Abstract
Two-level gene regulatory networks consist of the transcription factors (TFs) in the top level and their regulated genes in the second level. The expression profiles of the regulated genes are the observed high-throughput data given by experiments such as microarrays. The activity profiles of the TFs are treated as hidden variables as well as the connectivity matrix that indicates the regulatory relationships of TFs with their regulated genes. Factor analysis (FA) as well as other methods, such as the network component algorithm, has been suggested for reconstructing gene regulatory networks and also for predicting TF activities. They have been applied to E. coli and yeast data with the assumption that these datasets consist of identical and independently distributed samples. Thus, the main drawback of these algorithms is that they ignore any time correlation existing within the TF profiles. In this paper, we extend previously studied FA algorithms to include time correlation within the transcription factors. At the same time, we consider connectivity matrices that are sparse in order to capture the existing sparsity present in gene regulatory networks. The TFs activity profiles obtained by this approach are significantly smoother than profiles from previous FA algorithms. The periodicities in profiles from yeast expression data become prominent in our reconstruction. Moreover, the strength of the correlation between time points is estimated and can be used to assess the suitability of the experimental time interval.
Highlights
Genes are transcribed into mRNAs which in turn are translated into proteins
In a recent paper, [4], we examined the suitability of five Factor analysis (FA) algorithms for reconstructing both gene regulatory networks and transcription factors (TFs) activity profiles
We showed that FA faithfully reconstructs TF activity profiles as other more widely known reconstruction approaches do such as network component analysis (NCA) [5] and a piecewise leastsquare algorithm [8]
Summary
Genes are transcribed into mRNAs which in turn are translated into proteins. Some of these proteins activate or inhibit, as transcription factors (TFs), the transcription of a number of other genes creating a complex gene regulatory network. Some progress has been made with measurements of protein abundance by flow cytometry [1] following a dozen or so proteins of interest which need to be identified in advance Still, such experiments are less available than gene expression experiments and cannot compete in terms of the number of tracked genes. Current approaches that use microarray experiments make a strong assumption: the protein levels are proportional to the mRNA levels This assumption is not necessarily true due to the complexity of transcription, translation, and posttranslation modification. Twolevel networks have been studied with hidden profiles of the transcription factors at the top level and the observed expression levels of the regulated genes at the lower level. Some of these studies [2,3,4] are concerned with factor analysis algorithms
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