Working with PSM data (original) (raw)
Package: PSMatch
Authors: Laurent Gatto [aut, cre] (ORCID:https://orcid.org/0000-0002-1520-2268), Johannes Rainer [aut] (ORCID: https://orcid.org/0000-0002-6977-7147), Sebastian Gibb [aut] (ORCID: https://orcid.org/0000-0001-7406-4443), Samuel Wieczorek [ctb], Thomas Burger [ctb], Guillaume Deflandre [ctb] (ORCID:https://orcid.org/0009-0008-1257-2416)
Last modified: 2025-04-04 14:58:32.340617
Compiled: Tue Apr 15 20:15:09 2025
Installation instructions
To install the package from Bioconductor, make sure you have theBiocManager package, available from CRAN, and then run
BiocManager::install("PSMatch")Introduction
This vignette is one among several illustrating how to use thePSMatch package, focusing on the handling and processing of proteomics identification data using the PSM class. For a general overview of the package, see the PSMatch package manual page (?PSMatch) and references therein.
Handling and processing identification data
Loading PSM data
We are going to use an mzid file from the msdata package.
f <- msdata::ident(full.names = TRUE, pattern = "TMT")
basename(f)## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid"The PSM() function parses one or multiple mzid files and returns an object of class PSM. This class is a simple extension of theDFrame class, representing the peptide-spectrum matches in a tabular format.
library("PSMatch")
id <- PSM(f)
id## PSM with 5802 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationThis table contains 5802 matches for 5343 scans and 4938 peptides sequences, each annotated by 35 variables.
nrow(id) ## number of matches## [1] 5802length(unique(id$spectrumID)) ## number of scans## [1] 5343length(unique(id$sequence)) ## number of peptide sequences## [1] 4938names(id)## [1] "sequence" "spectrumID"
## [3] "chargeState" "rank"
## [5] "passThreshold" "experimentalMassToCharge"
## [7] "calculatedMassToCharge" "peptideRef"
## [9] "modNum" "isDecoy"
## [11] "post" "pre"
## [13] "start" "end"
## [15] "DatabaseAccess" "DBseqLength"
## [17] "DatabaseSeq" "DatabaseDescription"
## [19] "scan.number.s." "acquisitionNum"
## [21] "spectrumFile" "idFile"
## [23] "MS.GF.RawScore" "MS.GF.DeNovoScore"
## [25] "MS.GF.SpecEValue" "MS.GF.EValue"
## [27] "MS.GF.QValue" "MS.GF.PepQValue"
## [29] "modPeptideRef" "modName"
## [31] "modMass" "modLocation"
## [33] "subOriginalResidue" "subReplacementResidue"
## [35] "subLocation"The PSM data are read as is, without any filtering. As we can see below, we still have all the hits from the forward and reverse (decoy) databases.
table(id$isDecoy)##
## FALSE TRUE
## 2906 2896Keeping all matches
The data also contains multiple matches for several spectra. The table below shows the number of individual MS scans that have 1, 2, … up to 5 matches.
table(table(id$spectrumID))##
## 1 2 3 4 5
## 4936 369 26 10 2More specifically, we can see below how scan 1774 has 4 matches, all to sequence RTRYQAEVR, which itself matches to 4 different proteins:
i <- grep("scan=1774", id$spectrumID)
id[i, ]## PSM with 4 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationid[i, "DatabaseAccess"]## [1] "ECA2104" "ECA2867" "ECA3427" "ECA4142"If the goal is to keep all the matches, but arranged by scan/spectrum, one can reduce the DataFrame object by the spectrumID variable, so that each scan correponds to a single row that still stores all values111 The rownames aren’t needed here and are removed to reduce to output in the the next code chunks displaying parts of id2.:
id2 <- reducePSMs(id, id$spectrumID)
rownames(id2) <- NULL ## rownames not needed here
dim(id2)## [1] 5343 35The resulting object contains a single entrie for scan 1774 with information for the multiple matches stored as a list within the table cell.
j <- grep("scan=1774", id2$spectrumID)
id2[j, ]## Reduced PSM with 1 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationid2[j, "DatabaseAccess"]## CharacterList of length 1
## [["controllerType=0 controllerNumber=1 scan=1774"]] ECA2104 ECA2867 ECA3427 ECA4142The identification data could be used to annotate an raw mass spectrometry Spectra object (see the Spectra::joinSpectraData()function for details).
