Bioinformatics. 2022 Nov 16:btac739. doi: 10.1093/bioinformatics/btac739. Online ahead of print.
MOTIVATION: Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available protocols and multi-omics datasets (including parallel scRNA-seq and scATAC-seq data profiled from the same cell) are growing increasingly. However, such data are highly sparse and tend to have high level of noise, making data analysis challenging. The methods that integrate the multi-omics data can potentially improve the capacity of revealing the celluar heterogeneity.
RESULTS: We propose an adaptively weighted multi-view learning (scAWMV) method for the integrative analysis of parallel scRNA-seq and scATAC-seq data profiled from the same cell. scAWMV considers both the difference in importance across different modalities in multi-omics data and the biological connection of the features in the scRNA-seq and scATAC-seq data. It generates biologically meaningful low-dimensional representations for the transcriptomic and epigenomic profiles via unsupervised learning. Application to four real datasets demonstrates that our framework scAWMV is an efficient method to dissect cellular heterogeneity for single-cell multi-omics data.
AVAILABILITY: The software and datasets are available at https://github.com/pengchengzeng/scAWMV.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.