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 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. MSnbase (Gatto and Lilley 2012) is used for further analysis. The new synapter adds synapter/PLGS consensus filtering and the detector saturation correction for MSnSets.

New synapter workflow. Dark green boxes show the traditional synapter1 part and the light green boxes highlight the new synapter2 functionality.

## 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

## Object of class "Synapter" 
## Class version 2.0.0 
## Package version 1.99.0 
## Data files:
##  + Identification pep file: fermentor_03_sample_01_HDMSE_01_IA_final_peptide.csv.gz 
##  + Identification Fragment file: fermentor_02_sample_01_HDMSE_01_Pep3D_Spectrum.xml.gz 
##  + Quantitation pep file: fermentor_02_sample_01_HDMSE_01_IA_final_peptide.csv.gz 
##  + Quantitation Pep3DAMRT file: fermentor_02_sample_01_HDMSE_01_Pep3DAMRT.csv.gz 
##  + Quantitation Spectrum file: fermentor_02_sample_01_HDMSE_01_Pep3D_Spectrum.xml.gz 
##  + Fasta file: S.cerevisiae_Uniprot_reference_canonical_18_03_14.fasta 
## Log:
## [1] "Instance created on Tue Apr  4 09:01:34 2017"
## [2] "Read identification peptide data [11471,16]" 
## [ 10 lines ]
## [13] "Read identification fragment data [7078]"
## [14] "Read quantitation spectra [36539]"

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)

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)

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)

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

fragmentMatching(synobj2)

## Warning in object$fragmentMatching(verbose = verbose): FragmentMatching ppm
## tolerance undefined. Setting to default value.

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

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

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

rescueEMRTs(synobj2, method="rescue")

plotIntensity(synobj2, what="data")

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

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

synapter/PLGS agreement

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 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

As described in (Shliaha et al. 2013) 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 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 synapter vignette, available online or with vignette("synapter", package = "synapter"). Please find details about creating a fragment library in ?makeMaster.

Session information

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

sessionInfo()

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] qvalue_2.39.0       BiocStyle_2.35.0    synapterdata_1.44.0
##  [4] synapter_2.31.0     MSnbase_2.33.2      ProtGenerics_1.39.0
##  [7] S4Vectors_0.45.2    mzR_2.41.0          Rcpp_1.0.13-1      
## [10] Biobase_2.67.0      BiocGenerics_0.53.3 generics_0.1.3     
## [13] rmarkdown_2.29     
## 
## loaded via a namespace (and not attached):
##   [1] rlang_1.1.4                 magrittr_2.0.3             
##   [3] clue_0.3-66                 cleaver_1.45.0             
##   [5] matrixStats_1.4.1           compiler_4.4.2             
##   [7] vctrs_0.6.5                 reshape2_1.4.4             
##   [9] stringr_1.5.1               pkgconfig_2.0.3            
##  [11] crayon_1.5.3                fastmap_1.2.0              
##  [13] XVector_0.47.0              utf8_1.2.4                 
##  [15] tzdb_0.4.0                  UCSC.utils_1.3.0           
##  [17] preprocessCore_1.69.0       bit_4.5.0                  
##  [19] purrr_1.0.2                 xfun_0.49                  
##  [21] MultiAssayExperiment_1.33.0 zlibbioc_1.52.0            
##  [23] cachem_1.1.0                GenomeInfoDb_1.43.1        
##  [25] jsonlite_1.8.9              DelayedArray_0.33.2        
##  [27] BiocParallel_1.41.0         parallel_4.4.2             
##  [29] cluster_2.1.6               R6_2.5.1                   
##  [31] RColorBrewer_1.1-3          bslib_0.8.0                
##  [33] stringi_1.8.4               limma_3.63.2               
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##  [39] knitr_1.49                  readr_2.1.5                
##  [41] IRanges_2.41.1              splines_4.4.2              
##  [43] Matrix_1.7-1                igraph_2.1.1               
##  [45] tidyselect_1.2.1            abind_1.4-8                
##  [47] yaml_2.3.10                 doParallel_1.0.17          
##  [49] codetools_0.2-20            affy_1.85.0                
##  [51] lattice_0.22-6              tibble_3.2.1               
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##  [57] pillar_1.9.0                affyio_1.77.0              
##  [59] BiocManager_1.30.25         MatrixGenerics_1.19.0      
##  [61] foreach_1.5.2               MALDIquant_1.22.3          
##  [63] ncdf4_1.23                  vroom_1.6.5                
##  [65] hms_1.1.3                   ggplot2_3.5.1              
##  [67] munsell_0.5.1               scales_1.3.0               
##  [69] glue_1.8.0                  lazyeval_0.2.2             
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##  [75] QFeatures_1.17.0            vsn_3.75.0                 
##  [77] buildtools_1.0.0            XML_3.99-0.17              
##  [79] grid_4.4.2                  impute_1.81.0              
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##  [87] fansi_1.0.6                 S4Arrays_1.7.1             
##  [89] dplyr_1.1.4                 AnnotationFilter_1.31.0    
##  [91] pcaMethods_1.99.0           gtable_0.3.6               
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## [101] bit64_4.5.2                 MASS_7.3-61