kinaseSubstrateProfile {PhosR} | R Documentation |
This function generates substrate profiles for kinases that have one or more substrates quantified in the phosphoproteome data.
kinaseSubstrateProfile(substrate.list, mat)
substrate.list |
a list of kinases with each element containing an array of substrates. |
mat |
a matrix with rows correspond to phosphosites and columns correspond to samples. |
Kinase profile list.
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=';'))}) ks.profile.list <- kinaseSubstrateProfile(PhosphoSite.mouse, L6.phos.std)