RNentropy-package | R Documentation |
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>.
Federico Zambelli [cre] (<https://orcid.org/0000-0003-3487-4331>), Giulio Pavesi [aut] (<https://orcid.org/0000-0001-5705-6249>)
Maintainer: Federico Zambelli <federico.zambelli@unimi.it>
doi = 10.1093/nar/gky055 doi = 10.1007/978-1-0716-1307-8_6
#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)
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