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

EURASIP Journal on Bioinformatics and Systems Biology

Table 2 Reconstruction of TF complex activity profiles.

Method	B1	N1	N2	N3
PLS	L1	0.52	0.53	0.52
BFA		0.87	0.69	0.76
MFA		0.77	0.80	0.73
PLS	L2	0.52	0.52	0.52
BFA		0.84	0.68	0.59
MFA		0.89	0.71	0.60
PLS	L3	0.53	0.52	0.52
BFA		0.90	0.75	0.56
MFA		0.94	0.87	0.40
Method	B2	N1	N2	N3
PLS	L1	0.53	0.52	0.52
BFA		0.92	0.89	0.78
MFA		0.88	0.83	0.71
PLS	L2	0.52	0.51	0.52
BFA		0.83	0.72	0.72
MFA		0.95	0.85	0.71
PLS	L3	0.52	0.51	0.52
BFA		0.90	0.73	0.67
MFA		0.98	0.94	0.63

The mean absolute correlation coefficient between the true and inferred activity profiles, averaged over the 6 synthetic activity profiles of Figure 3. N1, N2 and N3 refer to the three noise levels of and . L1, L2, and L3 refer to the expression profile lengths being 10, 20 and 40. B1 and B2 refer to the two different binding data sets with different levels of noise. Details are described in Section 4.1. Three methods have been compared: the partial least squares (PLSs) approach of Boulesteix and Strimmer [22]; the Bayesian factor analysis (BFA) model with Gibbs sampling, as proposed in Sabatti and James [16]; and the MFA model trained with VBEM, as described in Section 3.