standardise {PhosR} | R Documentation

## Standardisation

### Description

Standardisation by z-score transformation.

### Usage

    standardise(mat)

### Arguments

`mat` | a matrix (or a PhosphoExperiment object) with rows correspond to phosphosites and columns correspond to samples.

### Value

A standardised matrix

### Examples

    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)

[Package PhosR version 1.2.0 Index]