The gene regulatory network of mESC differentiation: a benchmark for reverse engineering methods

A large body of data have accumulated that characterize the gene regulatory networkof stem cells. Yet, a comprehensive and integrative understanding of this complex network is lacking. Network reverse engineeringmethods that use transcriptome data to derive these networks may help to uncover the topology in an unbiased way. Many methods exist that use co-expression to reconstruct networks. However, it remains unclear how these methods perform in the context of stem cell differentiation, as most systematic assessments have been made for regulatory networks of unicellular organisms. Here, we report a systematic benchmark of different reverse engineeringmethods against functional data. We show that network pruning is critical for reconstruction performance. We also find that performance is similar for algorithms that use different co-expression measures, i.e. mutual information or correlation. In addition, different methods yield very different network topologies, highlighting the challenge of interpretin...