MSnSet-class: The "MSnSet" Class for MS Proteomics Expression Data and...
In lgatto/MSnbase: Base Functions and Classes for Mass Spectrometry and Proteomics

MSnSet-class

R Documentation

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

Description

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.

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 class 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. Proteomics expression data available as spreadsheets, as produced by third-party software such as Proteome Discoverer, MaxQuant, ... can be imported using the readMSnSet and readMSnSet2 functions.

Coercion methods are also available to transform MSnSet objects to IBSpectra, to data.frame and to/from ExpressionSet and SummarizedExperiment objects. In the latter case, the metadata available in the protocolData, experimentData are completely dropped, and only the logging information of the processingData slot is retained. All these metadata can be subsequently be added using the addMSnSetMetadata (see examples below). When converting a SummarizedExperiment to an MSnSet, the respective metadata slots will be populated if available in the SummarizedExperiment metadata.

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

Slots

qual:: Object of class "data.frame" that records peaks data for each of the reporter ions to be used as quality metrics.
processingData:: Object of class "MSnProcess" that records all processing.
assayData:: Object of class "assayData" containing a matrix with equal with column number equal to nrow(phenoData). assayData must contain a matrix exprs with rows represening features (e.g., reporters ions) and columns representing samples. See the "AssayData" class, exprs and assayData accessor for more details. This slot in indirectly inherited from "eSet".
phenoData:: Object of class "AnnotatedDataFrame" containing experimenter-supplied variables describing sample (i.e the individual tags for an labelled MS experiment) (indireclty inherited from "eSet"). See phenoData and the "eSet" class for more details. This slot can be accessed as a data.frame with pData and be replaced by a new valid (i.e. of compatible dimensions and row names) data.frame with pData()<-.
featureData:: Object of class "AnnotatedDataFrame" containing variables describing features (spectra in our case), e.g. identificaiton data, peptide sequence, identification score,... (inherited indirectly from "eSet"). See featureData and the "eSet" class for more details. This slot can be accessed as a data.frame with fData and be replaced by a new valid (i.e. of compatible dimensions and row names) data.frame with fData()<-.
experimentData:: Object of class "MIAPE", containing details of experimental methods (inherited from "eSet"). See experimentData and the "eSet" class for more details.
annotation:: not used here.
protocolData:: Object of class "AnnotatedDataFrame" containing equipment-generated variables (inherited indirectly from "eSet"). See protocolData and the "eSet" class for more details.
.__classVersion__:: Object of class "Versions" describing the versions of R, the Biobase package, "eSet", "pSet" and MSnSet of the current instance. Intended for developer use and debugging (inherited indirectly from "eSet").

Extends

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

Methods

MSnSet specific methods or over-riding it's super-class are described below. See also more "eSet" for inherited methods.

acquisitionNum

acquisitionNum(signature(object = "MSnSet")): Returns the a numeric vector with acquisition number of each spectrum. The vector names are the corresponding spectrum names. The information is extracted from the object's featureData slot.

fromFile

fromFile(signature(object = "MSnSet")): get the index of the file (in fileNames(object)) from which the raw spectra from which the corresponding feature were originally read. The relevant information is extracted from the object's featureData slot.

Returns a numeric vector with names corresponding to the spectrum names.

dim

signature(x = "MSnSet"): Returns the dimensions of object's assay data, i.e the number of samples and the number of features.

fileNames

signature(object = "MSnSet"): Access file names in the processingData slot.

msInfo

signature(object = "MSnSet"): Prints the MIAPE-MS meta-data stored in the experimentData slot.

processingData

signature(object = "MSnSet"): Access the processingData slot.

show

signature(object = "MSnSet"): Displays object content as text.

qual

signature(object = "MSnSet"): Access the reporter ion peaks description.

purityCorrect

signature(object = "MSnSet", impurities = "matrix"): performs reporter ions purity correction. See purityCorrect documentation for more details.

