Software
R/BHC: fast Bayesian hierarchical clustering for microarray data
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* Corresponding author: David L Wild d.l.wild@warwick.ac.uk
1 Systems Biology Centre, University of Warwick, Coventry House, Coventry, CV4 7AL, UK
2 Warwick HRI, University of Warwick, Wellesbourne, CV35 9EF, UK
3 Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
4 School of Biosciences, University of Exeter, Exeter, EX4 4QD, UK
BMC Bioinformatics 2009, 10:242 doi:10.1186/1471-2105-10-242
Published: 6 August 2009Additional files
Additional file 1:
Figure 2. Gene clustering dendrogram of a subset of the Ideker et al. data, showing leaf harmony values
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Additional file 2:
Table 1 – Speed-trial of the BHC algorithm. Trials were based on the NASC data (880 genes, 31 features), clustering over genes. In each case, the data were duplicated or a subset of genes taken as appropriate to get the required number genes and features. All trials were run on a single 2 GHz CPU core on a Macbook Pro laptop.
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Additional file 3:
Table 2. Data discretisation for NASC experiment clustering
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Additional file 4:
Table 3. Data discretisation for NASC gene clustering
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Additional file 5:
Figure 3. Condition clustering dendrogram for the NASC data.
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Additional file 6:
LeafDisparity values for the NASC experiments. The BHC clustering dendrogram is compared to a standard hierarchical method using uncentred correlation coefficients and complete linnkage.
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Additional file 7:
Figure 4. Gene clustering dendrogram for the NASC data.
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Additional file 8:
BHC cluster membership. BHC cluster membership
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Additional file 9:
GO annotations for BHC clusters. Statistically significantly over-represented GO annotations for BHC clusters (Bonferroni-corrected p-value < 0.05)
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Additional file 10:
GO annotations for agglomerative hierarchical clustering. Statistically significantly over-represented GO annotations for clusters manually identified from agglomerative hierarchical clustering (Bonferroni-corrected p-value < 0.05)
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