<p>Clustering algorithms and model fit to analyze karyotypic variation from flow cytometry data</p>

Margot Henry; Aleeza Gerstein

doi:10.7490/f1000research.1118691.1

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Henry M and Gerstein A. Clustering algorithms and model fit to analyze karyotypic variation from flow cytometry data [version 1; not peer reviewed]. F1000Research 2021, 10:722 (poster) (https://doi.org/10.7490/f1000research.1118691.1)

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Clustering algorithms and model fit to analyze karyotypic variation from flow cytometry data

Margot Henry¹, Aleeza Gerstein¹

Published 02 Aug 2021 (https://doi.org/10.7490/f1000research.1118691.1)

Author Affiliations

¹ University of Manitoba, Canada

Copyright © 2021 Henry M and Gerstein A. 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 02 Aug 2021

Clustering algorithms and model fit to analyze karyotypic variation from flow cytometry data
[version 1; not peer reviewed]

Margot Henry¹, Aleeza Gerstein¹

Author Affiliations

¹ University of Manitoba, Canada

Presented at

Bioconductor Virtual Conference 2021

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Flow cytometry, Cell cycle, Genome size, Machine Learning

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