## ---- warning=FALSE, message = FALSE-------------------------------------
library('TreeExp')
## ---- warning = FALSE, message = FALSE-----------------------------------
data('tetraexp')
## ---- warning = FALSE, message = FALSE-----------------------------------
species.group <- c("Human", "Chimpanzee", "Bonobo", "Gorilla", "Orangutan",
"Macaque", "Mouse", "Opossum", "Platypus")
### all mammalian species
inv.corr.mat <- corrMatInv(tetraexp.objects, taxa = species.group, subtaxa = "Brain")
## ---- warning = FALSE, message = FALSE-----------------------------------
brain.exptable <- exptabTE(tetraexp.objects, taxa = species.group, subtaxa = "Brain" )
head(brain.exptable)
## ---- warning = FALSE, message = FALSE-----------------------------------
gamma.paras <- estParaGamma(brain.exptable, inv.corr.mat)
cat(gamma.paras)
## ---- warning = FALSE, message = FALSE, fig.height=4, fig.width=6--------
brain.Q <- estParaQ(brain.exptable, corrmatinv = inv.corr.mat)
# with prior expression values and inversed correlation matrix
brain.post<- estParaWBayesian(brain.Q, gamma.paras)
brain.W <- brain.post$exp # posterior expression values
brain.CI <- brain.post$ci95 # posterior expression 95% confidence interval
names(brain.W) <- rownames(brain.exptable)
head(sort(brain.W, decreasing = T)) #check a few genes with highest seletion pressure
plot(density(brain.W))
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