<p>scAnnotatR: Framework to accurately classify cell types in single-cell RNA-sequencing data</p>

Vy Nguyen; Johannes Grisss

doi:10.7490/f1000research.1118708.1

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Nguyen V and Grisss J. scAnnotatR: Framework to accurately classify cell types in single-cell RNA-sequencing data [version 1; not peer reviewed]. F1000Research 2021, 10:755 (poster) (https://doi.org/10.7490/f1000research.1118708.1)

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scAnnotatR: Framework to accurately classify cell types in single-cell RNA-sequencing data

Vy Nguyen, Johannes Grisss¹

Published 04 Aug 2021 (https://doi.org/10.7490/f1000research.1118708.1)

Author Affiliations

¹ Medical University of Vienna, Austria

Copyright © 2021 Nguyen V and Grisss J. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 04 Aug 2021

scAnnotatR: Framework to accurately classify cell types in single-cell RNA-sequencing data
[version 1; not peer reviewed]

Vy Nguyen, Johannes Grisss¹

Author Affiliations

¹ Medical University of Vienna, Austria

Presented at

Bioconductor Virtual Conference 2021

Abstract

Competing Interests

No competing interests were disclosed

Keywords

scAnnotatR, cell classification, scRNAseq, machine learning, SVM, R, Bioconductor

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