Description Arguments Methods Author(s) References See Also Examples
The m/z delta plot illustrates the suitability of MS2 spectra for identification by plotting the m/z differences of the most intense peaks. The resulting histogram should optimally shown outstanding bars at amino acid residu masses. The plots have been described in Foster et al 2011.
Only a certain percentage of most intense MS2 peaks are taken into
account to use the most significant signal. Default value is 10% (see
percentage
argument). The difference between peaks is then
computed for all individual spectra and their distribution is plotted
as a histogram where single bars represent 1 m/z differences. Delta
m/z between 40 and 200 are plotted by default, to encompass the
residue masses of all amino acids and several common contaminants,
although this can be changes with the xlim
argument.
In addition to the processing described above, isobaric reporter tag
peaks (see the reporters
argument) and the precursor peak (see
the precMz
argument) can also be removed from the MS2 spectrum,
to avoid interence with the fragment peaks.
Note that figures in Foster et al 2011 have been produced and optimised for centroided data. Application of the plot as is for data in profile mode has not been tested thoroughly, although the example below suggest that it might work.
The methods make use the ggplot2
system. An object of class
ggplot
is returned invisibly.
Most of the code for plotMzDelta has kindly been contributed by Guangchuang Yu.
object |
An object of class |
reporters |
An object of class class
|
subset |
A numeric between 0 and 1 to use a subset of
|
percentage |
The percentage of most intense peaks to be used for the plot. Default is 0.1. |
precMz |
A |
precMzWidth |
A |
bw |
A |
xlim |
A |
withLabels |
A |
size |
A |
plot |
A |
verbose |
A |
signature(object = "MSnExp", ...)
Plots and (invisibly) returns the m/z delta histogram.
Laurent Gatto <lg390@cam.ac.uk> and Guangchuang Yu
Foster JM, Degroeve S, Gatto L, Visser M, Wang R, Griss J, Apweiler R, Martens L. "A posteriori quality control for the curation and reuse of public proteomics data." Proteomics, 2011 Jun;11(11):2182-94. doi:10.1002/pmic.201000602. Epub 2011 May 2. PMID: 21538885
The plotDensity
and plot2d
methods for
other QC plots.
1 2 3 4 5 6 7 8 9 10 11 12 | mzdplot <- plotMzDelta(itraqdata,
subset = 0.5,
reporters = iTRAQ4,
verbose = FALSE, plot = FALSE)
## let's retrieve peptide sequence information
## and get a table of amino acids
peps <- as.character(fData(itraqdata)$PeptideSequence)
aas <- unlist(strsplit(peps,""))
## table of aas
table(aas)
## mzDelta plot
print(mzdplot)
|
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