Signalomes {PhosR} | R Documentation |
A function to generate signalomes
Signalomes(KSR, predMatrix, exprsMat, KOI, threskinaseNetwork=0.9, signalomeCutoff=0.5, module_res = NULL, filter = FALSE, verbose = TRUE)
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 |
module_res |
parameter to select number of final modules |
filter |
parameter to filter modules with only few proteins |
verbose |
Default to |
A list of 3 elements.
Signalomes
, proteinModules
and kinaseSubstrates
data('phospho_L6_ratio_pe') data('SPSs') data('PhosphoSitePlus') grps = gsub('_.+', '', colnames(phospho.L6.ratio.pe)) # Construct a design matrix by condition design = model.matrix(~ grps - 1) # phosphoproteomics data normalisation using RUV L6.sites = paste(sapply(GeneSymbol(phospho.L6.ratio.pe), function(x)paste(x)), ";", sapply(Residue(phospho.L6.ratio.pe), function(x)paste(x)), sapply(Site(phospho.L6.ratio.pe), function(x)paste(x)), ";", sep = "") ctl = which(L6.sites %in% SPSs) phospho.L6.ratio.RUV = RUVphospho( SummarizedExperiment::assay(phospho.L6.ratio.pe, "Quantification"), M = design, k = 3, ctl = ctl) phosphoL6 = phospho.L6.ratio.RUV # filter for up-regulated phosphosites phosphoL6.mean <- meanAbundance(phosphoL6, grps = grps) aov <- matANOVA(mat=phosphoL6, grps=grps) phosphoL6.reg <- phosphoL6[(aov < 0.05) & (rowSums(phosphoL6.mean > 0.5) > 0),, drop = FALSE] L6.phos.std <- standardise(phosphoL6.reg) idx <- match(rownames(L6.phos.std), rownames(phospho.L6.ratio.pe)) rownames(L6.phos.std) <- L6.sites[idx] L6.phos.seq <- Sequence(phospho.L6.ratio.pe)[idx] 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)