RT Journal Article
SR Electronic
T1 Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.02.01.526609
DO 10.1101/2023.02.01.526609
A1 Michelle Y. Y. Lee
A1 Klaus H. Kaestner
A1 Mingyao Li
YR 2023
UL http://biorxiv.org/content/early/2023/02/03/2023.02.01.526609.abstract
AB Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) enables the quantification of chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types/states. However, when analyzed individually, scRNA-seq and snATAC-seq data often produce conflicting results regarding cell type/state assignment. In addition, there is a loss of power as the two modalities reflect the same underlying cell types/states. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data make it possible to directly model the relationships between the two modalities. However, given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality data to gain a comprehensive view of the cellular complexity. Here, we benchmarked the performance of seven existing single-cell multi-omic data integration methods. Specifically, we evaluated whether these methods are able to uncover peak-gene associations from single-modality data, and to what extent the multiome data can provide additional guidance for the analysis of the existing single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data, but the number of cells in the multiome data is critical to ensure a good cell type annotation. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation. Lastly, Seurat v4 is the best at integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.Competing Interest StatementM.L. receives research funding from Biogen Inc. The other authors declare no competing interests.