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Cited by 379

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

Critical limitations of consensus clustering in class discovery

Open access
Șenbabaoğlu, Yasin, Michailidis, George, Li, Jun Z.·2014·Scientific Reports
DOI 10.1038/srep06207
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Abstract

Abstract Consensus clustering (CC) is an unsupervised class discovery method widely used to study sample heterogeneity in high-dimensional datasets. It calculates “consensus rate” between any two samples as how frequently they are grouped together in repeated clustering runs under a certain degree of random perturbation. The pairwise consensus rates form a between-sample similarity matrix, which has been used (1) as a visual proof that clusters exist, (2) for comparing stability among clusters, and (3) for estimating the optimal number (K) of clusters. However, the sensitivity and specificity of CC have not been systemically studied. To assess its performance, we investigated the most common implementations of CC; and compared CC with other popular methods that also focus on cluster stability and estimation of K. We evaluated these methods using simulated datasets with either known structure or known absence of structure. Our results showed that (1) CC was able to divide randomly generated unimodal data into pre-specified numbers of clusters, and was able to show apparent stability of these chance partitions of known cluster-less data; (2) for data with known structure, the proportion of ambiguously clustered (PAC) pairs infers the known number of clusters more reliably than several commonly used K estimating methods; and (3) validation of the optimal K by choosing the most discriminant genes from the discovery cohort and applying them in an independent cohort often exaggerates the confidence in K due to inherent gene-gene correlations among the selected genes. While these results do not yet prove that any of the published studies using CC has generated false positive findings, they show that datasets with subtle or no structure are fully capable of producing strong evidence of consensus clustering. We therefore recommend caution is using CC in class discovery and validation.

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Metadata

Authors
Șenbabaoğlu, Y., Michailidis, G., & Li, J. Z.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2014-08-27
Volume
4
Issue
1
Pages
6207-6207
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/srep06207
OpenAlex
W2025303952
Cited by
379
References
40
FWCI
10.56
Type
journal article
Language
en
Open access
gold

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