Signalomes {PhosR} | R Documentation

## PhosR Signalomes

### Description

A function to generate signalomes

### Usage

    Signalomes(KSR, predMatrix, exprsMat, KOI, threskinaseNetwork=0.9,
    signalomeCutoff=0.5, module_res = NULL, filter = FALSE, verbose = TRUE)

### Arguments

`KSR` | kinase-substrate relationship scoring results
`predMatrix` | output of kinaseSubstratePred function
`exprsMat` | a matrix with rows corresponding to phosphosites and columns corresponding to samples
`KOI` | a character vector that contains kinases of interest for which expanded signalomes will be generated
`threskinaseNetwork` | threshold used to select interconnected kinases for the expanded signalomes
`signalomeCutoff` | threshold used to filter kinase-substrate relationships
`module_res` | parameter to select number of final modules
`filter` | parameter to filter modules with only few proteins
`verbose` | Default to `TRUE` to show messages during the progress. All messages will be suppressed if set to `FALSE`

### Value

A list of 3 elements. `Signalomes`, `proteinModules` and `kinaseSubstrates`

### Examples

    data('phospho_L6_ratio_pe')
    data('SPSs')
    data('PhosphoSitePlus')

    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.RUV = RUVphospho(
        SummarizedExperiment::assay(phospho.L6.ratio.pe, "Quantification"),
        M = design, k = 3, ctl = ctl)

    phosphoL6 = phospho.L6.ratio.RUV

    # 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)
    idx <- match(rownames(L6.phos.std), rownames(phospho.L6.ratio.pe))
    rownames(L6.phos.std) <- L6.sites[idx]

    L6.phos.seq <- Sequence(phospho.L6.ratio.pe)[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)

    kinaseOI = c('PRKAA1', 'AKT1')

    Signalomes_results <- Signalomes(KSR=L6.matrices,
                                     predMatrix=L6.predMat,
                                     exprsMat=L6.phos.std,
                                     KOI=kinaseOI)

[Package PhosR version 1.2.0 Index]