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FEBS Lett. 2017 Aug;591(15):2213-2225. doi: 10.1002/1873-3468.12684. Epub 2017 Jun 12.

Computational approaches for interpreting scRNA-seq data.

Author information

1
Wellcome Trust Sanger Institute, Cambridge, UK.
2
The European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.

Abstract

The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high-dimensional data mining techniques. Here, we consider biological questions for which scRNA-seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene-level analyses are particularly informative areas of computational analysis.

KEYWORDS:

single-cell analysis methods and tools; single-cell genomics

PMID:
28524227
PMCID:
PMC5575496
DOI:
10.1002/1873-3468.12684
[Indexed for MEDLINE]
Free PMC Article

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