Generalized EmbedSOM on quadtree-structured self-organizing maps

F1000Research
Miroslav KratochvílJiří Vondrášek

Abstract

EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-directed embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.

References

Feb 5, 2008·IEEE Transactions on Neural Networks·A RauberM Dittenbach
Jan 13, 2015·Cytometry. Part a : the Journal of the International Society for Analytical Cytology·Sofie Van GassenYvan Saeys
Dec 20, 2016·Cytometry. Part a : the Journal of the International Society for Analytical Cytology·Lukas M Weber, Mark D Robinson
Nov 25, 2017·Nature Communications·Vincent van UnenBoudewijn P F Lelieveldt
Dec 12, 2018·Nature Biotechnology·Etienne BechtEvan W Newell
Dec 5, 2019·Nature Biotechnology·Kevin R MoonSmita Krishnaswamy

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Citations

Nov 19, 2020·GigaScience·Miroslav KratochvílMarkus Ollert
Oct 26, 2021·Cytometry. Part a : the Journal of the International Society for Analytical Cytology·Sarka VanikovaJan Musil

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Methods Mentioned

BETA
single-cell cytometry
MDS
PCA
dissection

Software Mentioned

GQTSOMs
mbed SOM
HSNE
UMAP
R
SOM
FlowSOM
MDS
TriMap
SNE

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