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

摘要

How specific biological processes with temporal dynamics are regulated by the coordinated action of transcription factors is of much interest. The availability of gene expression measurements with microarrays and binding specificities of regulator proteins enables computational approaches to infer the regulatory dynamics. The need for model based inference arises from the fact that transcription factors themselves are subject to potential posttranscriptional regulation. Hence microarray measurements of their temporal profiles does not carry full information about their activities. Network component analysis provides a formal computational setting in which networks satisfying identifiability criteria can be constructed and used in the factorization of gene expression data matrices.Using high throughput DNA binding data for transcription factors, 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 posttranscriptional regulations.