Robust gene coexpression networks using signed distance correlation

Abstract

Motivation: Even within well studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information. Results: We introduce the concept of signed distance correlation as a measure of dependency between two variables, and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods such as Pearson correlation. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson or Spearman correlations. Code availability: https://github.com/javier-pardodiaz/sdcorGCN.

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

“…While this cut-off was well-suited for our large-scale approach, in the future, less restrictive cut-offs could be used to generate co-expression networks as well as other methods. For instance, Pardo-Diaz et al . (2021) recently presented a novel method that adds directionality into the co-expression network.…”

COGENT: evaluating the consistency of gene co-expression networks

“…A full worked example can be found in SI Section 5 and in the COGENT tutorial. COGENT has also been used to assess signed distance correlation as a measure of gene co-expression (Pardo-Diaz et al, 2020). This application further shows that network construction methods prioritised by COGENT also capture more protein-protein interaction data than methods which were not prioritised.…”

COGENT: evaluating the consistency of gene co-expression networks

“…A full worked example can be found in the SI. COGENT has also been used to assess signed distance correlation as a measure of gene co-expression (Pardo-Diaz et al, 2020).…”