A ratiometric-based measure of gene co-expression

Abstract

Background: Gene co-expression analysis has previously been based on measures that include correlation coefficients and mutual information, as well as newcomers such as MIC. These measures depend primarily on the degree of association between the RNA levels of two genes and to a lesser extent on their variability. They focus on the similarity of expression value trajectories that change in like manner across samples. However there are relationships of biological interest for which these classical measures are expected to be insensitive. These include genes whose expression levels are ratiometrically stable and genes whose variance is tightly constrained. Large-scale studies of relatively homogeneous samples, including single cell RNA-seq, are experimental settings in which such relationships might be especially pertinent. Results: We develop and implement a ratiometric approach for detecting gene associations (abbreviated RA). It is based on the coefficient of variation of the measured expression ratio of each pair of genes. We apply it to a collection of lymphoblastoid RNA-seq data from the 1000 Genomes Project Consortium, a typical sample set with high overall homogeneity. RA is a selective method, reporting in this case ~1/4 of all possible gene pairs, yet these relationships include a distilled picture of biological relationships previously found by other methods. In addition, RA reveals expression relationships that are not detected by traditional correlation and mutual information methods. We also analyze data from individual lymphoblastoid cells and show that desirable properties of the RA method extend to single-cell RNA-seq. Conclusion: We show that our ratiometric method identifies biologically significant relationships that are often missed or low-ranked by conventional association-based methods when applied to a relatively homogenous dataset. The results open new questions about the regulatory mechanisms that produce strong RA relationships. RA is scalable and potentially well suited for the analysis of thousands of bulk-RNA or single-cell transcriptomes.

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