SurprisalAnalysis - Information Theoretic Analysis of Gene Expression Data
Implements Surprisal Analysis for gene expression data
such as RNA-seq or microarray experiments. Surprisal Analysis
is an information-theoretic method that decomposes gene
expression data into a baseline state and constraint-associated
deviations, which helps to capture coordinated gene expression
patterns under different biological conditions. References
where Surprisal analysis has been used for analyzing gene
expression data (using the same methodology provided within
this R package) are Kravchenko-Balasha et al (2014)
<doi:10.1371/journal.pone.0108549>, Zadran et al. (2014)
<doi:10.1073/pnas.1414714111>, Su et al. (2019)
<doi:10.1371/journal.pcbi.1007034>, Bogaert et al. (2018)
<doi:10.1371/journal.pone.0195142>.
