University of Washington - Department of Statistics
We have developed LeTICE, an algorithm for learning a transcriptional regulatory network from ChIP-chip location and expression data. The network is specified by a binary matrix of transcription factor â€“ gene interactions which partitions the genes into a collection of modules (groups of genes regulated by the same TFs) and a background (a group of genes which do not belong to any module). We define a likelihood of a network given location and expression data and then search for the network optimizing the likelihood using numerical optimization.
We have applied LeTICE to location and expression data from yeast cells grown in rich media to learn the transcriptional regulatory network specific to the yeast cell cycle. It found 20 condition specific transcription factors and 22 modules, each of which is highly represented with functions related to particular phases of cell cycle regulation. We have also extracted a sub-network consisting only of transcription factors; it is almost identical to the previously suggested cell cycle TF network. Finally, we have compared our modules with those obtained using three alternative methods (GRAM, ReMoDiscovery, COGRIM) and with modules constructed from expression data only and from location data only. The genes in the modules obtained by LeTICE have the highest functional similarity, as estimated using Gene Ontology annotations.