synapter 2.10.0
synapter is free and open-source software. If you use it, please support the project by citing it in publications:
Nicholas James Bond, Pavel Vyacheslavovich Shliaha, Kathryn S. Lilley, and Laurent Gatto. Improving qualitative and quantitative performance for MS\(^E\)-based label free proteomics. J. Proteome Res., 2013, 12 (6), pp 2340–2353
For bugs, typos, suggestions or other questions, please file an issue
in our tracking system (https://github.com/lgatto/synapter/issues)
providing as much information as possible, a reproducible example and
the output of sessionInfo()
.
If you don’t have a GitHub account or wish to reach a broader audience for general questions about proteomics analysis using R, you may want to use the Bioconductor support site: https://support.bioconductor.org/.
This document assumes familiarity with standard synapter
pipeline described in (Bond et al. 2013) and in the package
synapter vignette, available
online
and with vignette("synapter", package = "synapter")
.
In this vignette we introduce a new fragment matching feature (see figures
2, 3 and 4) which
improves the matching of identification and the quantitation features. After
applying the usual synergise1
workflow (see ?synergise1
and ?Synapter
for details) a number of multiple matches and possible false unique matches
remain that can be deconvoluted by comparing common peaks in the identification
fragment peaks and the quantitation spectra.
The example data synobj2
used throughout this document is available in the
synapterdata package and can be directly load as follows:
library("synapterdata")
synobj2RData()
In the [next section](:synergise] we describe how synobj2
was generated.
The test files used in this vignette can be downloaded from
http://proteome.sysbiol.cam.ac.uk/lgatto/synapter/data/.
The following sections then describe the new fragment matching functionality.
synergise1
One has to run the synergise1
workflow before fragment
matching can be applied. Please read the general synapter
vignette
for the general use of synergise1
.
The additional data needed for the fragment matching procedure are a
final_fragment.csv
file for the identification run and a Spectrum.xml
file
for the quantitation run.
## Please find the raw data at:
## http://proteome.sysbiol.cam.ac.uk/lgatto/synapter/data/
library("synapter")
inlist <- list(
identpeptide = "fermentor_03_sample_01_HDMSE_01_IA_final_peptide.csv.gz",
identfragments = "fermentor_03_sample_01_HDMSE_01_IA_final_fragment.csv.gz",
quantpeptide = "fermentor_02_sample_01_HDMSE_01_IA_final_peptide.csv.gz",
quantpep3d = "fermentor_02_sample_01_HDMSE_01_Pep3DAMRT.csv.gz",
quantspectra = "fermentor_02_sample_01_HDMSE_01_Pep3D_Spectrum.xml.gz",
fasta = "S.cerevisiae_Uniprot_reference_canonical_18_03_14.fasta")
synobj2 <- Synapter(inlist, master=FALSE)
synobj2 <- synergise1(object=synobj2,
outputdir=tempdir())
This step is optional and allows one to remove low abundance fragments
in the spectra using filterFragments
. Filtering fragments can remove noise
in the spectra and reduce undesired fragment matches. Prior to filtering, the
plotCumulativeNumberOfFragments
function can be use to visualise the
intensity of all fragments. Both functions have an argument what
to decide
what spectra/fragments to filter/plot. Choose fragments.ident
for the
identification fragments and spectra.quant
for the quantitation fragments.
plotCumulativeNumberOfFragments(synobj2,
what = "fragments.ident")
plotCumulativeNumberOfFragments(synobj2,
what = "spectra.quant")
filterFragments(synobj2,
what = "fragments.ident",
minIntensity = 70)
filterFragments(synobj2,
what = "spectra.quant",
minIntensity = 70)
This method, named fragmentMatching
, performs the matching of the
identification fragments vs the quantitation spectra and counts the number
of identical peaks for each combination.
Because the peaks/fragments in the spectra of one run will never be
numerically identical to these in another, a tolerance parameter has
to be set using the setFragmentMatchingPpmTolerance
function.
Peaks/Fragments within this tolerance are treated as identical.
setFragmentMatchingPpmTolerance(synobj2, 25)
fragmentMatching(synobj2)
The plotFragmentMatching
function illustrates the details of this fragment
matching procedure. If it is called without any additional argument every
matched pair (fragment vs spectrum) is plotted.
One can use the key
argument to select a special value in a column
(defined by the column
argument) of the MatchedEMRTs
data.frame
.
E.g. if one wants to select the fragment matching results with a high number
of common peaks, e.g. 28 common peaks:
plotFragmentMatching(synobj2,
key = 28,
column = "FragmentMatching")