MSnSet-class

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The "MSnSet" Class for MS Proteomics Expression Data and Meta-Data

The MSnSet holds quantified expression data for MS proteomics data and the experimental meta-data. The MSnSet class is derived from the "eSet" class and mimics the "ExpressionSet" class classically used for microarray data.

Keywords
classes
Objects from the Class

The constructor MSnSet(exprs, fData, pData) can be used to create MSnSet instances. Argument exprs is a matrix and fData and pData must be of clas data.frame or "AnnotatedDataFrame" and all must meet the dimensions and name validity constrains.

Objects can also be created by calls of the form new("MSnSet", exprs, ...). See also "ExpressionSet" for helpful information. Expression data produced from other softwares can thus make use of this standardized data container to benefit R and Bioconductor packages. Importer functions will be developed to stream-line the generation of "MSnSet" instances from third-party software.

A coercion method is also available to transform an IBSpectra object (names x) from the isobar package into an MSnSet: as(x, "MSnSet").

In the frame of the MSnbase package, MSnSet instances can be generated from "MSnExp" experiments using the quantify method).

Extends

Class "eSet", directly. Class "VersionedBiobase", by class "eSet", distance 2. Class "Versioned", by class "eSet", distance 3.

See Also

"eSet", "ExpressionSet" and quantify. MSnSet quantitation values and annotation can be exported to a file with write.exprs. See readMSnSet to create and MSnSet using data available in a spreadsheet or data.frame.

Aliases
  • MSnSet-class
  • class:MSnSet
  • MSnSet
  • exprs,MSnSet-method
  • dim,MSnSet-method
  • fileNames,MSnSet-method
  • msInfo,MSnSet-method
  • processingData,MSnSet-method
  • qual,MSnSet-method
  • qual
  • show,MSnSet-method
  • purityCorrect,MSnSet-method
  • purityCorrect,MSnSet,matrix-method
  • meanSdPlot,MSnSet-method
  • t.MSnSet
  • [,MSnSet-method
  • [,MSnSet,ANY,ANY-method
  • [,MSnSet,ANY,ANY,ANY-method
  • as.ExpressionSet.MSnSet
  • as.MSnSet.ExpressionSet
  • as.data.frame.MSnSet
  • ms2df
  • coerce,IBSpectra,MSnSet-method
  • coerce,MSnSet,ExpressionSet-method
  • coerce,ExpressionSet,MSnSet-method
  • coerce,MSnSet,data.frame-method
  • write.exprs
  • write.exprs,MSnSet-method
  • experimentData<-,MSnSet,MIAPE-method
  • combine,MSnSet,MSnSet-method
  • topN,MSnSet,MSnSet-method
  • topN,MSnSet-method
  • topN,matrix-method
  • topN
  • filterNA,MSnSet-method
  • filterNA,matrix-method
  • filterNA
  • filterZero,MSnSet-method
  • filterZero,matrix-method
  • filterZero
  • log,MSnSet-method
  • image,MSnSet-method
  • image2
  • MAplot,MSnSet-method
  • addIdentificationData,MSnSet,character-method
  • addIdentificationData,MSnSet,mzIDClasses-method
  • addIdentificationData,MSnSet,mzID-method
  • addIdentificationData,MSnSet,mzIDCollection-method
  • addIdentificationData,MSnSet,data.frame-method
  • removeNoId,MSnSet-method
  • removeMultipleAssignment-method
  • removeMultipleAssignment,MSnSet-method
  • removeMultipleAssignment
  • idSummary,MSnSet-method
  • idSummary
  • trimws
  • trimws,MSnSet-method
  • trimws,data.frame-method
  • exptitle,MSnSet-method
  • expemail,MSnSet-method
  • ionSource,MSnSet-method
  • analyser,MSnSet-method
  • analyzer,MSnSet-method
  • detectorType,MSnSet-method
  • description,MSnSet-method
  • updateFvarLabels
  • updateSampleNames
  • updateFeatureNames
  • droplevels.MSnSet
Examples
data(msnset)
msnset <- msnset[10:15]

exprs(msnset)[1, c(1, 4)] <- NA
exprs(msnset)[2, c(1, 2)] <- NA
is.na(msnset)
featureNames(filterNA(msnset, pNA = 1/4))
featureNames(filterNA(msnset, pattern = "0110"))

M <- matrix(rnorm(12), 4)
pd <- data.frame(otherpdata = letters[1:3])
fd <- data.frame(otherfdata = letters[1:4])
x0 <- MSnSet(M, fd, pd)
sampleNames(x0)

M <- matrix(rnorm(12), 4)
colnames(M) <- LETTERS[1:3]
rownames(M) <- paste0("id", LETTERS[1:4])
pd <- data.frame(otherpdata = letters[1:3])
rownames(pd) <- colnames(M)
fd <- data.frame(otherfdata = letters[1:4])
rownames(fd) <- rownames(M)
x <- MSnSet(M, fd, pd)
sampleNames(x)


## Visualisation

library(pRolocdata)
data(dunkley2006)
image(dunkley2006)
## Changing colours
image(dunkley2006, high = "darkgreen")
image(dunkley2006, high = "darkgreen", low = "yellow")
## Forcing feature names
image(dunkley2006, fnames = TRUE)
## Facetting
image(dunkley2006, facetBy = "replicate")
p <- image(dunkley2006)
library("ggplot2") ## for facet_grid
p + facet_grid(replicate ~ membrane.prep, scales = 'free', space = 'free')
p + facet_grid(markers ~ replicate)
## Fold-changes
dd <- dunkley2006
exprs(dd) <- exprs(dd) - 0.25
image(dd)
image(dd, low = "green", high = "red")
## Feature names are displayed by default for smaller data
dunkley2006 <- dunkley2006[1:25, ]
image(dunkley2006)
image(dunkley2006, legend = "hello")
Documentation reproduced from package MSnbase, version 1.20.7, License: Artistic-2.0

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