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Results: 4

1.
Fig. 3.

Fig. 3. From: Bayesian correlated clustering to integrate multiple datasets.

(a) Densities fitted to the sampled values of . (b) Heatmap representation of the matrix with -entry , the posterior mean value for

Paul Kirk, et al. Bioinformatics. 2012 December;28(24):3290-3297.
2.
Fig. 4.

Fig. 4. From: Bayesian correlated clustering to integrate multiple datasets.

(a) Pairwise fusion probabilities for the 31 genes identified as fused across the ChIP and PPI datasets in the ‘Expression + ChIP + PPI’ example. Colours correspond to fused clusters and the dashed line indicates the fusion threshold. (b) Three-way fusion probabilities for the same 31 genes. Genes that do not exceed the fusion threshold have white bars. (c) The expression profiles for genes identified as fused according to the ChIP and PPI datasets. The coloured lines indicate genes that are also fused across the expression dataset as well

Paul Kirk, et al. Bioinformatics. 2012 December;28(24):3290-3297.
3.
Fig. 1.

Fig. 1. From: Bayesian correlated clustering to integrate multiple datasets.

Graphical representation of three DMA mixture models. (a) Independent case. (b) The MDI model. In both (a) and (b), denotes the observation in dataset k and is generated by mixture component . The prior probabilities associated with the distinct component allocation variables, , are given in the vector , which is itself assigned a symmetric Dirichlet prior with parameter . The parameter vector, , for component c in dataset k is assigned a prior. In (b), we additionally have parameters, each of which models the dependence between the component allocations of observations in dataset k and

Paul Kirk, et al. Bioinformatics. 2012 December;28(24):3290-3297.
4.
Fig. 2.

Fig. 2. From: Bayesian correlated clustering to integrate multiple datasets.

(a) The data for the six-dataset synthetic example, separated into seven clusters. (b) A representation of how the cluster labels associated with each gene vary from dataset to dataset. Genes are ordered so that the clustering of Dataset 1 is the one that appears coherent. (c) A table showing the number of genes having the same cluster labels in datasets i and j. (d) A heatmap depiction of the similarity matrix formed by calculating the ARI between pairs of datasets

Paul Kirk, et al. Bioinformatics. 2012 December;28(24):3290-3297.

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