Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization

EURASIP Journal on Bioinformatics and Systems Biology

Table 3 Enrichment for GO terms in predicted gene clusters.

Data	Clusters	Biologically meaningful clusters	Genes	Genes in biologically meaningful clusters
Average linkage
[35], E	48	10	3638	1483
[36], E	25	7	1993	1092
[35], E+B	30	8	3638	1148
[36], E+B	17	4	1993	703
K-means
[35], E	48	18	3638	1847
[36], E	25	12	1993	987
[35], E+B	30	13	3638	1337
[36], E+B	17	9	1993	884
COSA
[35], E	48	7	3638	1155
[36], E	25	8	1993	748
[35], E+B	30	10	3638	240
[36], E+B	17	4	1993	16
Plaid
[35], E	48	19	3638	1812
[36], E	25	10	1993	770
[35], E+B	30	11	3638	626
[36], E+B	17	9	1993	636
MFA-VBEM
[35], E	48	20	3638	2415
[36], E	25	16	1993	1278
[35], E+B	30	17	3638	2996
[36], E+B	17	14	1993	1645

The table shows the enrichment for known gene ontology (GO) terms in clusters predicted with different clustering algorithms from different data sets. Five clustering algorithms were compared: hierarchical agglomerative average linkage clustering, K-means, COSA [43], Plaid models [44], and the proposed MFA-VBEM scheme. The algorithms were applied to a combination of different microarray gene expression data. For the proposed MFA-VBEM algorithm, we additionally included the TF binding profiles of [34]. Clusters with significantly enriched GO terms (at the 5% significance level) are referred to as "biologically meaningful clusters". The number of genes in these clusters is shown in the rightmost column.
E: clustering based on gene expression data only; E+B: clusters obtained from both gene expression and TF binding data.