FocusHeuristics – expression-data-driven network optimization and disease gene prediction

Abstract

To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.

Capturing context-specific regulation in molecular interaction networks

“…However, this estimate does not account for the covariance between genes j and k, distorting the significance of the findings. This approach was subsequently abandoned in Ernst et al (2017) for a strictly quantile-based analysis.…”

“…FocusHeuristics is a quantile-based method to highlight the differentially active components of a gene expression network in order to focus on phenotypically-relevant subnetworks (Ernst et al, 2017). The authors define three scores on the edges and vertices of the network: (i) log-fold change ∆ log(x i j ), (ii) differential link score L( j, k), and (iii) interaction link score min{log(x 0 j + x 0k ), log(x 1 j + x 1k )}.…”

“…Let G be the corresponding GRN with vertex j ∈ V (G) corresponding to gene expression x i j . For every gene pair ( j, k) ∈ E(G), the differential link score (DLS) (Ernst et al (2017)) is defined as…”

“…More recent methods profit from prior topological knowledge to constrain inference in network regulation. Specifically, there is an emphasis on context-specific network regulation (Warsow et al, 2010;Woo et al, 2015;Ernst et al, 2017;Hill et al, 2017). Numerous network-based regularization methods profiting from previous studies have emerged to perform variable selection and to obtain biologically meaningful predictors (Li and Li, 2008;Sun and Wang, 2012;Avey et al, 2017).…”

“…With regulatory coherence, it becomes clear that a GRN represents a collection of potential interactions, which are realized in specific contexts. We present a new perspective on the differential link score (Ernst et al, 2017) as a measure of regulatory coherence, along with a methodology to ascertain coherence of differentially active networks. These realized interactions form the coherent subnetwork.…”