scmap: projection of single-cell RNA-seq data across data sets | Zendy

Vladimir Yu Kiselev | Zendy; Andrew Yiu | Zendy; Martin Hemberg | Zendy

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scmap: projection of single-cell RNA-seq data across data sets

Author(s) -

Vladimir Yu Kiselev,

Andrew Yiu,

Martin Hemberg

Publication year - 2018

Publication title -

nature methods

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 19.469

H-Index - 318

eISSN - 1548-7105

pISSN - 1548-7091

DOI - 10.1038/nmeth.4644

Subject(s) - bioconductor , computer science , data set , cluster analysis , set (abstract data type) , rna seq , computational biology , projection (relational algebra) , transcriptome , data mining , biology , artificial intelligence , genetics , gene expression , gene , algorithm , programming language

Single-cell RNA-seq (scRNA-seq) allows researchers to define cell types on the basis of unsupervised clustering of the transcriptome. However, differences in experimental methods and computational analyses make it challenging to compare data across experiments. Here we present scmap (http://bioconductor.org/packages/scmap; web version at http://www.sanger.ac.uk/science/tools/scmap), a method for projecting cells from an scRNA-seq data set onto cell types or individual cells from other experiments.

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