Filtering data
Often, the PSM data is filtered to only retain reliable matches. TheMSnID package can be used to set thresholds to attain user-defined PSM, peptide or protein-level FDRs. Here, we will simply filter out wrong or the least reliable identifications.
Remove decoy hits
id <- filterPsmDecoy(id)## Removed 2896 decoy hits.id## PSM with 2906 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationKeep first rank matches
id <- filterPsmRank(id)## Removed 155 PSMs with rank > 1.id## PSM with 2751 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationAll filters in one function
This can also be achieved with the filterPSMs() function:
id <- PSM(f)
filterPSMs(id)## Starting with 5802 PSMs:## Removed 2896 decoy hits.## Removed 155 PSMs with rank > 1.## Removed 85 shared peptides.## 2666 PSMs left.## PSM with 2666 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationThe mzR and mzID parsers
The PSM() function can take two different values for the parserparameter, namely "mzR" (the default value) and "mzID".
- mzR uses the
openIDfile()function from the_mzR_ to parse themzIdfile(s), and then coerces the data to adata.framewhich is eventually returned as aPSMobject. The parser function uses dedicated code from the Proteowizard project (included inmzR) and is generally the fastest approach. - mzID parses the
mzIdfile withmzID()function from the_mzID_ package, and then flattens the data to adata.framewithmzID::flatten()and eventuelly returns aPSMobject. ThemzIDpackage relies on the _XML_package. Is is slower but is is more robust to variations in themzIDimplementation, as is thus a useful backup when themzRbackend fails.
system.time(id1 <- PSM(f, parser = "mzR"))## user system elapsed
## 0.281 0.009 0.289system.time(id2 <- PSM(f, parser = "mzID"))## Loading required namespace: mzID## reading TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid...## DONE!## user system elapsed
## 8.530 0.294 8.824Other differences in the two parsers include the columns that are returned, the way they name them, and, as will shown below the matches that are returned. Note for instance (and this will be important later), that there is no equivalent of "modLocation" in id2.
names(id1)## [1] "sequence" "spectrumID"
## [3] "chargeState" "rank"
## [5] "passThreshold" "experimentalMassToCharge"
## [7] "calculatedMassToCharge" "peptideRef"
## [9] "modNum" "isDecoy"
## [11] "post" "pre"
## [13] "start" "end"
## [15] "DatabaseAccess" "DBseqLength"
## [17] "DatabaseSeq" "DatabaseDescription"
## [19] "scan.number.s." "acquisitionNum"
## [21] "spectrumFile" "idFile"
## [23] "MS.GF.RawScore" "MS.GF.DeNovoScore"
## [25] "MS.GF.SpecEValue" "MS.GF.EValue"
## [27] "MS.GF.QValue" "MS.GF.PepQValue"
## [29] "modPeptideRef" "modName"
## [31] "modMass" "modLocation"
## [33] "subOriginalResidue" "subReplacementResidue"
## [35] "subLocation"names(id2)## [1] "spectrumid" "scan number(s)"
## [3] "acquisitionnum" "passthreshold"
## [5] "rank" "calculatedmasstocharge"
## [7] "experimentalmasstocharge" "chargestate"
## [9] "ms-gf:denovoscore" "ms-gf:evalue"
## [11] "ms-gf:pepqvalue" "ms-gf:qvalue"
## [13] "ms-gf:rawscore" "ms-gf:specevalue"
## [15] "assumeddissociationmethod" "isotopeerror"
## [17] "isdecoy" "post"
## [19] "pre" "end"
## [21] "start" "accession"
## [23] "length" "description"
## [25] "pepseq" "modified"
## [27] "modification" "idFile"
## [29] "spectrumFile" "databaseFile"We also have different number of matches in the two tables:
nrow(id1)## [1] 5802nrow(id2)## [1] 5759table(id1$isDecoy)##
## FALSE TRUE
## 2906 2896table(id2$isdecoy)##
## FALSE TRUE
## 2886 2873Let’s first filter the PSM tables to facilitate focus the comparison of relevant scans. Note that the default filterPSMs() arguments are set to work with both parser.
id1_filtered <- filterPSMs(id1)## Starting with 5802 PSMs:## Removed 2896 decoy hits.## Removed 155 PSMs with rank > 1.## Removed 85 shared peptides.## 2666 PSMs left.id2_filtered <- filterPSMs(id2)## Starting with 5759 PSMs:## Removed 2873 decoy hits.## Removed 155 PSMs with rank > 1.## Removed 85 shared peptides.## 2646 PSMs left.As can be seen, we are also left with 2666 vs 2646 PSMs after filtering.
