Signalomes {PhosR}R Documentation

PhosR Signalomes

Description

A function to generate signalomes

Usage

Signalomes(KSR, predMatrix, exprsMat, KOI, threskinaseNetwork=0.9,
signalomeCutoff=0.5)

Arguments

KSR

kinase-substrate relationship scoring results

predMatrix

output of kinaseSubstratePred function

exprsMat

a matrix with rows corresponding to phosphosites and columns corresponding to samples

KOI

a character vector that contains kinases of interest for which expanded signalomes will be generated

threskinaseNetwork

threshold used to select interconnected kinases for the expanded signalomes

signalomeCutoff

threshold used to filter kinase-substrate relationships

Value

A list of 3 elements. Signalomes, proteinModules and kinaseSubstrates

Examples


data('phospho_L6_ratio')
data('SPSs')

grps = gsub('_.+', '', colnames(phospho.L6.ratio))

# Cleaning phosphosite label
phospho.site.names = rownames(phospho.L6.ratio)
L6.sites = gsub(' ', '', sapply(strsplit(rownames(phospho.L6.ratio), '~'),
                                function(x){paste(toupper(x[2]), x[3], '',
                                                sep=';')}))
phospho.L6.ratio = t(sapply(split(data.frame(phospho.L6.ratio), L6.sites),
                            colMeans))
phospho.site.names = split(phospho.site.names, L6.sites)

# Construct a design matrix by condition
design = model.matrix(~ grps - 1)

# phosphoproteomics data normalisation using RUV
ctl = which(rownames(phospho.L6.ratio) %in% SPSs)
phospho.L6.ratio.RUV = RUVphospho(phospho.L6.ratio, M = design, k = 3,
                                ctl = ctl)

phosphoL6 = phospho.L6.ratio.RUV
rownames(phosphoL6) = phospho.site.names

# filter for up-regulated phosphosites
phosphoL6.mean <- meanAbundance(phosphoL6, grps = gsub('_.+', '',
                                colnames(phosphoL6)))
aov <- matANOVA(mat=phosphoL6, grps=gsub('_.+', '', colnames(phosphoL6)))
phosphoL6.reg <- phosphoL6[(aov < 0.05) &
                        (rowSums(phosphoL6.mean > 0.5) > 0),,drop = FALSE]
L6.phos.std <- standardise(phosphoL6.reg)
rownames(L6.phos.std) <- sapply(strsplit(rownames(L6.phos.std), '~'),
    function(x){gsub(' ', '', paste(toupper(x[2]), x[3], '', sep=';'))})

L6.phos.seq <- sapply(strsplit(rownames(phosphoL6.reg), '~'),
                    function(x)x[4])

L6.matrices <- kinaseSubstrateScore(PhosphoSite.mouse, L6.phos.std,
    L6.phos.seq, numMotif = 5, numSub = 1)
set.seed(1)
L6.predMat <- kinaseSubstratePred(L6.matrices, top=30)

kinaseOI = c('PRKAA1', 'AKT1')

Signalomes_results <- Signalomes(KSR=L6.matrices,
                                predMatrix=L6.predMat,
                                exprsMat=L6.phos.std,
                                KOI=kinaseOI)

[Package PhosR version 1.0.0 Index]