Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.

ABSTRACT:

Background

Differences in data distribution, feature dimensions, and quality between different single-cell modalities pose challenges for clustering. Although clustering algorithms have been developed for single-cell transcriptomic or proteomic data, their performance across different omics data types and integration scenarios remains poorly investigated, which limits the selection of methods and future method development.

Results

In this study, we conduct a systematic and comparative benchmark analysis of 28 computational algorithms on 10 paired transcriptomic and proteomic datasets, evaluating their performance across various metrics in terms of clustering, peak memory, and running time. We also discuss the impact of highly variable genes (HVGs) and cell type granularity o

SUBMITTER: Yin YH 

PROVIDER: S-EPMC12406447 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature