Computational approaches for interpreting scRNA‐seq data

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 c...

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Bibliographic Details
Published inFEBS letters Vol. 591; no. 15; pp. 2213 - 2225
Main Authors Rostom, Raghd, Svensson, Valentine, Teichmann, Sarah A., Kar, Gozde
Format Journal Article
LanguageEnglish
Published England John Wiley and Sons Inc 01.08.2017
Subjects
Animals
Cluster Analysis
Computational Biology - methods
Gene Expression
genes
Humans
Review
RNA
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
single‐cell analysis methods and tools
single‐cell genomics
transcriptomics
single-cell analysis methods and tools
single-cell genomics
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Summary: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.
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Edited by Wilhelm Just
ISSN:0014-5793
1873-3468
1873-3468
DOI:10.1002/1873-3468.12684