kinaseSubstrateScore: Kinase substrate scoring

In PengyiYang/PhosR: A set of methods and tools for comprehensive analysis of phosphoproteomics data

Description

This function generates substrate scores for kinases that pass filtering based on both motifs and dynamic profiles

Usage

kinaseSubstrateScore(substrate.list, mat, seqs, numMotif = 5, numSub = 1)

Arguments

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.
seqs	an array containing aa sequences surrounding each of all phosphosites. Each sequence has length of 15 (-7, p, +7).
numMotif	minimum number of sequences used for compiling motif for each kinase. Default is 5.
numSub	minimum number of phosphosites used for compiling phosphorylation profile for each kinase. Default is 1.

Value

A list of 4 elements. motifScoreMatrix, profileScoreMatrix, combinedScoreMatrix, ksActivityMatrix (kinase activity matrix) and their weights.

Examples

data('phospho_L6_ratio')
data('SPSs')
data('PhosphoSitePlus')

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=';'))})

L6.phos.seq <- sapply(strsplit(rownames(phosphoL6.reg), '~'),
                    function(x)x[4])

L6.matrices <- kinaseSubstrateScore(PhosphoSite.mouse, L6.phos.std,
    L6.phos.seq, numMotif = 5, numSub = 1)

PengyiYang/PhosR documentation built on June 21, 2020, 8:37 a.m.