Computational methods for single-cell omics across modalities

Single-cell omics approaches provide high-resolution data on cellular phenotypes, developmental dynamics and communication networks in diverse tissues and conditions. Emerging technologies now measure different modalities of individual cells, such as genomes, epigenomes, transcriptomes and proteomes, in addition to spatial profiling. Combined with analytical approaches, these data open new avenues for accurate reconstruction of gene-regulatory and signaling networks driving cellular identity and function. Here we summarize computational methods for analysis and integration of single-cell omics data across different modalities and discuss their applications, challenges and future directions.

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We thank E. Dann and M. Sarkin Jain for careful and critical reading of the manuscript. We are grateful to J. Eliasova for help with the illustrations.

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M.E and S.A.T wrote the manuscript.

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Correspondence to Sarah A. Teichmann.

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The authors declare no competing interests.

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Efremova, M., Teichmann, S.A. Computational methods for single-cell omics across modalities. Nat Methods 17, 14–17 (2020).

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