RNentropy-package {RNentropy}

Entropy Based Method for the Detection of Significant Variation in Gene Expression Data

Description

An 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 et al. (2018) <doi:10.1093/nar/gky055>.

Author(s)

NA

Maintainer: Federico Zambelli <[e-mail]>

References

doi = 10.1093/nar/gky055 doi = 10.1007/978-1-0716-1307-8_6

Examples

#load expression values and experiment design
data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN). 
Results <- RN_calc(RN_Brain_Example_tpm[1:10000,], 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)

#load expression values and experiment design
data("RN_BarresLab_FPKM", "RN_BarresLab_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN)
Results_B <- RN_calc(RN_BarresLab_FPKM[1:10000,], RN_BarresLab_design)
#select only genes with significant changes of expression
Results_B <- RN_select(Results_B)
#Compute the Point Mutual information matrix
Results_B <- RN_pmi(Results_B)