scmap: projection of single-cell RNA-seq data across data sets
By Vladimir Yu. Kiselev, A. Yiu, Martin Hemberg. 2018
634 citations recorded by the source corpus.
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.
Subfields: Biophysics, Molecular Biology
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In Biophysics
In Molecular Biology
- Rahul Satija
- Tim Stuart
- Avi Srivastava
- Peter Smibert
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- Paul J. Hoffman
- Andrew W. Butler
- William M. Mauck
Citations in the corpus
- SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species (2018, methodology): “SCMAP is the method most akin to SCN in intent (Kiselev et al., 2018) because it classifies query cells according to their similarity to reference cell types based on various measures of correlation.”
- Integrated analysis of multimodal single-cell data (2020, positive): “This metric and approach are inspired by scmap (Kiselev et al., 2018).”