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An automated resource for enhanced differential analysis


Differential Shannon entropy (DSE) and differential coefficient of variation (DCV) have proven to be effective complements to differential expression (DE) in the analysis of gene co-expression data[1]. Because DSE and DCV measure difference in variability, rather than mere difference in magnitude, they can often identify significant changes in gene activity not reflected in mere mean expression level.

Materials and methods

Thus, we have devised a general purpose, easy-to-use R package to calculate DSE and DCV. Dubbed Entropy Explorer, this package operates on two numeric matrices with identically labeled rows, such as case/control transcriptomic data. All functionality has been wrapped into one routine. With a single procedure call a user may select a metric, whether to display that metric, its raw and adjusted p-value, or both, whether to sort by metric or raw or adjusted p-value, and how many of the most highly ranked results to display.


  1. Wang K, Phillips CA, Rogers GL, Barrenas F, Benson M, Langston MA: Differential Shannon Entropy and Differential Coefficient of Variation: Alternatives and Augmentations to Differential Expression in the Search for Disease-Related Genes. International Journal of Computational Biology and Drug Design. 2014, 183-194.

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Correspondence to Michael A Langston.

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Wang, K., Phillips, C.A., Saxton, A.M. et al. An automated resource for enhanced differential analysis. BMC Bioinformatics 16 (Suppl 15), P20 (2015).

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