| Signalomes {PhosR} | R Documentation |
A function to generate signalomes
Signalomes(KSR, predMatrix, exprsMat, KOI, threskinaseNetwork=0.9, signalomeCutoff=0.5)
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 |
A list of 3 elements.
Signalomes, proteinModules and kinaseSubstrates
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)