Signalomes: PhosR Signalomes

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

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)

PYangLab/PhosR documentation built on Feb. 3, 2024, 3:29 a.m.