standardise {PhosR} | R Documentation |
Standardisation by z-score transformation.
standardise(mat)
mat |
a matrix (or a PhosphoExperiment object) with rows correspond to phosphosites and columns correspond to samples. |
A standardised matrix
data('phospho_L6_ratio_pe') data('SPSs') 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.pe = RUVphospho(phospho.L6.ratio.pe, M = design, k = 3,ctl = ctl) phosphoL6 = SummarizedExperiment::assay(phospho.L6.ratio.pe, "normalised") # 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)