  Gene expression is a readily-observed quantification of transcriptional
activity and cellular state that enables the recovery of the relationships
between regulators and their target genes. Reconstructing transcriptional
regulatory networks from gene expression data is a problem that has attracted
much attention, but previous work often makes the simplifying (but unrealistic)
assumption that regulator activity is represented by mRNA levels. We use a
latent tree graphical model to analyze gene expression without relying on
transcription factor expression as a proxy for regulator activity. The latent
tree model is a type of Markov random field that includes both observed gene
variables and latent (hidden) variables, which factorize on a Markov tree.
Through efficient unsupervised learning approaches, we determine which groups
of genes are co-regulated by hidden regulators and the activity levels of those
regulators. Post-processing annotates many of these discovered latent variables
as specific transcription factors or groups of transcription factors. Other
latent variables do not necessarily represent physical regulators but instead
reveal hidden structure in the gene expression such as shared biological
function. We apply the latent tree graphical model to a yeast stress response
dataset. In addition to novel predictions, such as condition-specific binding
of the transcription factor Msn4, our model recovers many known aspects of the
yeast regulatory network. These include groups of co-regulated genes,
condition-specific regulator activity, and combinatorial regulation among
transcription factors. The latent tree graphical model is a general approach
for analyzing gene expression data that requires no prior knowledge of which
possible regulators exist, regulator activity, or where transcription factors
physically bind.
