Computational approaches for interpreting scRNA-seq data.
Published version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Rostom, Raghd
Svensson, Valentine
Teichmann, Sarah A
Kar, Gozde
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.
Description
Keywords
single-cell analysis methods and tools, single-cell genomics, Animals, Cluster Analysis, Computational Biology, Gene Expression, Humans, Sequence Analysis, RNA, Single-Cell Analysis
Journal Title
FEBS Lett
Conference Name
Journal ISSN
0014-5793
1873-3468
1873-3468
Volume Title
591
Publisher
Wiley