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Thresher: determining the number of clusters while removing outliers.

Abstract：

BACKGROUND:Cluster analysis is the most common unsupervised method for finding hidden groups in data. Clustering presents two main challenges: (1) finding the optimal number of clusters, and (2) removing "outliers" among the objects being clustered. Few clustering algorithms currently deal directly with the outlier problem. Furthermore, existing methods for identifying the number of clusters still have some drawbacks. Thus, there is a need for a better algorithm to tackle both challenges. RESULTS:We present a new approach, implemented in an R package called Thresher, to cluster objects in general datasets. Thresher combines ideas from principal component analysis, outlier filtering, and von Mises-Fisher mixture models in order to select the optimal number of clusters. We performed a large Monte Carlo simulation study to compare Thresher with other methods for detecting outliers and determining the number of clusters. We found that Thresher had good sensitivity and specificity for detecting and removing outliers. We also found that Thresher is the best method for estimating the optimal number of clusters when the number of objects being clustered is smaller than the number of variables used for clustering. Finally, we applied Thresher and eleven other methods to 25 sets of breast cancer data downloaded from the Gene Expression Omnibus; only Thresher consistently estimated the number of clusters to lie in the range of 4-7 that is consistent with the literature. CONCLUSIONS:Thresher is effective at automatically detecting and removing outliers. By thus cleaning the data, it produces better estimates of the optimal number of clusters when there are more variables than objects. When we applied Thresher to a variety of breast cancer datasets, it produced estimates that were both self-consistent and consistent with the literature. We expect Thresher to be useful for studying a wide variety of biological datasets.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Wang M,Abrams ZB,Kornblau SM,Coombes KR

doi

10.1186/s12859-017-1998-9

subject

Has Abstract

pub_date

2018-01-08 00:00:00

pages

issue

issn

1471-2105

pii

10.1186/s12859-017-1998-9

journal_volume

pub_type

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Thresher: determining the number of clusters while removing outliers.