Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

Abstract

Gene expression is controlled by networks of transcription factors and regulators, but the structure of these networks is as yet poorly understood and is thus inferred from data. Recent work has shown the efficacy of information theoretical approaches for network reconstruction from single cell transcriptomic data. Such methods use information to estimate dependence between every pair of genes in the dataset, then edges are inferred between top-scoring pairs. Dependence, however, does not indicate significance, and the definition of “top-scoring” is often arbitrary and a priori related to expected network size. This makes comparing networks across datasets difficult, because networks of a si..