Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle

Using high throughput DNA binding data for transcription factors and DNA microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (NCA) approach. We incorporated probe level uncertainties in gene expression measurements into the NCA analysis by the application of probabilistic principal component analysis (PPCA), and applied the method to data from yeast metabolic cycle. Analysis shows statistically significant enhancement to periodicity in a large fraction of the transcription factor activities inferred from the model. For several of these we found literature evidence of post-transcriptional regulation. Accounting for probe level uncertainty of microarray measurements leads to improved network component analysis. Transcription factor profiles showing greater periodicity at their activity levels, rather than at the corresponding mRNA levels, for over half the regulators in the networks points to extensive post-transcriptional regulations.