minmax {PhosR} | R Documentation |
Perform a minmax standardisation to scale data into 0 to 1 range
minmax(mat)
mat |
a matrix with rows correspond to phosphosites and columns correspond to condition |
Minmax standardised 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) ks.profile.list <- kinaseSubstrateProfile(PhosphoSite.mouse, L6.phos.std) data(KinaseMotifs) numMotif = 5 numSub = 1 motif.mouse.list.filtered <- motif.mouse.list[which(motif.mouse.list$NumInputSeq >= numMotif)] ks.profile.list.filtered <- ks.profile.list[which(ks.profile.list$NumSub >= numSub)] # scoring all phosphosites against all motifs motifScoreMatrix <- matrix(NA, nrow=nrow(L6.phos.std), ncol=length(motif.mouse.list.filtered)) rownames(motifScoreMatrix) <- rownames(L6.phos.std) colnames(motifScoreMatrix) <- names(motif.mouse.list.filtered) L6.phos.seq <- Sequence(ppe)[idx] # extracting flanking sequences seqWin = mapply(function(x) { mid <- (nchar(x)+1)/2 substr(x, start=(mid-7), stop=(mid+7)) }, L6.phos.seq) print('Scoring phosphosites against kinase motifs:') for(i in seq_len(length(motif.mouse.list.filtered))) { motifScoreMatrix[,i] <- frequencyScoring(seqWin, motif.mouse.list.filtered[[i]]) cat(paste(i, '.', sep='')) } motifScoreMatrix <- minmax(motifScoreMatrix)