Identification of the minimal connected network of transcription factors by transcriptomic and genomic data integration.

Thanks to high-throughput experiments, biological conditions can be investigated at both the entire genomic and transcriptomic levels. In addition, protein-protein interaction (PPI) data are widely available for well-studied organisms, such as human. In this chapter, we will present an integrative approach that makes use of these data to find the PPI module involving the key regulated transcription factors shared by a number of given conditions. These conditions could be for instance different cancer types. Briefly, for the studied conditions, we need to identify commonly affected chromosomal regions subjected to copy number alterations together with the identification of differentially expressed list of genes in each condition. Transcription factor activity will be inferred from these regulated gene lists. Then, we will define TFs, for which the activity could be explained by an associative effect of both loci copy number alteration and gene expression levels of their coding genes. PPI networks could be mined, afterwards, using appropriate algorithms to find the significant module that connect those TFs together. This module could be viewed as the minimal connected network of TFs, the regulation of which is shared between the investigated conditions.