Predicting gene regulatory network interactions withhigh certainty using the Linear Profile Likelihood -LiPLike

Abstract

Gene regulatory network inference typically revolves around the use of expression data to predict the most likely underlying network, and these predictions of transcription factor-target regulations have a low accuracy and are full of false positives. This low accuracy is in part due to a high degree of correlation between regulatory transcription factors. However, most gene regulatory network inference methods aim at predicting the maximizing a likelihood function, overlooking the degrees of freedom in almost equally likely alternative solutions. This chapter introduces the LInear Profile LIKE-lihood, LiPLike, which controls for this uncertainty by only selecting network interactions that are uniquely needed to explain the data. If there exists an alternative solution B to an interaction prediction A, LiPLike predicts neither A nor B, whereas state-of-the-art inference methods typically predict either A or B, or both. LiPLike can be used as a standalone inference method, or to stratify the predictions of alternative inference methods into sets of low and high confidence.