Synapter2 and synergise2

In synapter: Label-free data analysis pipeline for optimal identification and quantitation

suppressPackageStartupMessages(library("synapter"))
suppressPackageStartupMessages(library("synapterdata"))
suppressPackageStartupMessages(library("BiocStyle"))
synobj2RData()

Introduction

Here we describe the new functionality implemented in synapter 2.0. Namely this vignette covers the utilisation of the new 3D grid search, the fragment matching, intensity modeling and correction of detector saturation.

Workflow

The synapter2 workflow is similar to the old one in synapter1. First it is necessary to use PLGS to create the csv (and xml) files. Therefore we refer the reader to the default r Biocpkg("synapter") vignette, available online and with vignette("synapter", package = "synapter").

In contrast to the original workflow the final_fragment.csv file for the identification run and a Spectrum.xml file for the quantification run are needed if the fragment matching should be applied.

Subsequently the original workflow is enhanced by the new 3D grid search and the intensity modeling. Afterwards the fragment matching could be applied. r Biocpkg("MSnbase") [@Gatto2012] is used for further analysis. The new r Biocpkg("synapter") adds synapter/PLGS consensus filtering and the detector saturation correction for MSnSets.

Step-by-step workflow

Create a Synapter object

To demonstrate a typical step-by-step workflow we use example data that are available on http://proteome.sysbiol.cam.ac.uk/lgatto/synapter/data/. There is also an synobj2 object in r Biocexptpkg("synapterdata") which contains the same data.

The Synapter constructor uses a named list of input files. Please note that we add identfragments (final_fragment.csv) and quantspectra (Spectrum.xml) because we want to apply the fragment matching later.

cat(readLines(system.file(file.path("scripts", "create_synobj2.R"),
                          package="synapterdata"), n=13), sep="\n")

synobj2

Filtering

The first steps in each r Biocpkg("synapter") analysis are filtering by peptide sequence, peptide length, ppm error and false positive rate.

Here we use the default values for each method. But the accompanying plotting methods should be used to find the best threshold:

filterUniqueDbPeptides(synobj2,
                       missedCleavages=0,
                       IisL=TRUE)
filterPeptideLength(synobj2, l=7)
plotFdr(synobj2)
filterQuantPepScore(synobj2, method="BH",
                    fdr=0.05)
filterIdentPepScore(synobj2, method="BH",
                    fdr=0.05)
par(mfcol=c(1, 2))
plotPpmError(synobj2, what="Ident")
plotPpmError(synobj2, what="Quant")
par(mfcol=c(1, 1))
filterQuantPpmError(synobj2, ppm=20)
filterIdentPpmError(synobj2, ppm=20)
plotPepScores(synobj2)
filterIdentProtFpr(synobj2, fpr=0.05)
filterQuantProtFpr(synobj2, fpr=0.05)

Modeling retention time

Next we merge the identified peptides from the identification run and quantification run and build a LOWESS based retention time model to remove systematic shifts in the retention times. Here we use the default values but as stated above the plotting methods should be used to find sensible thresholds.

mergePeptides(synobj2)

plotRt(synobj2, what="data")
setLowessSpan(synobj2, span=0.05)
modelRt(synobj2)

par(mfcol=c(1, 2))
plotRtDiffs(synobj2)
plotRt(synobj2, what="model", nsd=1)
par(mfcol=c(1, 1))

plotFeatures(synobj2, what="all", ionmobility=TRUE)

Grid search{#gridsearch}

To find EMRTS (exact m/z-retention time pairs) we try are running a grid search to find the best retention time tolerance and m/z tolerance that results in the most correct one-to-one matching in the merged (already identified) data. If the identification and quantitation run are HDMS$^E$ data we could use the new 3D grid search that looks for the best matching in the retention time, m/z and ion mobility (drift time) domain to increase the accuracy. If one or both datasets are MS$^E$ data it falls back to the traditional 2D grid search.

searchGrid(synobj2,
           imdiffs=seq(from=0.6, to=1.6, by=0.2),
           ppms=seq(from=2, to=20, by=2),
           nsds=seq(from=0.5, to=5, by=0.5))
setBestGridParams(synobj2)
findEMRTs(synobj2)
plotEMRTtable(synobj2)

Fragment matching{#fragmentmatching}

For the details of the fragment matching procedure we refer to the fragment matching vignette that is available online and with vignette("fragmentmatching", package = "synapter"). Briefly we compare the fragments of the identification run with the spectra from the quantification run and remove entries where there are very few/none common peaks/fragments between them.

