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]
Last modified: 2024-10-24 00:16:19.807674
Compiled: Tue Oct 29 18:57:29 2024
To install the package from Bioconductor, make sure you have the
BiocManager package, available from CRAN, and then run
BiocManager::install("PSMatch")This vignette is one among several illustrating how to use the
PSMatch 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.
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 the
DFrame 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$isDecoy)##
## FALSE TRUE
## 2906 2896The data also contains multiple matches for several spectra. The table below shows the number of individual MS scans that have 1, 2, … up to 5 matches.
table(table(id$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$spectrumID)
id[i, ]## PSM with 4 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationid[i, "DatabaseAccess"]## [1] "ECA2104" "ECA2867" "ECA3427" "ECA4142"If the goal is to keep all the matches, but arranged by scan/spectrum,
one can reduce the DataFrame object by the spectrumID variable,
so that each scan correponds to a single row that still stores all
values1 The rownames aren’t needed here and are removed to reduce
to output in the the next code chunks displaying parts of id2.:
id2 <- reducePSMs(id, id$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).
Often, the PSM data is filtered to only retain reliable matches. The
MSnID 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.
id <- filterPsmDecoy(id)## Removed 2896 decoy hits.id## PSM with 2906 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationid <- filterPsmRank(id)## Removed 155 PSMs with rank > 1.id## PSM with 2751 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocationThis 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 subLocationmzR and mzID parsersThe PSM() function can take two different values for the parser
parameter, namely "mzR" (the default value) and "mzID".
mzR uses the openIDfile() function from the
mzR to parse the mzId file(s), and then
coerces the data to a data.frame which is eventually returned as
a PSM object. The parser function uses dedicated code from the
Proteowizard project (included in mzR) and is generally the
fastest approach.
mzID parses the mzId file with mzID() function from the
mzID package, and then flattens the data to
a data.frame with mzID::flatten() and eventuelly returns a
PSM object. The mzID package relies on the XML
package. Is is slower but is is more robust to variations in the
mzID implementation, as is thus a useful backup when the mzR
backend fails.
system.time(id1 <- PSM(f, parser = "mzR"))## user system elapsed
## 0.277 0.002 0.279system.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
## 7.875 0.266 8.153Other 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$spectrumID))##
## 1 2 3 4
## 2630 13 2 1The example below shows that these differences stem from the
modification location ("modLocation"), that is not report by the
mzID parser:
k <- names(which(table(id1_filtered$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] 2646sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
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## locale:
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## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
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## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] Spectra_1.17.0 BiocParallel_1.41.0
## [3] factoextra_1.0.7 ggplot2_3.5.1
## [5] QFeatures_1.17.0 MultiAssayExperiment_1.33.0
## [7] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [9] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
## [11] IRanges_2.41.0 MatrixGenerics_1.19.0
## [13] matrixStats_1.4.1 PSMatch_1.11.0
## [15] S4Vectors_0.45.0 BiocGenerics_0.53.0
## [17] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] rlang_1.1.4 magrittr_2.0.3 clue_0.3-65
## [4] compiler_4.5.0 vctrs_0.6.5 reshape2_1.4.4
## [7] stringr_1.5.1 ProtGenerics_1.39.0 pkgconfig_2.0.3
## [10] MetaboCoreUtils_1.15.0 crayon_1.5.3 fastmap_1.2.0
## [13] backports_1.5.0 magick_2.8.5 XVector_0.47.0
## [16] labeling_0.4.3 utf8_1.2.4 rmarkdown_2.28
## [19] UCSC.utils_1.3.0 tinytex_0.53 purrr_1.0.2
## [22] xfun_0.48 zlibbioc_1.53.0 cachem_1.1.0
## [25] jsonlite_1.8.9 highr_0.11 DelayedArray_0.33.0
## [28] broom_1.0.7 parallel_4.5.0 cluster_2.1.6
## [31] R6_2.5.1 bslib_0.8.0 stringi_1.8.4
## [34] car_3.1-3 jquerylib_0.1.4 iterators_1.0.14
## [37] Rcpp_1.0.13 bookdown_0.41 knitr_1.48
## [40] Matrix_1.7-1 igraph_2.1.1 tidyselect_1.2.1
## [43] abind_1.4-8 yaml_2.3.10 doParallel_1.0.17
## [46] codetools_0.2-20 lattice_0.22-6 tibble_3.2.1
## [49] plyr_1.8.9 withr_3.0.2 evaluate_1.0.1
## [52] pillar_1.9.0 BiocManager_1.30.25 ggpubr_0.6.0
## [55] carData_3.0-5 foreach_1.5.2 ncdf4_1.23
## [58] generics_0.1.3 munsell_0.5.1 scales_1.3.0
## [61] glue_1.8.0 lazyeval_0.2.2 tools_4.5.0
## [64] mzID_1.45.0 mzR_2.41.0 ggsignif_0.6.4
## [67] XML_3.99-0.17 fs_1.6.4 grid_4.5.0
## [70] tidyr_1.3.1 MsCoreUtils_1.19.0 msdata_0.45.0
## [73] colorspace_2.1-1 GenomeInfoDbData_1.2.13 Formula_1.2-5
## [76] cli_3.6.3 fansi_1.0.6 S4Arrays_1.7.0
## [79] dplyr_1.1.4 AnnotationFilter_1.31.0 gtable_0.3.6
## [82] rstatix_0.7.2 sass_0.4.9 digest_0.6.37
## [85] SparseArray_1.7.0 ggrepel_0.9.6 farver_2.1.2
## [88] htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7
## [91] MASS_7.3-61