Author:
Filippova Darya,Gadani Aashish,Kingsford Carl
Abstract
Abstract
Background
Clustering has become a standard analysis for many types of biological data (e.g interaction networks, gene expression, metagenomic abundance). In practice, it is possible to obtain a large number of contradictory clusterings by varying which clustering algorithm is used, which data attributes are considered, how algorithmic parameters are set, and which near-optimal clusterings are chosen. It is a difficult task to sift though such a large collection of varied clusterings to determine which clustering features are affected by parameter settings or are artifacts of particular algorithms and which represent meaningful patterns. Knowing which items are often clustered together helps to improve our understanding of the underlying data and to increase our confidence about generated modules.
Results
We present Coral, an application for interactive exploration of large ensembles of clusterings. Coral makes all-to-all clustering comparison easy, supports exploration of individual clusterings, allows tracking modules across clusterings, and supports identification of core and peripheral items in modules. We discuss how each visual component in Coral tackles a specific question related to clustering comparison and provide examples of their use. We also show how Coral could be used to visually and quantitatively compare clusterings with a ground truth clustering.
Conclusion
As a case study, we compare clusterings of a recently published protein interaction network of Arabidopsis thaliana. We use several popular algorithms to generate the network’s clusterings. We find that the clusterings vary significantly and that few proteins are consistently co-clustered in all clusterings. This is evidence that several clusterings should typically be considered when evaluating modules of genes, proteins, or sequences, and Coral can be used to perform a comprehensive analysis of these clustering ensembles.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference43 articles.
1. Sharan R, Ulitsky I: Network-based prediction of protein function. Mol Syst Biol 2007, 3: 88.
2. Ulitsky I, Maron-Katz A, Shavit S, Sagir D, Linhart C, Elkon R, Tanay A, Sharan R, Shiloh Y, Shamir R: Expander: from expression microarrays to networks and functions. Nat Protoc 2010, 5(2):303–322. 10.1038/nprot.2009.230
3. Chatterji S, Yamazaki I, Bai Z, Eisen JA: CompostBin: A DNA composition-based algorithm for binning environmental shotgun reads. Tech. rep., arXiv 2007 Tech. rep., arXiv 2007
4. White JR, Navlakha S, Nagarajan N, Ghodsi MR, Kingsford C, Pop M: Alignment and clustering of phylogenetic markers — implications for microbial diversity studies. BMC Bioinf 2010, 11: 152. 10.1186/1471-2105-11-152
5. van Dongen S: Graph clustering by flow simulation. PhD thesis. University of Utrecht, 2000 University of Utrecht, 2000
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