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)