SIMBA: SIngle-cell eMBedding Along with features

Abstract

Recent advances in single-cell omics technologies enable the individual and joint profiling of cellular measurements. Currently, most single-cell analysis pipelines are cluster-centric and cannot explicitly model the interactions between different feature types. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a unified way and thus simplifies the development of new analyses and integration of other single-cell modalities. SIMBA is implemented as an efficient, comprehensive, and extensible Python library (https://simba-bio.readthedocs.io) for the analysis of single-cell omics data using graph embedding.