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

Nature Methods volume 15, pages 359362 (2018) | Download Citation

Abstract

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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Acknowledgements

We thank T. Andrews, K.N. Natarajan, G. Parada, M. Schaub, M. Stubbington, V. Svensson, J. Westoby and F. Wünnermann for helpful discussions, feedback on the manuscript and testing of the cloud implementation of scmap. Amazon Web Services (AWS) Cloud provided credits for running the scmap server for 1 year. V.Y.K., A.Y. and M.H. were supported by core funding to the Wellcome Sanger Institute provided by the Wellcome Trust.

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Affiliations

  1. Wellcome Sanger Institute, Hinxton, UK.

    • Vladimir Yu Kiselev
    • , Andrew Yiu
    •  & Martin Hemberg

Authors

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Contributions

M.H. conceived the study and supervised the research; V.Y.K., A.Y. and M.H. contributed to the computational framework; V.Y.K. and M.H. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Martin Hemberg.

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DOI

https://doi.org/10.1038/nmeth.4644