NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles

Abstract

Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.

Gap-com: general model selection criterion for sparse undirected gene networks with nontrivial community structure

“…Other measures (coefficients) one can consider which are capable of modeling both linear and nonlinear dependencies between genes are, e.g. Spearman’s rank correlation, Kendall rank correlation coefficient, the distance correlation ( Székely et al 2007 ), mutual information (see, Husain et al 2020 , and the references therein), and the maximal information coefficient ( Reshef et al 2011 ). See also Kontio et al (2020) .…”

EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks

“…Conventional algorithms such as weighted GCN analysis (WGCNA) ( Langfelder and Horvath 2008 ) are limited by the fact that they can only identify linearly correlated relationships while struggling to differentiate between intrinsic signal and extrinsic noise. To this end, algorithms such as Knowledge Independent Network Construction (KINC) ( Ficklin et al 2017 ) and NetExtractor ( Husain et al 2020 ) were developed to capture linear and potentially nonlinear relationships that have traditionally been ignored. Integration of these nonlinear relationships, however, greatly increases the computational complexity of constructing relationship networks.…”