This is the implementation of a method based on information theory devised for the identification of genes showing a significant variation of expression across multiple conditions. Given expression estimates from any number of RNA-Seq samples and conditions it identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. Zambelli F. et al. (2018).
A detailed walk-through on how to use RNentropy is available at Zambelli F., Pavesi G. (2021)
You can install the development version of RNentropy like so:
install.packages("RNentropy")This is a basic example showing how to use RNentropy. Please see Zambelli F., Pavesi G. (2021) for more info.
library(RNentropy)
# basic example code
##load expression values and experiment design
data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
#Run RNentropy
Results <- RN_calc(RN_Brain_Example_tpm, RN_Brain_Example_design)
#select only genes with significant changes of expression
Results <- RN_select(Results)
#Compute the Point Mutual information Matrix
Results <- RN_pmi(Results)