normalise

signature(object = "MSnSet"): Performs MSnSet normalisation. See normalise for more details.

t

signature(x = "MSnSet"): Returns a transposed MSnSet object where features are now aligned along columns and samples along rows and the phenoData and featureData slots have been swapped. The protocolData slot is always dropped.

as(,"ExpressionSet")

signature(x = "MSnSet"): Coerce object from MSnSet to ExpressionSet-class. The experimentData slot is converted to a MIAME instance. It is also possible to coerce an ExpressionSet to and MSnSet, in which case the experimentData slot is newly initialised.

as(,"IBSpectra")

signature(x = "MSnSet"): Coerce object from MSnSet to IBSpectra from the isobar package.

as(,"data.frame")

signature(x = "MSnSet"): Coerce object from MSnSet to data.frame. The MSnSet is transposed and the PhenoData slot is appended.

as(,"SummarizedExperiment")

signature(x = "MSnSet"): Coerce object from MSnSet to SummarizedExperiment. Only part of the metadata is retained. See addMSnSetMetadata and the example below for details.

write.exprs

signature(x = "MSnSet"): Writes expression values to a tab-separated file (default is tmp.txt). The fDataCols parameter can be used to specify which featureData columns (as column names, column number or logical) to append on the right of the expression matrix. The following arguments are the same as write.table.

combine

signature(x = "MSnSet", y = "MSnSet", ...): Combines 2 or more MSnSet instances according to their feature names. Note that the qual slot and the processing information are silently dropped.

topN

signature(object = "MSnSet", groupBy, n = 3, fun, ..., verbose = isMSnbaseVerbose()): Selects the n most intense features (typically peptides or spectra) out of all available for each set defined by groupBy (typically proteins) and creates a new instance of class MSnSet. If less than n features are available, all are selected. The ncol(object) features are summerised using fun (default is sum) prior to be ordered in decreasing order. Additional parameters can be passed to fun through ..., for instance to control the behaviour of topN in case of NA values. (Works also with matrix instances.)

See also the nQuants function to retrieve the actual number of retained peptides out of n.

A complete use case using topN and nQuants is detailed in the synapter package vignette.

filterNA

signature(object = "MSnSet", pNA = "numeric", pattern = "character", droplevels = "logical"): This method subsets object by removing features that have (strictly) more than pNA percent of NA values. Default pNA is 0, which removes any feature that exhibits missing data. The method can also be used with a character pattern composed of 0 or 1 characters only. A 0 represent a column/sample that is allowed a missing values, while columns/samples with and 1 must not have NAs.

This method also accepts matrix instances. droplevels defines whether unused levels in the feature meta-data ought to be lost. Default is TRUE. See the droplevels method below.

See also the is.na.MSnSet and plotNA methods for missing data exploration.

filterZero

signature(object = "MSnSet", pNA = "numeric", pattern = "character", droplevels = "logical"): As filterNA, but for zeros.

filterMsLevel

signature(object = "MSnSet", msLevel. = "numeric", fcol = "character") Keeps only spectra with level msLevel., as defined by the fcol feature variable (default is "msLevel").

log

signature(object = "MSnSet", base = "numeric") Log transforms exprs(object) using base::log. base (defaults is e='exp(1)') must be a positive or complex number, the base with respect to which logarithms are computed.

droplevels

signature(x = "MSnSet", ...)Drops the unused factor levels in the featureData slot. See droplevels for details.

impute

signature(object = "MSnSet", ...) Performs data imputation on the MSnSet object. See impute for more details.

trimws

signature(object = "MSnSet", ...)Trim leading and/or trailing white spaces in the feature data slot. Also available for data.frame objects. See ?base::trimws for details.

Additional accessors for the experimental metadata (experimentData slot) are defined. See "MIAPE" for details.

