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Annual Review of Biomedical Data Science

Volume 6, 2023

Single-Cell Multiomics

Abstract

Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.

Keyword(s): computation, integration, multimodal, multiomics, next-generation sequencing, single-cell
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/content/journals/10.1146/annurev-biodatasci-020422-050645
2023-08-10
2024-04-28
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/content/journals/10.1146/annurev-biodatasci-020422-050645
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Single-Cell Multiomics
Annual Review of Biomedical Data Science 6, 313 (2023); https://doi.org/10.1146/annurev-biodatasci-020422-050645
/content/journals/10.1146/annurev-biodatasci-020422-050645
/content/journals/10.1146/annurev-biodatasci-020422-050645
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