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Rapid single-cell cytometry data visualization with EmbedSOM

View ORCID ProfileMiroslav Kratochvíl, Abhishek Koladiya, Jana Balounova, Vendula Novosadova, Karel Fišer, Radislav Sedlacek, Jiří Vondrášek, Karel Drbal
doi: https://doi.org/10.1101/496869
Miroslav Kratochvíl
aInstitute of Organic Chemistry and Biochemistry of the CAS, Prague
bDepartment of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague
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  • ORCID record for Miroslav Kratochvíl
Abhishek Koladiya
cDepartment of Cell Biology, Faculty of Science, Charles University, Prague
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Jana Balounova
dCzech Centre for Phenogenomics, Institute of Molecular Genetics of the CAS, Prague
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Vendula Novosadova
dCzech Centre for Phenogenomics, Institute of Molecular Genetics of the CAS, Prague
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Karel Fišer
eChildhood Leukaemia Investigation Prague (CLIP), 2nd Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
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Radislav Sedlacek
dCzech Centre for Phenogenomics, Institute of Molecular Genetics of the CAS, Prague
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Jiří Vondrášek
aInstitute of Organic Chemistry and Biochemistry of the CAS, Prague
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Karel Drbal
cDepartment of Cell Biology, Faculty of Science, Charles University, Prague
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Abstract

Efficient unbiased data analysis is a major challenge for laboratories handling large cytometry datasets. We present EmbedSOM, a non-linear embedding algorithm based on FlowSOM that improves the analyses by providing high-performance visualization of complex single cell distributions within cellular populations and their transition states. The algorithm is designed for linear scaling and speed suitable for interactive analyses of millions of cells without downsampling. At the same time, the visualization quality is competitive with current state-of-art algorithms. We demonstrate the properties of EmbedSOM on workflows that improve two essential types of analyses: The native ability of EmbedSOM to align population positions in embedding is used for comparative analysis of multi-sample data, and the connection to FlowSOM is exploited for simplifying the supervised hierarchical dissection of cell populations. Additionally, we discuss the visualization of the trajectories between cellular states facilitated by the local linearity of the embedding.

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Posted December 20, 2018.
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Rapid single-cell cytometry data visualization with EmbedSOM
Miroslav Kratochvíl, Abhishek Koladiya, Jana Balounova, Vendula Novosadova, Karel Fišer, Radislav Sedlacek, Jiří Vondrášek, Karel Drbal
bioRxiv 496869; doi: https://doi.org/10.1101/496869
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Rapid single-cell cytometry data visualization with EmbedSOM
Miroslav Kratochvíl, Abhishek Koladiya, Jana Balounova, Vendula Novosadova, Karel Fišer, Radislav Sedlacek, Jiří Vondrášek, Karel Drbal
bioRxiv 496869; doi: https://doi.org/10.1101/496869

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