Plotting

meanSdPlot

signature(object = "MSnSet") Plots row standard deviations versus row means. See meanSdPlot (vsn package) for more details.

image

signature(x = "MSnSet", facetBy = "character", sOrderBy = "character", legend = "character", low = "character", high = "character", fnames = "logical", nmax = "numeric") Produces an heatmap of expression values in the x object. Simple horizontal facetting is enabled by passing a single character as facetBy. Arbitrary facetting can be performed manually by saving the return value of the method (see example below). Re-ordering of the samples is possible by providing the name of a phenotypic variable to sOrderBy. The title of the legend can be set with legend and the colours with the low and high arguments. If any negative value is detected in the data, the values are considered as log fold-changes and a divergent colour scale is used. Otherwise, a gradient from low to high is used. To scale the quantitative data in x prior to plotting, please see the scale method.

When there are more than nmax (default is 50) features/rows, these are not printed. This behaviour can be controlled by setting fnames to TRUE (always print) or FALSE (never print). See examples below.

The code is based on Vlad Petyuk's vp.misc::image_msnset. The previous version of this method is still available through the image2 function.

plotNA

signature(object = "MSnSet", pNA = "numeric") Plots missing data for an MSnSet instance. pNA is a numeric of length 1 that specifies the percentage of accepted missing data values per features. This value will be highlighted with a point on the figure, illustrating the overall percentage of NA values in the full data set and the number of proteins retained. Default is 1/2. See also plotNA.

MAplot

signature(object = "MSnSet", log.it = "logical", base = "numeric", ...) Produces MA plots (Ratio as a function of average intensity) for the samples in object. If ncol(object) == 2, then one MA plot is produced using the ma.plot function from the affy package. If object has more than 2 columns, then mva.pairs. log.it specifies is the data should be log-transformed (default is TRUE) using base. Further ... arguments will be passed to the respective functions.

addIdentificationData

signature(object = "MSnSet", ...): Adds identification data to a MSnSet instance. See addIdentificationData documentation for more details and examples.

removeNoId

signature(object = "MSnSet", fcol = "pepseq", keep = NULL): Removes non-identified features. See removeNoId documentation for more details and examples.

removeMultipleAssignment

signature(object = "MSnSet", fcol = "nprot"): Removes protein groups (or feature belong to protein groups) with more than one member. The latter is defined by extracting a feature variable (default is "nprot"). Also removes non-identified features.

idSummary

signature(object = "MSnSet", ...): Prints a summary that lists the percentage of identified features per file (called coverage).

Functions

updateFvarLabels: signature(object, label, sep) This function updates object's featureData variable labels by appending label. By default, label is the variable name and the separator sep is ..
updateSampleNames: signature(object, label, sep) This function updates object's sample names by appending label. By default, label is the variable name and the separator sep is ..
updateFeatureNames: signature(object, label, sep) This function updates object's feature names by appending label. By default, label is the variable name and the separator sep is ..
ms2df: signature(x, fcols) Coerces the MSnSet instance to a data.frame. The direction of the data is retained and the feature variable labels that match fcol are appended to the expression values. See also as(x, "data.frame") above.
addMSnSetMetadata: signature(x, y) When coercing an MSnSet y to a SummarizedExperiment x with x <- as(y, "SummarizedExperiment"), most of y's metadata is lost. Only the file names, the processing log and the MSnbase version from the processingData slots are passed along. The addMSnSetMetadata function can be used to add the complete processingData, experimentData and protocolData slots. The downside of this is that MSnbase is now required to use the SummarizedExperiment object.

Author(s)

Laurent Gatto

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")


## Coercion
if (require("SummarizedExperiment")) {
    data(msnset)
    se <- as(msnset, "SummarizedExperiment")
    metadata(se) ## only logging
    se <- addMSnSetMetadata(se, msnset)
    metadata(se) ## all metadata
    msnset2 <- as(se, "MSnSet")
    processingData(msnset2)
}

as(msnset, "ExpressionSet")

lgatto/MSnbase documentation built on Nov. 12, 2024, 10:58 a.m.