Table 1 Overview of methods.

PLS

The partial least squares approach proposed by Boulesteix and Strimmer [22], using the software provided by the authors. Note that the method treats TF-gene interactions as fixed constants that cannot be changed in light of the gene expression data. Hence, this approach cannot be used for network reconstruction and was only applied for reconstructing the TF activity profiles.

FA

Maximum likelihood factor analysis, effected with the EM algorithm of Ghahramani and Hinton [24] and a subsequent varimax rotation [39] of the loading matrix towards maximum sparsity, as proposed in Pournara and Wernisch [18].

BFA-Gibbs

Bayesian factor analysis of Sabatti and James [16], trained with Gibbs sampling. The TF regulatory network is obtained from the posterior expected loading matrix via (A.32) and (A.35).

MFA-VBEM

The proposed mixture of factor analyzers model, shown in Figure 2 and discussed in Section 3, trained with variational Bayesian Expectation Maximization. The approach is based on the work of Beal [23], with the extension described in the text. The TF regulatory network is obtained from (24) and (25) for the curation and prediction tasks, respectively.