CPS Analysis: Self-contained validation of biomedical data clustering

Bioinformatics
Lixiang ZhangJia Li

Abstract

Cluster analysis is widely used to identify interesting subgroups in biomedical data. Since true class labels are unknown in the unsupervised setting, it is challenging to validate any cluster obtained computationally, an important problem barely addressed by the research community. We have developed a toolkit called Covering Point Set (CPS) analysis to quantify uncertainty at the levels of individual clusters and overall partitions. Functions have been developed to effectively visualize the inherent variation in any cluster for data of high dimension, and provide more comprehensive view on potentially interesting subgroups in the data. Applying to three usage scenarios for biomedical data, we demonstrate that CPS analysis is more effective for evaluating uncertainty of clusters comparing to state-of-the-art measurements. We also showcase how to use CPS analysis to select data generation technologies or visualization methods. The method is implemented in an R package called OTclust, available on CRAN. Supplementary information are available at Bioinformatics online.

References

Nov 8, 2008·IEEE Transactions on Visualization and Computer Graphics·Markus GlatterAidong Lu
Jul 31, 2013·Nature Reviews. Genetics·Ehud ShapiroSten Linnarsson
Aug 13, 2013·Nature Structural & Molecular Biology·Liying YanFuchou Tang
Mar 28, 2017·Nature Methods·Vladimir Yu KiselevMartin Hemberg
Aug 23, 2018·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Snehalika LallDebarka Sengupta

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