First we starting by removing less intense fragments and peaks.

filterFragments(synobj2,
                what="fragments.ident",
                minIntensity=70)
filterFragments(synobj2,
                what="spectra.quant",
                minIntensity=70)

Next we look for common peaks via fragmentMatching:

fragmentMatching(synobj2)

We get tables for unique and non-unique matches:

knitr::kable(fragmentMatchingPerformance(synobj2,
                                         what="unique"))
knitr::kable(fragmentMatchingPerformance(synobj2,
                                         what="non-unique"))

Subsequently we could filter by minimal accepted common peaks:

filterUniqueMatches(synobj2, minNumber=1)
filterNonUniqueMatches(synobj2, minDelta=2)
filterNonUniqueIdentMatches(synobj2)

Finally we rescue EMRTs that are filtered but were identified by PLGS:

rescueEMRTs(synobj2, method="rescue")

Modeling intensity{#intensitymodel}

In a similar manner as correcting for the retention time drift we correct systematic errors of the intensity via a LOWESS model. The function modelIntensity has to applied after findEMRTs. The model is build on the merged peptides as it is done for the retention time model. But in contrast to the retention time model the prediction is necessary for the matched quantitation data.

plotIntensity(synobj2, what="data")
setLowessSpan(synobj2, 0.05)
modelIntensity(synobj2)
plotIntensity(synobj2, what="model", nsd=1)

synergise2

The whole workflow described in the step-by-step workflow is wrapped in the synergise2 function. As side effect it generates a nice HTML report. An example could be found on https://github.com/lgatto/synapter.

synobj2 <- synergise2(filenames = inlist,
                      outputdir = ".")

synapter/PLGS agreement{#synapterplgs}

For the next steps we need to convert the Synapter object into an MSnSet.

msn <- as(synobj2, "MSnSet")

Subsequently we look for synapter/PLGS agreement (this is more useful for a combined MSnSet; see basic r Biocpkg("synapter") online or with vignette("synapter", package = "synapter")). synapterPlgsAgreement adds an agreement column for each sample and counts the agreement/disagreement in additional columns:

msn <- synapterPlgsAgreement(msn)
knitr::kable(head(fData(msn)[, grepl("[Aa]gree",
                                     fvarLabels(msn)),
                             drop=FALSE]))

Correction of detector saturation{#saturation}

As described in [@Shliaha2013] Synapt G2 devices suffer from detector saturation. This could be partly corrected by requantify. Therefore a saturationThreshold has to be given above that intensity saturation potentially happens. There are several methods available.

msncor <- requantify(msn,
                     saturationThreshold=1e5,
                     method="sum")

If an MSnSet object was requantified using the "sum" requantification method TOP3 normalisation is not valid anymore because the most abundant proteins are penalised by removing high intensity isotopes (for details see ?requantify and ?rescaleForTop3). This could be overcome by calling rescaleForTop3:

msncor <- rescaleForTop3(before=msn,
                         after=msncor,
                         saturationThreshold=1e5)

New functions not covered in this vignette

Since r Biocpkg("synapter") 2.0 makeMaster supports fragment files as well. It is possible to create a fragment library that could used for fragment matching because of the large data this could not covered in this vignette. An introduction how to create a master could be found in the basic r Biocpkg("synapter") vignette, available online or with vignette("synapter", package = "synapter"). Please find details about creating a fragment library in ?makeMaster.

Session information{#sec:sessionInfo}

All software and respective versions used to produce this document are listed below.

sessionInfo()

References

synapter documentation built on Nov. 8, 2020, 6:25 p.m.