RT Journal Article
SR Electronic
T1 Fast and accurate single-cell RNA-Seq analysis by clustering of transcript-compatibility counts
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 036863
DO 10.1101/036863
A1 Vasilis Ntranos
A1 Govinda M. Kamath
A1 Jesse Zhang
A1 Lior Pachter
A1 David N. Tse
YR 2016
UL http://biorxiv.org/content/early/2016/01/15/036863.abstract
AB Current approaches to single-cell transcriptomic analysis are computationally expensive and require assay-specific modeling which limit their scope and generality. We propose a novel method that departs from standard analysis pipelines, comparing and clustering cells based not on their transcript or gene quantifications but on their transcript-compatibility read counts. In re-analysis of two landmark yet disparate single-cell RNA-Seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is universal, being directly applicable to data from a wide variety of assays.