<p>Training and interpreting hierarchical cell type classification models using mass, heterogeneous RNA-seq data from human primary cells</p>

Matthew N. Bernstein; Zhongjie Ma; Michael Gleicher; Colin N. Dewey

doi:10.7490/f1000research.1118126.1

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Bernstein MN, Ma Z, Gleicher M and Dewey CN. Training and interpreting hierarchical cell type classification models using mass, heterogeneous RNA-seq data from human primary cells [version 1; not peer reviewed]. F1000Research 2020, 9(ISCB Comm J):830 (poster) (https://doi.org/10.7490/f1000research.1118126.1)

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Training and interpreting hierarchical cell type classification models using mass, heterogeneous RNA-seq data from human primary cells

Matthew N. Bernstein¹, Zhongjie Ma, Michael Gleicher, Colin N. Dewey

Published 30 Jul 2020 (https://doi.org/10.7490/f1000research.1118126.1)

Author Affiliations

¹ Morgridge Institute for Research, USA

Copyright © 2020 Bernstein MN et al. 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 30 Jul 2020

Training and interpreting hierarchical cell type classification models using mass, heterogeneous RNA-seq data from human primary cells
[version 1; not peer reviewed]

Matthew N. Bernstein¹, Zhongjie Ma, Michael Gleicher, Colin N. Dewey

Author Affiliations

¹ Morgridge Institute for Research, USA

Presented at

28th International Conference on Intelligent Systems for Molecular Biology (ISMB) 2020

Abstract

Competing Interests

No competing interests were disclosed

Keywords

cell type, single cell RNA-seq, classification, ontology

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