Faculty, Staff and Student Publications
Publication Date
8-1-2024
Journal
Advanced Science
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
Keywords
Single-Cell Analysis, Cell Cycle, RNA-Seq, Chromatin Immunoprecipitation Sequencing, Mice, Sequence Analysis, RNA, Humans, Animals, Computational Biology, Single-Cell Gene Expression Analysis, batch effect, cell cycle effect, domain separation network, single‐cell multi‐omics integration
DOI
10.1002/advs.202401815
PMID
38887194
PMCID
PMC11336957
PubMedCentral® Posted Date
6-17-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes