aee: Generate an inversed variance matrix from expression profile...

In HangRuan1912/phyExp: the R package for analyzing expression evolution based on RNA-seq data

Description

This function generate an inversed variance matrix from expression profiles of one-to-one orthologous genes across species

This function esitmates ancestral expression profile and related statistical uncertainty

Usage

varMatInv(objects, phy, taxa = "all", subtaxa)

aee(x, phy, mat, select = c("all", "descendents"), CI = TRUE)

Arguments

objects	a vector of objects of class taxonExp or an object of class taxaExp
phy	an rooted expression character tree
taxa	oen single character or a vector of characters sepcifying taxa to generate an inversed variance matrix. If one single character "all" is given, all the taxa included in the taxaExp will be matched and included ("all" by default).
subtaxa	one single character specifying sub taxa to be included in generating an inversed variance matrix
x	a vector of known expression profile, preferably log-transformed expression levels (e.g. log RPKM)
mat	a matrix generated from "varMatInv" function
select	indicate if descendents of the node or all tips should be used
CI	a logical specifying whether to return the 95 of the estimated ancestral expression levels
phy	a phylogenetic tree in the form of object "phylo"

Value

returns an inversed variance matrix

returns a list containing estimated ancestral expression profile as well as other requested parameters

Examples

data('tetraexp')
dismat <- expdist(tetraexp.objects, taxa = "all", subtaxa = "Brain", method = "sou")
exp_tree <- NJ(dismat)
exp_tree <- root(exp_tree, outgroup = "Chicken_Brain", resolve.root = T)
exp_tree <- no0br(exp_tree)
var_mat <- varMatInv(objects = tetraexp.objects,phy = exp_tree,taxa = "all", subtaxa = "Brain")
exp_table <- exptabTE(tetraexp.objects, taxa = "all", subtaxa = "Brain")
exp_one <- aee(exp_table[1,], exp_tree, var_mat)
exp_tree$node.label <- exp_one$est
plot(exp_tree)

HangRuan1912/phyExp documentation built on July 15, 2019, 9:01 p.m.