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, researchers from the Wellcome Trust Sanger Institute and the European Bioinformatics Institute 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.

Overview of analysis methods for the interpretation of scRNA-seq data


Rostom R, Svensson V, Teichmann SA, Kar G. (2017) Computational approaches for interpreting scRNA-seq data. FEBS Lett [Epub ahead of print]. [article]

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