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Annual Review of Biomedical Data Science

Volume 3, 2020

Computational Methods for Single-Cell RNA Sequencing

Abstract

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

Keyword(s): clustering, computational methods, data integration, dimensionality reduction, gene regulatory networks, single-cell RNA sequencing
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/content/journals/10.1146/annurev-biodatasci-012220-100601
2020-07-20
2024-04-18
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Computational Methods for Single-Cell RNA Sequencing
Annual Review of Biomedical Data Science 3, 339 (2020); https://doi.org/10.1146/annurev-biodatasci-012220-100601
/content/journals/10.1146/annurev-biodatasci-012220-100601
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