<p>Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation</p>

Valentine Svensson; Tapio Lönnberg; Kylie R James; Oliver Stegle; Ashraful Haque; Sarah A Teichmann

doi:10.7490/f1000research.1113890.1

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Svensson V, Lönnberg T, James KR et al. Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation [version 1; not peer reviewed]. F1000Research 2017, 6:439 (poster) (https://doi.org/10.7490/f1000research.1113890.1)

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Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation

Valentine Svensson¹, Tapio Lönnberg, Kylie R James, Oliver Stegle, Ashraful Haque, Sarah A Teichmann

Published 06 Apr 2017 (https://doi.org/10.7490/f1000research.1113890.1)

Author Affiliations

¹ EMBL European Bioinformatics Institute, UK

Copyright © 2017 Svensson V 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 06 Apr 2017

Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation
[version 1; not peer reviewed]

Valentine Svensson¹, Tapio Lönnberg, Kylie R James, Oliver Stegle, Ashraful Haque, Sarah A Teichmann

Author Affiliations

¹ EMBL European Bioinformatics Institute, UK

Presented at

Mathematical and Statistical Aspects of Molecular Biology (MASAMB) 2016

Cell Symposia: Technology. Biology. Data Science. Conference 2016

NIPS Workshop on Machine Learning in Computational Biology (MLCB) 2016

Single Cell Genomics Conference 2016

Competing Interests

No competing interests were disclosed

Keywords

Single cell RNA-seq, malaria, gaussian process, gplvm, omgp, th1, tfh

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