Computational approaches for interpreting scRNA‐seq data. Issue 15 (12th June 2017)

• Record Type: Journal Article
• Authors: Rostom, Raghd; Svensson, Valentine; Teichmann, Sarah A.; Kar, Gozde
• Is Part Of: FEBS letters. Volume 591:Issue 15(2017)
• Page Start: 2213
• Page End: 2225
• Publication Date: 2017-06-12
• Subjects: single‐cell analysis methods and tools -- single‐cell genomics; Biochemistry -- Periodicals; Biophysics -- Periodicals; Molecular biology -- Periodicals; Biochimie -- Périodiques; Biochemistry; Biophysics; Molecular biology; Periodicals; 572.05
• DOI: 10.1002/1873-3468.12684
• Languages: English
• ISSNs: 0014-5793

Abstract

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. Abstract :