Diametrical clustering for identifying anti-correlated gene clusters

Dhillon, Inderjit S.; Marcotte, Edward M.; Roshan, Usman

doi:10.1093/bioinformatics/btg209

Cited by 90 publications

(69 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another possible explanation is that the subunit II genes in the six species were analogously displaced by unrelated sequences (nonorthologous replacement). Indeed, displacement of an enzyme is now widely acknowledged as an important source of phylogenetic difference (Dhillon et al 2003;Morett et al 2003). All subunit II genes from the Staphylococcus-Bacillus cluster were used as query sequences to find potential homologues without regard to the physical location relative to subunit I.…”

Section: Discussionmentioning

confidence: 99%

Asymmetrical Evolution of Cytochrome bd Subunits

Hao

Golding

2006

J Mol Evol

View full text Add to dashboard Cite

Abstract. Functionally linked genes generally evolve at similar rates and the knowledge of this particular feature of genomic evolution has been used as the basis for the phylogenetic profiling method. We illustrate here an exception to this rule in the evolution of the cytochrome bd complex. This is a twocomponent oxidase complex, with the subunits I and II known to be widely present in bacteria. The subunits within the cytochrome bd complex are under the same evolutionary pressure and most likely behave in the same evolutionary manner. However, the sequence similarity of genes encoding subunit II varies considerably across species. Genes encoding subunit II evolve 1.2 times faster on most of the branches of their phylogeny than subunit I genes. Furthermore, the genes encoding subunit II in Oceanobacillus iheyensis, Bacillus halodurans, and Staphylococcus species do not have detectable homologues within E. coli due to their large divergence. Together, the two subunits of cytochrome bd reveal an interesting example of an asymmetric pattern of evolutionary change.

show abstract

Section: Discussionmentioning

confidence: 99%

Asymmetrical Evolution of Cytochrome bd Subunits

Hao

Golding

2006

J Mol Evol

View full text Add to dashboard Cite

show abstract

“…Anti-correlation has been observed to imply functional similarity of genes [9]; we plan to extend our co-clustering algorithms to detect such anti-correlations.…”

Section: Discussionmentioning

confidence: 99%

“…For a survey, see [12]. Dhillon et al [9] present diametric clustering for identifying anti-correlated gene clusters for it is observed that genes that are functionally related may demonstrate strong anti-correlation in their expression levels. All of the above work is focussed on clustering genes using conditions as features.…”

Section: Introductionmentioning

confidence: 99%

Minimum Sum-Squared Residue Co-clustering of Gene Expression Data

Cho

Dhillon²,

Guan

et al. 2004

Proceedings of the 2004 SIAM International Conference on Data Mining

232

229

View full text Add to dashboard Cite

Microarray experiments have been extensively used for simultaneously measuring DNA expression levels of thousands of genes in genome research. A key step in the analysis of gene expression data is the clustering of genes into groups that show similar expression values over a range of conditions. Since only a small subset of the genes participate in any cellular process of interest, by focusing on subsets of genes and conditions, we can lower the noise induced by other genes and conditions -a co-cluster characterizes such a subset of interest. Cheng and Church [3] introduced an effective measure of co-cluster quality based on mean squared residue. In this paper, we use two similar squared residue measures and propose two fast k-means like co-clustering algorithms corresponding to the two residue measures. Our algorithms discover k row clusters and l column clusters simultaneously while monotonically decreasing the respective squared residues. Our co-clustering algorithms inherit the simplicity, efficiency and wide applicability of the k-means algorithm. Minimizing the residues may also be formulated as trace optimization problems that allow us to obtain a spectral relaxation that we use for a principled initialization for our iterative algorithms. We further enhance our algorithms by an incremental local search strategy that helps avoid empty clusters and escape poor local minima. We illustrate co-clustering results on a yeast cell cycle dataset and a human B-cell lymphoma dataset. Our experiments show that our co-clustering algorithms are efficient and are able to discover coherent co-clusters.

show abstract

“…We also assume a constant and positive concentration parameter κ > 0. The associated Bregman divergence D W is then a scaled cosine-squared dissimilarity measure, which is equivalent to the measure used for diametrical clustering-the asymptotic limit of a mixture of Watsons [4,12]. The Bregman divergences for the Gaussian and Watson distributions can then be used to define hard clustering algorithms, which we'll describe next.…”

Section: Gaussian and Watson Distributionsmentioning

confidence: 99%

White Matter Supervoxel Segmentation by Axial DP-Means Clustering

Cabeen¹,

Laidlaw²

2014

Medical Computer Vision. Large Data in Medical Imaging

View full text Add to dashboard Cite

Abstract. A powerful aspect of diffusion MR imaging is the ability to reconstruct fiber orientations in brain white matter; however, the application of traditional learning algorithms is challenging due to the directional nature of the data. In this paper, we present an algorithmic approach to clustering such spatial and orientation data and apply it to brain white matter supervoxel segmentation. This approach is an extension of the DP-means algorithm to support axial data, and we present its theoretical connection to probabilistic models, including the Gaussian and Watson distributions. We evaluate our method with the analysis of synthetic data and an application to diffusion tensor atlas segmentation. We find our approach to be efficient and effective for the automatic extraction of regions of interest that respect the structure of brain white matter. The resulting supervoxel segmentation could be used to map regional anatomical changes in clinical studies or serve as a domain for more complex modeling.

show abstract

Diametrical clustering for identifying anti-correlated gene clusters

Cited by 90 publications

References 28 publications

Asymmetrical Evolution of Cytochrome bd Subunits

Asymmetrical Evolution of Cytochrome bd Subunits

Minimum Sum-Squared Residue Co-clustering of Gene Expression Data

White Matter Supervoxel Segmentation by Axial DP-Means Clustering

Contact Info

Product

Resources

About