Figure 8

TF regulatory network reconstruction for yeast. Receiver operating characteristic (ROC) curves obtained for S. cerevisiae with three different methods: (1) solid line: the proposed MFA-VBEM method, based on the work of [23], and extended as described in Section 3; (2) dashed line: the Bayesian FA model with Gibbs sampling, as proposed in Sabatti and James [16]; and (3) dotted line: maximum likelihood FA 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]. (a) The performance on a noisy training set, where 10% false positive interactions had been randomly added to the TF binding profiles from the literature [38], while the computation of the ROC curves was based on the un-perturbed literature data (network curation task). (b) The out-of-sample performance on an independent test set containing genes not used for training (network prediction). Note that in the latter case the Gibbs sampling approach was run twice, with two different prior matrices : a random prior, where for each gene 11 randomly chosen elements in the matrix were nonzero (dashed line); and a "good" prior, where the nonzero elements in were chosen according to Teixeira et al. [38] subject to the maximum connectivity constraint described in the text (dash-dotted line).