The difference doesn’t stem from different scans, given that the spectum identifiers are identical in both tables:
identical(sort(unique(id1_filtered$spectrumID)),
sort(unique(id2_filtered$spectrumid)))## [1] TRUEThe difference is obvious when we tally a table of spectrum id occurences in the filtered tables. In id2_filtered, each scan is unique, i.e matched only once.
anyDuplicated(id2_filtered$spectrumid)## [1] 0However, for id1_filtered, we see that some scans are still repeat up to 4 times in the table:
table(table(id1_filtered$spectrumID))##
## 1 2 3 4
## 2630 13 2 1The example below shows that these differences stem from the modification location ("modLocation"), that is not report by themzID parser:
k <- names(which(table(id1_filtered$spectrumID) == 4))
id1_filtered[id1_filtered$spectrumID == k, "sequence"]## [1] "KCNQCLKVACTLFYCK" "KCNQCLKVACTLFYCK" "KCNQCLKVACTLFYCK" "KCNQCLKVACTLFYCK"id1_filtered[id1_filtered$spectrumID == k, "modLocation"]## [1] 2 5 10 15id1_filtered[id1_filtered$spectrumID == k, "modName"]## [1] "Carbamidomethyl" "Carbamidomethyl" "Carbamidomethyl" "Carbamidomethyl"If we remove the "modLocation" column, we recoved the same number of PSMs than with the mzID parser.
id1_filtered$modLocation <- NULL
nrow(unique(id1_filtered))## [1] 2646nrow(unique(id2_filtered))## [1] 2646Session information
sessionInfo()## R version 4.5.0 beta (2025-04-02 r88102)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
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## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
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## time zone: America/New_York
## tzcode source: system (glibc)
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] Spectra_1.19.0 BiocParallel_1.43.0
## [3] factoextra_1.0.7 ggplot2_3.5.2
## [5] QFeatures_1.19.0 MultiAssayExperiment_1.35.0
## [7] SummarizedExperiment_1.39.0 Biobase_2.69.0
## [9] GenomicRanges_1.61.0 GenomeInfoDb_1.45.0
## [11] IRanges_2.43.0 MatrixGenerics_1.21.0
## [13] matrixStats_1.5.0 PSMatch_1.13.0
## [15] S4Vectors_0.47.0 BiocGenerics_0.55.0
## [17] generics_0.1.3 BiocStyle_2.37.0
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## loaded via a namespace (and not attached):
## [1] rlang_1.1.6 magrittr_2.0.3 clue_0.3-66
## [4] compiler_4.5.0 vctrs_0.6.5 reshape2_1.4.4
## [7] stringr_1.5.1 ProtGenerics_1.41.0 pkgconfig_2.0.3
## [10] MetaboCoreUtils_1.17.0 crayon_1.5.3 fastmap_1.2.0
## [13] backports_1.5.0 magick_2.8.6 XVector_0.49.0
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## [25] broom_1.0.8 parallel_4.5.0 cluster_2.1.8.1
## [28] R6_2.6.1 bslib_0.9.0 stringi_1.8.7
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## [34] Rcpp_1.0.14 bookdown_0.43 knitr_1.50
## [37] BiocBaseUtils_1.11.0 Matrix_1.7-3 igraph_2.1.4
## [40] tidyselect_1.2.1 abind_1.4-8 yaml_2.3.10
## [43] doParallel_1.0.17 codetools_0.2-20 lattice_0.22-7
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## [61] mzID_1.47.0 mzR_2.43.0 ggsignif_0.6.4
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## [67] tidyr_1.3.1 MsCoreUtils_1.21.0 msdata_0.47.0
## [70] colorspace_2.1-1 GenomeInfoDbData_1.2.14 Formula_1.2-5
## [73] cli_3.6.4 S4Arrays_1.9.0 dplyr_1.1.4
## [76] AnnotationFilter_1.33.0 gtable_0.3.6 rstatix_0.7.2
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