IRSnorm: Batch Correction by Internal Reference Scale

In crukci-bioinformatics/qPLEXanalyzer: Tools for quantitative proteomics data analysis

Batch Correction by Internal Reference Scale

Description

Performs batch correction on multiple runs using an Internal Reference Scale sample.

Usage

IRSnorm(MSnSetObj, IRSname = "RefPool", groupingColumn = "Plex")

Arguments

MSnSetObj	MSnSet; an object of class MSnSet
IRSname	character; name of the Reference group within the SampleGroup column
groupingColumn	character; the pData(MSnSetObj) column name used to define batches; default="Plex"

Details

The Internal Reference Scale sample (IRS) should ideally be representative of the entire proteome detectable across all sample in the experiment, e.g. a pooled sample made up of aliquots of protein from all samples. The IRS is then run and measured in each TMT experiment. The normalization procedure makes measurements of the IRS from different TMT batches exactly the same, and puts all of the reporter ions on the same "intensity scale". The argument 'IRSname' is used to define the name of the Reference group within the SampleGroup column. The argument "groupingColumn" takes one of the column of pData(MSnSetObj) to define separate batches to correct; the default variable name is "Plex".

Value

An object of class MSnSet (see MSnSet-class)

Examples

data(human_anno)
data(ER_ARID1A_KO_MCF7)
MSnset_SET1 <- convertToMSnset(ER_ARID1A_KO_MCF7$intensities_Set1,
                               metadata=ER_ARID1A_KO_MCF7$metadata_Set1,
                               indExpData=c(7:15),
                               Sequences=2,
                               Accessions=6)
MSnset_SET2 <- convertToMSnset(ER_ARID1A_KO_MCF7$intensities_Set2,
                               metadata=ER_ARID1A_KO_MCF7$metadata_Set2,
                               indExpData=c(7:15),
                               Sequences=2,
                               Accessions=6)
MSnset_SET3 <- convertToMSnset(ER_ARID1A_KO_MCF7$intensities_Set3,
                               metadata=ER_ARID1A_KO_MCF7$metadata_Set3,
                               indExpData=c(7:15),
                               Sequences=2,
                               Accessions=6)
MSnset_SET4 <- convertToMSnset(ER_ARID1A_KO_MCF7$intensities_Set4,
                               metadata=ER_ARID1A_KO_MCF7$metadata_Set4,
                               indExpData=c(7:14),
                               Sequences=2,
                               Accessions=6)
MSnset_SET5 <- convertToMSnset(ER_ARID1A_KO_MCF7$intensities_Set5,
                               metadata=ER_ARID1A_KO_MCF7$metadata_Set5,
                               indExpData=c(7:15),
                               Sequences=2,
                               Accessions=6)
MSnset_SET1_norm <- normalizeScaling(MSnset_SET1, median)
MSnset_SET2_norm <- normalizeScaling(MSnset_SET2, median)
MSnset_SET3_norm <- normalizeScaling(MSnset_SET3, median)
MSnset_SET4_norm <- normalizeScaling(MSnset_SET4, median)
MSnset_SET5_norm <- normalizeScaling(MSnset_SET5, median)
MSnset_SET1_Pnorm <- summarizeIntensities(MSnset_SET1_norm, sum, human_anno)
MSnset_SET2_Pnorm <- summarizeIntensities(MSnset_SET2_norm, sum, human_anno)
MSnset_SET3_Pnorm <- summarizeIntensities(MSnset_SET3_norm, sum, human_anno)
MSnset_SET4_Pnorm <- summarizeIntensities(MSnset_SET4_norm, sum, human_anno)
MSnset_SET5_Pnorm <- summarizeIntensities(MSnset_SET5_norm, sum, human_anno)
MSnset_SET1_Pnorm <- updateSampleNames(updateFvarLabels(MSnset_SET1_Pnorm))
MSnset_SET2_Pnorm <- updateSampleNames(updateFvarLabels(MSnset_SET2_Pnorm))
MSnset_SET3_Pnorm <- updateSampleNames(updateFvarLabels(MSnset_SET3_Pnorm))
MSnset_SET4_Pnorm <- updateSampleNames(updateFvarLabels(MSnset_SET4_Pnorm))
MSnset_SET5_Pnorm <- updateSampleNames(updateFvarLabels(MSnset_SET5_Pnorm))
MSnset_comb <- MSnbase::combine(MSnset_SET1_Pnorm,
                                MSnset_SET2_Pnorm,
                                MSnset_SET3_Pnorm,
                                MSnset_SET4_Pnorm,
                                MSnset_SET5_Pnorm)
tokeep <- complete.cases(fData(MSnset_comb))
MSnset_comb <- MSnset_comb[tokeep,]
sampleNames(MSnset_comb) <- pData(MSnset_comb)$SampleName
fData(MSnset_comb) <- fData(MSnset_comb)[,c(2,3,6)]
colnames(fData(MSnset_comb)) <- c("Sequences", "Modifications", "Accessions")
MSnset_comb_corr <- IRSnorm(MSnset_comb, IRSname="Ref", groupingColumn="Run")

crukci-bioinformatics/qPLEXanalyzer documentation built on Sept. 24, 2024, 1:13 a.m.