kinaseSubstratePred {PhosR} | R Documentation |
A machine learning approach for predicting specific kinase for a given substrate. This prediction framework utilise adaptive sampling.
kinaseSubstratePred( phosScoringMatrices, ensembleSize = 10, top = 50, cs = 0.8, inclusion = 20, iter = 5, verbose = TRUE )
phosScoringMatrices |
An output of kinaseSubstrateScore. |
ensembleSize |
An ensemble size. |
top |
a number to select top kinase substrates. |
cs |
Score threshold. |
inclusion |
A minimal number of substrates required for a kinase to be selected. |
iter |
A number of iterations for adaSampling. |
verbose |
Default to |
Kinase prediction matrix
data('phospho_L6_ratio_pe') data('SPSs') data('PhosphoSitePlus') ppe <- phospho.L6.ratio.pe sites = paste(sapply(GeneSymbol(ppe), function(x)x),";", sapply(Residue(ppe), function(x)x), sapply(Site(ppe), function(x)x), ";", sep = "") grps = gsub("_.+", "", colnames(ppe)) design = model.matrix(~ grps - 1) ctl = which(sites %in% SPSs) ppe = RUVphospho(ppe, M = design, k = 3, ctl = ctl) phosphoL6 = SummarizedExperiment::assay(ppe, "normalised") # filter for up-regulated phosphosites phosphoL6.mean <- meanAbundance(phosphoL6, grps = grps) aov <- matANOVA(mat=phosphoL6, grps = grps) idx <- (aov < 0.05) & (rowSums(phosphoL6.mean > 0.5) > 0) phosphoL6.reg <- phosphoL6[idx, ,drop = FALSE] L6.phos.std <- standardise(phosphoL6.reg) rownames(L6.phos.std) <- paste0(GeneSymbol(ppe), ";", Residue(ppe), Site(ppe), ";")[idx] L6.phos.seq <- Sequence(ppe)[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)