RT Journal Article
SR Electronic
T1 Scalable sequence-informed embedding of single-cell ATAC-seq data with CellSpace
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.02.490310
DO 10.1101/2022.05.02.490310
A1 Zakieh Tayyebi
A1 Allison R. Pine
A1 Christina S. Leslie
YR 2022
UL http://biorxiv.org/content/early/2022/05/20/2022.05.02.490310.abstract
AB Standard scATAC-seq analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. We present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space. CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and scores the activity of transcription factors in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors, or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia.Competing Interest StatementThe authors have declared no competing interest.