RT Journal Article
SR Electronic
T1 Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.06.438536
DO 10.1101/2021.04.06.438536
A1 Lei Xiong
A1 Kang Tian
A1 Yuzhe Li
A1 Qiangfeng Cliff Zhang
YR 2021
UL http://biorxiv.org/content/early/2021/04/08/2021.04.06.438536.abstract
AB Single-cell RNA-seq and ATAC-seq analyses have been widely applied to decipher cell-type and regulation complexities. However, experimental conditions often confound biological variations when comparing data from different samples. For integrative single-cell data analysis, we have developed SCALEX, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space. We demonstrate that SCALEX accurately and efficiently integrates heterogenous single-cell data using multiple benchmarks. It outperforms competing methods, especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We demonstrate the advantages of SCALEX by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19, which were assembled from multiple data sources and can keep growing through the inclusion of new incoming data. Analyses based on these atlases revealed the complex cellular landscapes of human and mouse tissues and identified multiple peripheral immune subtypes associated with COVID-19 disease severity.Competing Interest StatementThe authors have declared no competing interest.