kinaseSubstratePred {PhosR} | R Documentation

## kinaseSubstratePred

### Description

A machine learning approach for predicting specific kinase for a given substrate. This prediction framework utilise adaptive sampling.

### Usage

    kinaseSubstratePred(
        phosScoringMatrices,
        ensembleSize = 10,
        top = 50,
        cs = 0.8,
        inclusion = 20,
        iter = 5,
        verbose = TRUE
    )

### Arguments

`phosScoringMatrices` | An output of kinaseSubstrateScore.
`ensembleSize` | An ensemble size.
`top` | a number to select top kinase substrates.
`cs` | Score threshold.
`inclusion` | A minimal number of substrates required for a kinase to be selected.
`iter` | A number of iterations for adaSampling.
`verbose` | Default to `TRUE` to show messages during the progress. All messages will be suppressed if set to `FALSE`

### Value

Kinase prediction matrix

### Examples

    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)

    rownames(L6.phos.std) <- paste0(GeneSymbol(ppe), ";", Residue(ppe),
        Site(ppe), ";")[idx]

    L6.phos.seq <- Sequence(ppe)[idx]

    L6.matrices <- kinaseSubstrateScore(PhosphoSite.mouse, L6.phos.std,
        L6.phos.seq, numMotif = 5, numSub = 1)
    set.seed(1)
    L6.predMat <- kinaseSubstratePred(L6.matrices, top=30)

[Package PhosR version 1.2.0 Index]