- Poster presentation
- Open access
- Published:
An automated resource for enhanced differential analysis
BMC Bioinformatics volume 16, Article number: P20 (2015)
Background
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.
References
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Wang, K., Phillips, C.A., Saxton, A.M. et al. An automated resource for enhanced differential analysis. BMC Bioinformatics 16 (Suppl 15), P20 (2015). https://doi.org/10.1186/1471-2105-16-S15-P20
Published:
DOI: https://doi.org/10.1186/1471-2105-16-S15-P20