSTRACT:

Background

A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.

Results

We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell

SUBMITTER: Yu L 

PROVIDER: S-EPMC8822786 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature