MSnbase +
MSnbase (Laurent Gatto and Lilley 2012) aims are providing a reproducible research framework to proteomics data analysis. It should allow researcher to easily mine mass spectrometry data, explore the data and its statistical properties and visually display these.
-MSnbase +
MSnbase also aims at being compatible with the infrastructure implemented in -Bioconductor, in particular Biobase. +Bioconductor, in particular Biobase. As such, classes developed specifically for proteomics mass spectrometry data are based on the eSet and ExpressionSet classes. The main goal is to assure seamless compatibility with existing meta data structure, accessor methods and normalisation techniques.
-This vignette illustrates MSnbase +
This vignette illustrates MSnbase utility using a dummy data sets provided with the package without describing the underlying data structures. More details can be found in the package, classes, method and function documentations. A description @@ -198,11 +198,11 @@
Parallel support is provided by the BiocParallel
and various backends including multicore (forking, default on Linux),
simple networf network of workstations (SNOW, default on Windows) using
sockets, forking or MPI among others. We refer readers to the
-documentation in BiocParallel.
+documentation in BiocParallel.
Automatic parallel processing of spectra is only established for a
certain number of spectra (per file). This value (default is 1000) can
be set with the setMSnbaseParallelThresh function.
MSnbase +
MSnbase
is able to import raw MS data stored in one of the
XML-based formats as well as peak lists in the
mfg format3.
msLevel parameter accordingly in
readMSData and in-memory data. In this document,
-we will use the itraqdata data set, provided with MSnbase.
+we will use the itraqdata data set, provided with MSnbase.
It includes feature metadata, accessible with the fData
accessor. The metadata includes identification data for the 55 MS2
spectra.
-Version 2.0 and later of MSnbase +
Version 2.0 and later of MSnbase provide a new on-disk data storage model (see the benchmarking vignette for more details). The new data backend is compatible with the orignal in-memory model. To make use of @@ -294,10 +294,10 @@
See also section @ref(sec:io2) to import quantitative data stored in
-spreadsheets into R for further processing using MSnbase.
+spreadsheets into R for further processing using MSnbase.
The MSnbase-iovignette[in R, open it with
vignette("MSnbase-io") or read it online here]
-gives a general overview of MSnbase’s
+gives a general overview of MSnbase’s
input/ouput capabilites.
See section @ref(sec:io3) for importing chromatographic data of SRM/MRM experiments.
@@ -443,7 +443,7 @@?ReporterIons for details about how to generate new
ReporterIons objects.
@@ -538,7 +538,7 @@Chromatogram objects
mtof_bpc <- chromatogram(mtof, aggregationFun = "max")
See the Chromatogram help page and the vignettes from
-the xcms package
+the xcms package
for more details and use cases, also on how to extract chromatograms for
specific ions.
openMSfile (mzML, mzXML, …).
Below we first download a raw data file from the PRIDE repository and
create an MSmap containing all the MS1 spectra between acquired
@@ -655,7 +655,7 @@ Customising your plots The MSnbase
plot methods have a logical plot parameter
(default is TRUE), that specifies if the plot should be
printed to the current device. A plot object is also (invisibly)
returned, so that it can be saved as a variable for later use or for
customisation.
MSnbase +
MSnbase
uses the package to generate plots, which can subsequently easily be
customised. More details about can be found in (Wickham 2009) (especially chapter 8) and on http://had.co.nz/ggplot2/. Finally, if a plot object has
been saved in a variable p, it is possible to obtain a
@@ -724,8 +724,8 @@
mzID from the mzID
+the mzR package
+or mzID from the mzID
package. The MSnbase package relies on the former (which is
faster) and offers a simplified interface by converting the dedicated
identification data objects into data.frames.
@@ -831,10 +831,10 @@ MSnbase +
MSnbase
is able to integrate identification data from mzIdentML
(Jones et al. 2012) files.
We first load two example files shipped with the MSnbase +
We first load two example files shipped with the MSnbase
containing raw data (as above) and the corresponding identification
results respectively. The raw data is read with the
readMSData, as demonstrated above. As can be seen, the
@@ -944,7 +944,7 @@
MSnbase +
MSnbase
is able to calculate theoretical peptide fragments via
calculateFragments.
@@ -1004,7 +1004,7 @@@@ -1317,12 +1317,12 @@Quality controlMSnbase +
MSnbase allows easy and flexible access to the data, which allows to visualise data features to assess it’s quality. Some methods are readily available, although many QC approaches will be experiment specific and @@ -1140,8 +1140,8 @@
Focusing on specific MZ values## - - - Processing information - - - ## Data loaded: Wed May 11 18:54:39 2011 ## Updated from version 0.3.0 to 0.3.1 [Fri Jul 8 20:23:25 2016] -## Curves <= 400 set to '0': Tue Apr 30 16:23:48 2024 -## Spectra cleaned: Tue Apr 30 16:23:49 2024 +## Curves <= 400 set to '0': Tue Apr 30 17:23:48 2024 +## Spectra cleaned: Tue Apr 30 17:23:48 2024 ## MSnbase version: 1.1.22 ## - - - Meta data - - - ## phenoData @@ -1215,7 +1215,7 @@
Reporter ions quantitationBiobase +that extend the well-known eSet class defined in the Biobase package. MSnSet instances are very similar to ExpressionSet objects, except for the experiment meta-data that captures MIAPE specific information. The assay data is a matrix of @@ -1249,9 +1249,9 @@
Reporter ions quantitation## - - - Processing information - - - ## Data loaded: Wed May 11 18:54:39 2011 ## Updated from version 0.3.0 to 0.3.1 [Fri Jul 8 20:23:25 2016] -## Curves <= 400 set to '0': Tue Apr 30 16:23:48 2024 -## Spectra cleaned: Tue Apr 30 16:23:49 2024 -## iTRAQ4 quantification by trapezoidation: Tue Apr 30 16:23:51 2024 +## Curves <= 400 set to '0': Tue Apr 30 17:23:48 2024 +## Spectra cleaned: Tue Apr 30 17:23:48 2024 +## iTRAQ4 quantification by trapezoidation: Tue Apr 30 17:23:50 2024 ## MSnbase version: 1.1.22
MSnbase
also supports the mzTab format, a light-weight,
tab-delimited file format for proteomics data. mzTab files
can be read into R with readMzTabData to create and
MSnSet instance.
See the MSnbase-io vignette for a general overview of MSnbase’s +
See the MSnbase-io vignette for a general overview of MSnbase’s input/ouput capabilites.
library(Rdisop)
@@ -1472,7 +1472,7 @@ Data imputationx <- impute(naset, "min")
processingData(x)
## - - - Processing information - - -
-## Data imputation using min Tue Apr 30 16:23:52 2024
+## Data imputation using min Tue Apr 30 17:23:51 2024
## MSnbase version: 1.15.6Please read ?MsCoreUtils::impute_matix() for a
description of the different methods.
normalize.quantiles function of the preprocessCore
+normalize.quantiles function of the preprocessCore
package.
quantiles.robust: Applies robust quantile
normalisation (Bolstad et al. 2003) as
implemented in the normalize.quantiles.robust function of
-the preprocessCore
+the preprocessCore
package.
vsn: Applies variance stabilisation normalization
(Huber et al. 2002) as implemented in the
-vsn2 function of the vsn
+vsn2 function of the vsn
package.
max: Each feature’s reporter intensity is divided by
the maximum of the reporter ions intensities.
MSnbase
provides one function, combineFeatures, that allows to
aggregate features stored in an MSnSet using build-in or user
defined summary function and return a new MSnSet instance. The
@@ -1696,13 +1696,13 @@
Of interest is also the iPQF spectra-to-protein
summarisation method, which integrates peptide spectra characteristics
@@ -1716,7 +1716,7 @@
Note that if samples are not multiplexed, label-free MS2 quantitation -by spectral counting is possible using MSnbase. +by spectral counting is possible using MSnbase. Once individual spectra have been assigned to peptides and proteins (see section @ref(sec:id)), it becomes straightforward to estimate protein quantities using the simple peptide counting method, as illustrated in @@ -1736,7 +1736,7 @@
Such count data could then be further analyses using dedicated count methods (originally developed for high-throughput sequencing) and -directly available for MSnSet instances in the msmsTests +directly available for MSnSet instances in the msmsTests Bioconductor package.
siquant <- quantify(msexp, method = "SIn")
processingData(siquant)## - - - Processing information - - -
-## Data loaded: Tue Apr 30 16:23:47 2024
-## Filtered 2 unidentified peptides out [Tue Apr 30 16:23:48 2024]
-## Quantitation by total ion current [Tue Apr 30 16:23:54 2024]
-## Combined 3 features into 3 using sum: Tue Apr 30 16:23:54 2024
-## Quantification by SIn [Tue Apr 30 16:23:54 2024]
+## Data loaded: Tue Apr 30 17:23:47 2024
+## Filtered 2 unidentified peptides out [Tue Apr 30 17:23:47 2024]
+## Quantitation by total ion current [Tue Apr 30 17:23:54 2024]
+## Combined 3 features into 3 using sum: Tue Apr 30 17:23:54 2024
+## Quantification by SIn [Tue Apr 30 17:23:54 2024]
## MSnbase version: 2.29.5
exprs(siquant)MSnbase +
MSnbase
provides functionality to compare spectra against each other. The first
notable function is plot. If two Spectrum2 objects
are provided plot will draw two plots: the upper and lower
@@ -1828,7 +1828,7 @@
Currently MSnbase +
Currently MSnbase
supports three different metrics to compare spectra against each other:
common to calculate the number of common peaks,
cor to calculate the Pearson correlation and
@@ -1886,7 +1886,7 @@
MSnbase +
MSnbase provides, among others, a ReporterIons object for iTRAQ 4-plex that includes the 145 peaks, called iTRAQ5. This can then be used to quantify the experiment as show in section @ref(sec:quant) to @@ -2165,7 +2165,7 @@
In summary, when experiments with different samples need to be combined (along the columns), one needs to (1) clarify the sample names @@ -2231,7 +2231,7 @@
The next code chunk illustrates the averaging function using three replicated experiments from (Tan et al. -2009) available in the pRolocdata +2009) available in the pRolocdata package.
library("pRolocdata")
@@ -2267,7 +2267,7 @@ Averaging MSnSet instances## Q7KJ73 2 2 2 2
## Q7JZN0 0 0 0 0
We are going to visualise the average data on a principle component
-(PCA) plot using the plot2D function from the pRoloc
+(PCA) plot using the plot2D function from the pRoloc
package (L. Gatto et al. 2014). In
addition, we are going to use the measure of dispersion to highlight
averages with high variability by taking, for each protein, the maximum
@@ -2295,12 +2295,12 @@
MSnbase +
MSnbase
can also be used for MSE data independent acquisition from
Waters instrument. The MSE pipeline depends on the
-Bioconductor synapter
+Bioconductor synapter
package (Bond et al. 2013) that produces
-MSnSet instances for indvidual acquisitions. The MSnbase
+MSnSet instances for indvidual acquisitions. The MSnbase
infrastructure can subsequently be used to further combine experiments,
as shown in section @ref(sec:comb2) and apply top3 quantitation
using the topN method.
## R version 4.3.3 (2024-02-29)
-## Platform: x86_64-pc-linux-gnu (64-bit)
+## R version 4.4.0 Patched (2024-04-24 r86483)
+## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
@@ -2332,98 +2332,99 @@ Session information## [8] methods base
##
## other attached packages:
-## [1] gplots_3.1.3.1 msdata_0.42.0 pRoloc_1.42.0
-## [4] BiocParallel_1.36.0 MLInterfaces_1.82.0 cluster_2.1.6
-## [7] annotate_1.80.0 XML_3.99-0.16.1 AnnotationDbi_1.64.1
-## [10] IRanges_2.36.0 pRolocdata_1.40.0 Rdisop_1.62.0
-## [13] zoo_1.8-12 MSnbase_2.29.5 ProtGenerics_1.34.0
-## [16] S4Vectors_0.40.2 mzR_2.36.0 Rcpp_1.0.12
-## [19] Biobase_2.62.0 BiocGenerics_0.48.1 ggplot2_3.5.1
-## [22] BiocStyle_2.30.0
+## [1] gplots_3.1.3.1 msdata_0.43.0 pRoloc_1.43.2
+## [4] BiocParallel_1.37.1 MLInterfaces_1.83.0 cluster_2.1.6
+## [7] annotate_1.81.2 XML_3.99-0.16.1 AnnotationDbi_1.65.2
+## [10] IRanges_2.37.1 pRolocdata_1.41.0 Rdisop_1.63.0
+## [13] zoo_1.8-12 MSnbase_2.29.5 ProtGenerics_1.35.4
+## [16] S4Vectors_0.41.7 mzR_2.37.3 Rcpp_1.0.12
+## [19] Biobase_2.63.1 BiocGenerics_0.49.1 ggplot2_3.5.1
+## [22] BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
-## [1] splines_4.3.3 bitops_1.0-7
+## [1] splines_4.4.0 bitops_1.0-7
## [3] filelock_1.0.3 tibble_3.2.1
## [5] hardhat_1.3.1 preprocessCore_1.61.0
## [7] pROC_1.18.5 rpart_4.1.23
-## [9] lifecycle_1.0.4 doParallel_1.0.17
-## [11] globals_0.16.3 lattice_0.22-6
-## [13] MASS_7.3-60.0.1 MultiAssayExperiment_1.28.0
-## [15] dendextend_1.17.1 magrittr_2.0.3
-## [17] limma_3.58.1 plotly_4.10.4
-## [19] sass_0.4.9 rmarkdown_2.26
-## [21] jquerylib_0.1.4 yaml_2.3.8
-## [23] MsCoreUtils_1.14.1 DBI_1.2.2
-## [25] RColorBrewer_1.1-3 lubridate_1.9.3
-## [27] abind_1.4-5 zlibbioc_1.48.2
-## [29] GenomicRanges_1.54.1 purrr_1.0.2
-## [31] mixtools_2.0.0 AnnotationFilter_1.26.0
-## [33] RCurl_1.98-1.14 nnet_7.3-19
-## [35] rappdirs_0.3.3 ipred_0.9-14
-## [37] lava_1.8.0 GenomeInfoDbData_1.2.11
-## [39] listenv_0.9.1 parallelly_1.37.1
-## [41] pkgdown_2.0.9.9000 ncdf4_1.22
-## [43] codetools_0.2-20 DelayedArray_0.28.0
-## [45] xml2_1.3.6 tidyselect_1.2.1
-## [47] farver_2.1.1 viridis_0.6.5
-## [49] matrixStats_1.3.0 BiocFileCache_2.10.2
-## [51] jsonlite_1.8.8 caret_6.0-94
-## [53] e1071_1.7-14 survival_3.6-4
-## [55] iterators_1.0.14 systemfonts_1.0.6
-## [57] foreach_1.5.2 segmented_2.0-4
-## [59] tools_4.3.3 progress_1.2.3
-## [61] ragg_1.3.0 glue_1.7.0
-## [63] prodlim_2023.08.28 gridExtra_2.3
-## [65] SparseArray_1.2.4 mgcv_1.9-1
-## [67] xfun_0.43 MatrixGenerics_1.14.0
-## [69] GenomeInfoDb_1.38.8 dplyr_1.1.4
-## [71] withr_3.0.0 BiocManager_1.30.22
-## [73] fastmap_1.1.1 fansi_1.0.6
-## [75] caTools_1.18.2 digest_0.6.35
-## [77] timechange_0.3.0 R6_2.5.1
-## [79] textshaping_0.3.7 colorspace_2.1-0
-## [81] gtools_3.9.5 lpSolve_5.6.20
-## [83] biomaRt_2.58.2 RSQLite_2.3.6
-## [85] utf8_1.2.4 tidyr_1.3.1
-## [87] generics_0.1.3 hexbin_1.28.3
-## [89] data.table_1.15.4 recipes_1.0.10
-## [91] FNN_1.1.4 class_7.3-22
-## [93] prettyunits_1.2.0 PSMatch_1.6.0
-## [95] httr_1.4.7 htmlwidgets_1.6.4
-## [97] S4Arrays_1.2.1 ModelMetrics_1.2.2.2
-## [99] pkgconfig_2.0.3 gtable_0.3.5
-## [101] timeDate_4032.109 blob_1.2.4
-## [103] impute_1.76.0 XVector_0.42.0
-## [105] htmltools_0.5.8.1 bookdown_0.39
-## [107] MALDIquant_1.22.2 clue_0.3-65
-## [109] scales_1.3.0 png_0.1-8
-## [111] gower_1.0.1 knitr_1.46
-## [113] reshape2_1.4.4 coda_0.19-4.1
-## [115] nlme_3.1-164 curl_5.2.1
-## [117] proxy_0.4-27 cachem_1.0.8
-## [119] stringr_1.5.1 KernSmooth_2.23-22
-## [121] parallel_4.3.3 mzID_1.40.0
-## [123] vsn_3.70.0 desc_1.4.3
-## [125] pillar_1.9.0 vctrs_0.6.5
-## [127] pcaMethods_1.94.0 randomForest_4.7-1.1
-## [129] dbplyr_2.5.0 xtable_1.8-4
-## [131] evaluate_0.23 mvtnorm_1.2-4
-## [133] cli_3.6.2 compiler_4.3.3
-## [135] rlang_1.1.3 crayon_1.5.2
-## [137] future.apply_1.11.2 labeling_0.4.3
-## [139] LaplacesDemon_16.1.6 mclust_6.1.1
-## [141] QFeatures_1.12.0 affy_1.80.0
-## [143] plyr_1.8.9 fs_1.6.4
-## [145] stringi_1.8.3 viridisLite_0.4.2
-## [147] munsell_0.5.1 Biostrings_2.70.3
-## [149] lazyeval_0.2.2 Matrix_1.6-5
-## [151] hms_1.1.3 future_1.33.2
-## [153] bit64_4.0.5 KEGGREST_1.42.0
-## [155] statmod_1.5.0 highr_0.10
-## [157] SummarizedExperiment_1.32.0 kernlab_0.9-32
-## [159] igraph_2.0.3 memoise_2.0.1
-## [161] affyio_1.72.0 bslib_0.7.0
-## [163] sampling_2.10 bit_4.0.5
+## [9] lifecycle_1.0.4 httr2_1.0.1
+## [11] doParallel_1.0.17 globals_0.16.3
+## [13] lattice_0.22-6 MASS_7.3-60.2
+## [15] MultiAssayExperiment_1.29.2 dendextend_1.17.1
+## [17] magrittr_2.0.3 limma_3.59.10
+## [19] plotly_4.10.4 sass_0.4.9
+## [21] rmarkdown_2.26 jquerylib_0.1.4
+## [23] yaml_2.3.8 MsCoreUtils_1.15.7
+## [25] DBI_1.2.2 RColorBrewer_1.1-3
+## [27] lubridate_1.9.3 abind_1.4-5
+## [29] zlibbioc_1.49.3 GenomicRanges_1.55.4
+## [31] purrr_1.0.2 mixtools_2.0.0
+## [33] AnnotationFilter_1.27.0 RCurl_1.98-1.14
+## [35] nnet_7.3-19 rappdirs_0.3.3
+## [37] ipred_0.9-14 lava_1.8.0
+## [39] GenomeInfoDbData_1.2.12 listenv_0.9.1
+## [41] parallelly_1.37.1 pkgdown_2.0.9.9000
+## [43] ncdf4_1.22 codetools_0.2-20
+## [45] DelayedArray_0.29.9 xml2_1.3.6
+## [47] tidyselect_1.2.1 farver_2.1.1
+## [49] UCSC.utils_0.99.7 viridis_0.6.5
+## [51] matrixStats_1.3.0 BiocFileCache_2.11.2
+## [53] jsonlite_1.8.8 caret_6.0-94
+## [55] e1071_1.7-14 survival_3.6-4
+## [57] iterators_1.0.14 systemfonts_1.0.6
+## [59] foreach_1.5.2 segmented_2.0-4
+## [61] tools_4.4.0 progress_1.2.3
+## [63] ragg_1.3.0 glue_1.7.0
+## [65] prodlim_2023.08.28 gridExtra_2.3
+## [67] SparseArray_1.3.7 mgcv_1.9-1
+## [69] xfun_0.43 MatrixGenerics_1.15.1
+## [71] GenomeInfoDb_1.39.14 dplyr_1.1.4
+## [73] withr_3.0.0 BiocManager_1.30.22
+## [75] fastmap_1.1.1 fansi_1.0.6
+## [77] caTools_1.18.2 digest_0.6.35
+## [79] timechange_0.3.0 R6_2.5.1
+## [81] textshaping_0.3.7 colorspace_2.1-0
+## [83] gtools_3.9.5 lpSolve_5.6.20
+## [85] biomaRt_2.59.1 RSQLite_2.3.6
+## [87] utf8_1.2.4 tidyr_1.3.1
+## [89] generics_0.1.3 hexbin_1.28.3
+## [91] data.table_1.15.4 recipes_1.0.10
+## [93] FNN_1.1.4 class_7.3-22
+## [95] prettyunits_1.2.0 PSMatch_1.7.2
+## [97] httr_1.4.7 htmlwidgets_1.6.4
+## [99] S4Arrays_1.3.7 ModelMetrics_1.2.2.2
+## [101] pkgconfig_2.0.3 gtable_0.3.5
+## [103] timeDate_4032.109 blob_1.2.4
+## [105] impute_1.77.0 XVector_0.43.1
+## [107] htmltools_0.5.8.1 bookdown_0.39
+## [109] MALDIquant_1.22.2 clue_0.3-65
+## [111] scales_1.3.0 png_0.1-8
+## [113] gower_1.0.1 knitr_1.46
+## [115] reshape2_1.4.4 coda_0.19-4.1
+## [117] nlme_3.1-164 curl_5.2.1
+## [119] proxy_0.4-27 cachem_1.0.8
+## [121] stringr_1.5.1 KernSmooth_2.23-22
+## [123] parallel_4.4.0 mzID_1.41.0
+## [125] vsn_3.71.1 desc_1.4.3
+## [127] pillar_1.9.0 vctrs_0.6.5
+## [129] pcaMethods_1.95.0 randomForest_4.7-1.1
+## [131] dbplyr_2.5.0 xtable_1.8-4
+## [133] evaluate_0.23 mvtnorm_1.2-4
+## [135] cli_3.6.2 compiler_4.4.0
+## [137] rlang_1.1.3 crayon_1.5.2
+## [139] future.apply_1.11.2 labeling_0.4.3
+## [141] LaplacesDemon_16.1.6 mclust_6.1.1
+## [143] QFeatures_1.13.7 affy_1.81.0
+## [145] plyr_1.8.9 fs_1.6.4
+## [147] stringi_1.8.3 viridisLite_0.4.2
+## [149] munsell_0.5.1 Biostrings_2.71.6
+## [151] lazyeval_0.2.2 Matrix_1.7-0
+## [153] hms_1.1.3 future_1.33.2
+## [155] bit64_4.0.5 KEGGREST_1.43.1
+## [157] statmod_1.5.0 highr_0.10
+## [159] SummarizedExperiment_1.33.3 kernlab_0.9-32
+## [161] igraph_2.0.3 memoise_2.0.1
+## [163] affyio_1.73.0 bslib_0.7.0
+## [165] sampling_2.10 bit_4.0.5This part will be automatically updated when the object is modified with it’s ad hoc methods, as illustrated later.↩︎
The identification data can also be passed as dedicated
-identification objects such as mzRident from the mzR package
-or mzID from thr mzID
+identification objects such as mzRident from the mzR package
+or mzID from thr mzID
package, or as a data.frame - see
?addIdentifionData for details.↩︎
The code to generate the histograms has been contributed diff --git a/articles/v01-MSnbase-demo_files/figure-html/idvis-1.png b/articles/v01-MSnbase-demo_files/figure-html/idvis-1.png index 6203c03b..1085b04c 100644 Binary files a/articles/v01-MSnbase-demo_files/figure-html/idvis-1.png and b/articles/v01-MSnbase-demo_files/figure-html/idvis-1.png differ diff --git a/articles/v01-MSnbase-demo_files/figure-html/normPlot-1.png b/articles/v01-MSnbase-demo_files/figure-html/normPlot-1.png index 5757372c..59409fb6 100644 Binary files a/articles/v01-MSnbase-demo_files/figure-html/normPlot-1.png and b/articles/v01-MSnbase-demo_files/figure-html/normPlot-1.png differ diff --git a/articles/v01-MSnbase-demo_files/figure-html/plot2Davg-1.png b/articles/v01-MSnbase-demo_files/figure-html/plot2Davg-1.png index 33a37f2e..8cc4de85 100644 Binary files a/articles/v01-MSnbase-demo_files/figure-html/plot2Davg-1.png and b/articles/v01-MSnbase-demo_files/figure-html/plot2Davg-1.png differ diff --git a/articles/v02-MSnbase-io.html b/articles/v02-MSnbase-io.html index 828429ff..27de16c5 100644 --- a/articles/v02-MSnbase-io.html +++ b/articles/v02-MSnbase-io.html @@ -138,7 +138,7 @@
MSnbase’s +
MSnbase’s aims are to facilitate the reproducible analysis of mass spectrometry data within the R environment, from raw data import and processing, feature quantification, quantification and statistical analysis of the @@ -158,7 +158,7 @@
mzML (Martens et al.
2010), can be imported with the readMSData method,
-which makes use of the mzR package
+which makes use of the mzR package
to create MSnExp objects. The files can be in profile or
centroided mode. See ?readMSData for details.
Data from mzML files containing chromatographic data
@@ -183,7 +183,7 @@
?readMSnSet for
details.
-MSnbase +
MSnbase
also supports the mzTab format2, a light-weight,
tab-delimited file format for proteomics data developed within the
Proteomics Standards Initiative (PSI). mzTab files can be
@@ -247,7 +247,7 @@
MSnSet fr
This section describes the generation of MSnSet objects
using data available in a text-based spreadsheet. This entry point into
-R and MSnbase
+R and MSnbase
allows to import data processed by any of the third party
mass-spectrometry processing software available and proceed with data
exploration, normalisation and statistical analysis using functions
@@ -258,8 +258,8 @@
We start by describing the csv to be used as input using
the read.csv function.
MSnSet class
@@ -479,8 +479,8 @@
sessionInfo()## R version 4.3.3 (2024-02-29)
-## Platform: x86_64-pc-linux-gnu (64-bit)
+## R version 4.4.0 Patched (2024-04-24 r86483)
+## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
@@ -503,58 +503,60 @@ Session information## [8] base
##
## other attached packages:
-## [1] pRolocdata_1.40.0 MSnbase_2.29.5 ProtGenerics_1.34.0
-## [4] S4Vectors_0.40.2 mzR_2.36.0 Rcpp_1.0.12
-## [7] Biobase_2.62.0 BiocGenerics_0.48.1 BiocStyle_2.30.0
+## [1] pRolocdata_1.41.0 MSnbase_2.29.5 ProtGenerics_1.35.4
+## [4] S4Vectors_0.41.7 mzR_2.37.3 Rcpp_1.0.12
+## [7] Biobase_2.63.1 BiocGenerics_0.49.1 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 rlang_1.1.3
## [3] magrittr_2.0.3 clue_0.3-65
-## [5] matrixStats_1.3.0 compiler_4.3.3
+## [5] matrixStats_1.3.0 compiler_4.4.0
## [7] systemfonts_1.0.6 vctrs_0.6.5
## [9] pkgconfig_2.0.3 crayon_1.5.2
-## [11] fastmap_1.1.1 XVector_0.42.0
+## [11] fastmap_1.1.1 XVector_0.43.1
## [13] utf8_1.2.4 rmarkdown_2.26
-## [15] preprocessCore_1.61.0 ragg_1.3.0
-## [17] purrr_1.0.2 xfun_0.43
-## [19] MultiAssayExperiment_1.28.0 zlibbioc_1.48.2
-## [21] cachem_1.0.8 GenomeInfoDb_1.38.8
-## [23] jsonlite_1.8.8 DelayedArray_0.28.0
-## [25] BiocParallel_1.36.0 parallel_4.3.3
-## [27] cluster_2.1.6 R6_2.5.1
-## [29] bslib_0.7.0 limma_3.58.1
-## [31] GenomicRanges_1.54.1 jquerylib_0.1.4
-## [33] bookdown_0.39 SummarizedExperiment_1.32.0
-## [35] iterators_1.0.14 knitr_1.46
-## [37] IRanges_2.36.0 Matrix_1.6-5
-## [39] igraph_2.0.3 tidyselect_1.2.1
-## [41] abind_1.4-5 yaml_2.3.8
-## [43] doParallel_1.0.17 codetools_0.2-20
-## [45] affy_1.80.0 lattice_0.22-6
-## [47] tibble_3.2.1 plyr_1.8.9
-## [49] evaluate_0.23 desc_1.4.3
-## [51] pillar_1.9.0 affyio_1.72.0
-## [53] BiocManager_1.30.22 MatrixGenerics_1.14.0
-## [55] foreach_1.5.2 MALDIquant_1.22.2
-## [57] ncdf4_1.22 generics_0.1.3
-## [59] RCurl_1.98-1.14 ggplot2_3.5.1
-## [61] munsell_0.5.1 scales_1.3.0
-## [63] glue_1.7.0 lazyeval_0.2.2
-## [65] tools_4.3.3 mzID_1.40.0
-## [67] QFeatures_1.12.0 vsn_3.70.0
-## [69] fs_1.6.4 XML_3.99-0.16.1
-## [71] grid_4.3.3 impute_1.76.0
-## [73] MsCoreUtils_1.14.1 colorspace_2.1-0
-## [75] GenomeInfoDbData_1.2.11 PSMatch_1.6.0
-## [77] cli_3.6.2 textshaping_0.3.7
-## [79] fansi_1.0.6 S4Arrays_1.2.1
-## [81] dplyr_1.1.4 AnnotationFilter_1.26.0
-## [83] pcaMethods_1.94.0 gtable_0.3.5
-## [85] sass_0.4.9 digest_0.6.35
-## [87] SparseArray_1.2.4 htmlwidgets_1.6.4
-## [89] memoise_2.0.1 htmltools_0.5.8.1
-## [91] pkgdown_2.0.9.9000 lifecycle_1.0.4
-## [93] statmod_1.5.0 MASS_7.3-60.0.1
+## [15] UCSC.utils_0.99.7 preprocessCore_1.61.0
+## [17] ragg_1.3.0 purrr_1.0.2
+## [19] xfun_0.43 MultiAssayExperiment_1.29.2
+## [21] zlibbioc_1.49.3 cachem_1.0.8
+## [23] GenomeInfoDb_1.39.14 jsonlite_1.8.8
+## [25] DelayedArray_0.29.9 BiocParallel_1.37.1
+## [27] parallel_4.4.0 cluster_2.1.6
+## [29] R6_2.5.1 bslib_0.7.0
+## [31] limma_3.59.10 GenomicRanges_1.55.4
+## [33] jquerylib_0.1.4 bookdown_0.39
+## [35] SummarizedExperiment_1.33.3 iterators_1.0.14
+## [37] knitr_1.46 IRanges_2.37.1
+## [39] Matrix_1.7-0 igraph_2.0.3
+## [41] tidyselect_1.2.1 abind_1.4-5
+## [43] yaml_2.3.8 doParallel_1.0.17
+## [45] codetools_0.2-20 affy_1.81.0
+## [47] lattice_0.22-6 tibble_3.2.1
+## [49] plyr_1.8.9 evaluate_0.23
+## [51] desc_1.4.3 pillar_1.9.0
+## [53] affyio_1.73.0 BiocManager_1.30.22
+## [55] MatrixGenerics_1.15.1 foreach_1.5.2
+## [57] MALDIquant_1.22.2 ncdf4_1.22
+## [59] generics_0.1.3 RCurl_1.98-1.14
+## [61] ggplot2_3.5.1 munsell_0.5.1
+## [63] scales_1.3.0 glue_1.7.0
+## [65] lazyeval_0.2.2 tools_4.4.0
+## [67] mzID_1.41.0 QFeatures_1.13.7
+## [69] vsn_3.71.1 fs_1.6.4
+## [71] XML_3.99-0.16.1 grid_4.4.0
+## [73] impute_1.77.0 tidyr_1.3.1
+## [75] MsCoreUtils_1.15.7 colorspace_2.1-0
+## [77] GenomeInfoDbData_1.2.12 PSMatch_1.7.2
+## [79] cli_3.6.2 textshaping_0.3.7
+## [81] fansi_1.0.6 S4Arrays_1.3.7
+## [83] dplyr_1.1.4 AnnotationFilter_1.27.0
+## [85] pcaMethods_1.95.0 gtable_0.3.5
+## [87] sass_0.4.9 digest_0.6.35
+## [89] SparseArray_1.3.7 htmlwidgets_1.6.4
+## [91] memoise_2.0.1 htmltools_0.5.8.1
+## [93] pkgdown_2.0.9.9000 lifecycle_1.0.4
+## [95] httr_1.4.7 statmod_1.5.0
+## [97] MASS_7.3-60.2In this vignette, we will document various timings and benchmarkings -of the MSnbase +of the MSnbase version 2, that focuses on on-disk data access (as opposed to in-memory). More details about the new implementation are documented in the respective classes manual pages and in
@@ -123,7 +123,7 @@
library("msdata")
@@ -131,7 +131,7 @@ Introduction= "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz")
basename(f)
## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz"We need to load the MSnbase +
We need to load the MSnbase
package and set the session-wide verbosity flag to
FALSE.
@@ -154,7 +154,7 @@Reading data= "inMemory", centroided = TRUE))
## user system elapsed
-## 3.653 0.120 3.779Next, we use the readMSData function to generate an
on-disk representation of the same data by setting
mode = "onDisk".
## user system elapsed
-## 1.269 0.024 1.287Creating the on-disk experiment is considerable faster and scales to much bigger, multi-file data, both in terms of object creation time, but also in terms of object size (see next section). We must of course make @@ -217,16 +217,16 @@
## Unit: microseconds
-## expr min lq mean median uq
-## spectra(inmem) 58.780 72.185 143.8212 165.6485 187.941
-## inmem[[200]] 16.961 18.755 40.6848 43.3460 61.404
-## spectra(ondisk) 292370.549 292919.023 295463.0374 294919.4175 296032.417
-## ondisk[[200]] 161233.552 162128.079 164210.7864 162841.5990 164913.371
+## expr min lq mean median uq
+## spectra(inmem) 58.760 62.556 135.5678 161.256 175.577
+## inmem[[200]] 18.505 18.755 40.6592 43.852 57.698
+## spectra(ondisk) 295593.670 296351.244 299130.7132 297202.829 301726.968
+## ondisk[[200]] 162731.286 162840.390 165374.5058 164705.441 165468.135
## max neval
-## 206.405 10
-## 64.360 10
-## 301520.457 10
-## 171958.013 10While it takes order or magnitudes more time to access the data on-the-fly rather than a pre-generated spectrum, accessing all spectra is only marginally slower than accessing all spectra, as most of the @@ -248,11 +248,11 @@
## user system elapsed
-## 0.071 0.000 0.072
system.time(ondisk[i])## user system elapsed
-## 0.01 0.00 0.01Operations on the spectra data, such as peak picking, smoothing, cleaning, … are cleverly cached and only applied when the data is accessed, to minimise file access overhead. Finally, specific operations @@ -269,12 +269,12 @@
## user system elapsed
-## 5.735 1.174 3.682
system.time(eod <- quantify(ondisk[1:100], reporters = TMT6,
method = "max"))## user system elapsed
-## 0.221 0.055 0.257
all.equal(eim, eod, check.attributes = FALSE)## [1] TRUEas(, "MSnExp"). We would need mzML write
-support in mzR to be
+support in mzR to be
able to implement serialisation for on-disk data.
vignette(package = "MSnbase")This document is not a replacement for the individual manual pages, -that document the slots of the MSnbase +that document the slots of the MSnbase classes. It is a centralised high-level description of the package design.
-MSnbase -aims at being compatible with the Biobase +
MSnbase
+aims at being compatible with the Biobase
infrastructure (Gentleman et al. 2004).
Many meta data structures that are used in eSet and
associated classes are also used here. As such, knowledge of the
Biobase development and the new eSet vignette would be
beneficial; the vignette can directly be accessed with
vignette("BiobaseDevelopment", package="Biobase").
The initial goal is to use the MSnbase +
The initial goal is to use the MSnbase infrastructure for MS2 labelled (iTRAQ (Ross et al. 2004) and TMT (Thompson et al. 2003)) and label-free (spectral counting, index and abundance) @@ -215,11 +215,11 @@
All classes have a .__classVersion__ slot, of class
-Versioned from the Biobase
+Versioned from the Biobase
package. This slot documents the class version for any instance to be
used for debugging and object update purposes. Any change in a class
implementation should trigger a version change.
MSnExp objects, not kept in memory (in
the assayData slot), but are fetched from the original
-files on-demand. Because mzML files are indexed, using the mzR package
+files on-demand. Because mzML files are indexed, using the mzR package
to read the relevant spectrum data is fast and only moderately slower
than for in-memory MSnExp1.
To keep track of data manipulation steps that are applied to spectrum @@ -326,7 +326,7 @@
OnDiskMSnExp
objects have an argument BPPARAM and users can set a
PARALLEL_THRESH option flag2 that enables to define
-how and when parallel processing should be performed (using the BiocParallel
+how and when parallel processing should be performed (using the BiocParallel
package).
Note that all data manipulations that are not applied to M/Z or intensity values of a spectrum (e.g. sub-setting by retention time etc) @@ -445,7 +445,7 @@
It also documents the raw data file from which the data originates
-(files slot) and the MSnbase
+(files slot) and the MSnbase
version that was in use when the MSnProcess instance, and
hence the MSnExp/MSnSet objects, were
originally created.
MSnbase +
sessionInfo()
-## R version 4.3.3 (2024-02-29)
-## Platform: x86_64-pc-linux-gnu (64-bit)
+## R version 4.4.0 Patched (2024-04-24 r86483)
+## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
@@ -758,58 +757,60 @@ Session information## [8] base
##
## other attached packages:
-## [1] MSnbase_2.29.5 ProtGenerics_1.34.0 S4Vectors_0.40.2
-## [4] mzR_2.36.0 Rcpp_1.0.12 Biobase_2.62.0
-## [7] BiocGenerics_0.48.1 BiocStyle_2.30.0
+## [1] MSnbase_2.29.5 ProtGenerics_1.35.4 S4Vectors_0.41.7
+## [4] mzR_2.37.3 Rcpp_1.0.12 Biobase_2.63.1
+## [7] BiocGenerics_0.49.1 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 rlang_1.1.3
## [3] magrittr_2.0.3 clue_0.3-65
-## [5] matrixStats_1.3.0 compiler_4.3.3
+## [5] matrixStats_1.3.0 compiler_4.4.0
## [7] systemfonts_1.0.6 vctrs_0.6.5
## [9] pkgconfig_2.0.3 crayon_1.5.2
-## [11] fastmap_1.1.1 XVector_0.42.0
+## [11] fastmap_1.1.1 XVector_0.43.1
## [13] utf8_1.2.4 rmarkdown_2.26
-## [15] preprocessCore_1.61.0 ragg_1.3.0
-## [17] purrr_1.0.2 xfun_0.43
-## [19] MultiAssayExperiment_1.28.0 zlibbioc_1.48.2
-## [21] cachem_1.0.8 GenomeInfoDb_1.38.8
-## [23] jsonlite_1.8.8 DelayedArray_0.28.0
-## [25] BiocParallel_1.36.0 parallel_4.3.3
-## [27] cluster_2.1.6 R6_2.5.1
-## [29] bslib_0.7.0 limma_3.58.1
-## [31] GenomicRanges_1.54.1 jquerylib_0.1.4
-## [33] bookdown_0.39 SummarizedExperiment_1.32.0
-## [35] iterators_1.0.14 knitr_1.46
-## [37] IRanges_2.36.0 Matrix_1.6-5
-## [39] igraph_2.0.3 tidyselect_1.2.1
-## [41] abind_1.4-5 yaml_2.3.8
-## [43] doParallel_1.0.17 codetools_0.2-20
-## [45] affy_1.80.0 lattice_0.22-6
-## [47] tibble_3.2.1 plyr_1.8.9
-## [49] evaluate_0.23 desc_1.4.3
-## [51] pillar_1.9.0 affyio_1.72.0
-## [53] BiocManager_1.30.22 MatrixGenerics_1.14.0
-## [55] foreach_1.5.2 MALDIquant_1.22.2
-## [57] ncdf4_1.22 generics_0.1.3
-## [59] RCurl_1.98-1.14 ggplot2_3.5.1
-## [61] munsell_0.5.1 scales_1.3.0
-## [63] glue_1.7.0 lazyeval_0.2.2
-## [65] tools_4.3.3 mzID_1.40.0
-## [67] QFeatures_1.12.0 vsn_3.70.0
-## [69] fs_1.6.4 XML_3.99-0.16.1
-## [71] grid_4.3.3 impute_1.76.0
-## [73] MsCoreUtils_1.14.1 colorspace_2.1-0
-## [75] GenomeInfoDbData_1.2.11 PSMatch_1.6.0
-## [77] cli_3.6.2 textshaping_0.3.7
-## [79] fansi_1.0.6 S4Arrays_1.2.1
-## [81] dplyr_1.1.4 AnnotationFilter_1.26.0
-## [83] pcaMethods_1.94.0 gtable_0.3.5
-## [85] sass_0.4.9 digest_0.6.35
-## [87] SparseArray_1.2.4 htmlwidgets_1.6.4
-## [89] memoise_2.0.1 htmltools_0.5.8.1
-## [91] pkgdown_2.0.9.9000 lifecycle_1.0.4
-## [93] statmod_1.5.0 MASS_7.3-60.0.1
+## [15] UCSC.utils_0.99.7 preprocessCore_1.61.0
+## [17] ragg_1.3.0 purrr_1.0.2
+## [19] xfun_0.43 MultiAssayExperiment_1.29.2
+## [21] zlibbioc_1.49.3 cachem_1.0.8
+## [23] GenomeInfoDb_1.39.14 jsonlite_1.8.8
+## [25] DelayedArray_0.29.9 BiocParallel_1.37.1
+## [27] parallel_4.4.0 cluster_2.1.6
+## [29] R6_2.5.1 bslib_0.7.0
+## [31] limma_3.59.10 GenomicRanges_1.55.4
+## [33] jquerylib_0.1.4 bookdown_0.39
+## [35] SummarizedExperiment_1.33.3 iterators_1.0.14
+## [37] knitr_1.46 IRanges_2.37.1
+## [39] Matrix_1.7-0 igraph_2.0.3
+## [41] tidyselect_1.2.1 abind_1.4-5
+## [43] yaml_2.3.8 doParallel_1.0.17
+## [45] codetools_0.2-20 affy_1.81.0
+## [47] lattice_0.22-6 tibble_3.2.1
+## [49] plyr_1.8.9 evaluate_0.23
+## [51] desc_1.4.3 pillar_1.9.0
+## [53] affyio_1.73.0 BiocManager_1.30.22
+## [55] MatrixGenerics_1.15.1 foreach_1.5.2
+## [57] MALDIquant_1.22.2 ncdf4_1.22
+## [59] generics_0.1.3 RCurl_1.98-1.14
+## [61] ggplot2_3.5.1 munsell_0.5.1
+## [63] scales_1.3.0 glue_1.7.0
+## [65] lazyeval_0.2.2 tools_4.4.0
+## [67] mzID_1.41.0 QFeatures_1.13.7
+## [69] vsn_3.71.1 fs_1.6.4
+## [71] XML_3.99-0.16.1 grid_4.4.0
+## [73] impute_1.77.0 tidyr_1.3.1
+## [75] MsCoreUtils_1.15.7 colorspace_2.1-0
+## [77] GenomeInfoDbData_1.2.12 PSMatch_1.7.2
+## [79] cli_3.6.2 textshaping_0.3.7
+## [81] fansi_1.0.6 S4Arrays_1.3.7
+## [83] dplyr_1.1.4 AnnotationFilter_1.27.0
+## [85] pcaMethods_1.95.0 gtable_0.3.5
+## [87] sass_0.4.9 digest_0.6.35
+## [89] SparseArray_1.3.7 htmlwidgets_1.6.4
+## [91] memoise_2.0.1 htmltools_0.5.8.1
+## [93] pkgdown_2.0.9.9000 lifecycle_1.0.4
+## [95] httr_1.4.7 statmod_1.5.0
+## [97] MASS_7.3-60.2library("pRolocdata")
#>
-#> This is pRolocdata version 1.40.0.
+#> This is pRolocdata version 1.41.0.
#> Use 'pRolocdata()' to list available data sets.
data(tan2009r1)
data(tan2009r2)
diff --git a/reference/FeaturesOfInterest-class.html b/reference/FeaturesOfInterest-class.html
index c8b0ed7a..09cfd7fc 100644
--- a/reference/FeaturesOfInterest-class.html
+++ b/reference/FeaturesOfInterest-class.html
@@ -267,7 +267,7 @@ Examples
object = tan2009r1)
x
#> Traceable object of class "FeaturesOfInterest"
-#> Created on Tue Apr 30 16:21:31 2024
+#> Created on Tue Apr 30 17:21:30 2024
#> Description:
#> A traceable test set of features of interest
#> 10 features of interest:
@@ -283,7 +283,7 @@ Examples
fnames = featureNames(tan2009r1)[111:113])
y
#> Object of class "FeaturesOfInterest"
-#> Created on Tue Apr 30 16:21:31 2024
+#> Created on Tue Apr 30 17:21:30 2024
#> Description:
#> Non-traceable features of interest
#> 3 features of interest:
@@ -301,7 +301,7 @@ Examples
FeaturesOfInterest(description = "This work, but not traceable",
fnames = c("A", "Z", featureNames(tan2009r1)))
#> Object of class "FeaturesOfInterest"
-#> Created on Tue Apr 30 16:21:31 2024
+#> Created on Tue Apr 30 17:21:30 2024
#> Description:
#> This work, but not traceable
#> 890 features of interest:
@@ -324,14 +324,14 @@ Examples
#> A collection of 1 features of interest.
xx[[1]]
#> Traceable object of class "FeaturesOfInterest"
-#> Created on Tue Apr 30 16:21:31 2024
+#> Created on Tue Apr 30 17:21:30 2024
#> Description:
#> A traceable test set of features of interest
#> 10 features of interest:
#> P20353, P53501 ... Q9VCK0, Q9VIU7
xx[["A"]]
#> Traceable object of class "FeaturesOfInterest"
-#> Created on Tue Apr 30 16:21:31 2024
+#> Created on Tue Apr 30 17:21:30 2024
#> Description:
#> A traceable test set of features of interest
#> 10 features of interest:
@@ -345,7 +345,7 @@ Examples
foi(xx)
#> $A
#> Traceable object of class "FeaturesOfInterest"
-#> Created on Tue Apr 30 16:21:31 2024
+#> Created on Tue Apr 30 17:21:30 2024
#> Description:
#> A traceable test set of features of interest
#> 10 features of interest:
@@ -353,7 +353,7 @@ Examples
#>
#> $B
#> Object of class "FeaturesOfInterest"
-#> Created on Tue Apr 30 16:21:31 2024
+#> Created on Tue Apr 30 17:21:30 2024
#> Description:
#> Non-traceable features of interest
#> 3 features of interest:
diff --git a/reference/MSnExp-class.html b/reference/MSnExp-class.html
index 9797a633..3250a4e0 100644
--- a/reference/MSnExp-class.html
+++ b/reference/MSnExp-class.html
@@ -462,7 +462,7 @@ Examples
#> Number of spectra: 5
#> MSn retention times: 25:01 - 25:02 minutes
#> - - - Processing information - - -
-#> Data loaded: Tue Apr 30 16:21:36 2024
+#> Data loaded: Tue Apr 30 17:21:36 2024
#> MSnbase version: 2.29.5
#> - - - Meta data - - -
#> phenoData
diff --git a/reference/MSnSet-class.html b/reference/MSnSet-class.html
index 265ba1a2..4dfb9700 100644
--- a/reference/MSnSet-class.html
+++ b/reference/MSnSet-class.html
@@ -678,7 +678,7 @@ Examples
#> - - - Processing information - - -
#> Data loaded: Wed May 11 18:54:39 2011
#> iTRAQ4 quantification by trapezoidation: Wed Apr 1 21:41:53 2015
-#> Subset [55,4][6,4] Tue Apr 30 16:21:36 2024
+#> Subset [55,4][6,4] Tue Apr 30 17:21:37 2024
#> MSnbase version: 1.1.22
as(msnset, "ExpressionSet")
diff --git a/reference/MSpectra.html b/reference/MSpectra.html
index f244dcca..4705ce91 100644
--- a/reference/MSpectra.html
+++ b/reference/MSpectra.html
@@ -511,7 +511,7 @@ Examples
## Evaluate the written output. The ID of each spectrum (defined in the
## "id" metadata column) is exported as field "ID".
readLines(tmpf)
-#> [1] "COM=Experimentexported by MSnbase on Tue Apr 30 16:21:45 2024"
+#> [1] "COM=Experimentexported by MSnbase on Tue Apr 30 17:21:44 2024"
#> [2] "BEGIN IONS"
#> [3] "SCANS=NA"
#> [4] "TITLE=msLevel 1; retentionTime ; scanNum NA"
@@ -541,7 +541,7 @@ Examples
writeMgfData(spl, tmpf)
readLines(tmpf)
-#> [1] "COM=Experimentexported by MSnbase on Tue Apr 30 16:21:45 2024"
+#> [1] "COM=Experimentexported by MSnbase on Tue Apr 30 17:21:44 2024"
#> [2] "BEGIN IONS"
#> [3] "SCANS=NA"
#> [4] "TITLE=msLevel 1; retentionTime ; scanNum NA"
diff --git a/reference/OnDiskMSnExp-class.html b/reference/OnDiskMSnExp-class.html
index da351a15..e44f9687 100644
--- a/reference/OnDiskMSnExp-class.html
+++ b/reference/OnDiskMSnExp-class.html
@@ -1117,8 +1117,8 @@ Examples
#> Number of spectra: 35
#> MSn retention times: 45:27 - 45:30 minutes
#> - - - Processing information - - -
-#> Data loaded [Tue Apr 30 16:21:52 2024]
-#> Filter: select parent/children scans for 21945 [Tue Apr 30 16:21:53 2024]
+#> Data loaded [Tue Apr 30 17:21:53 2024]
+#> Filter: select parent/children scans for 21945 [Tue Apr 30 17:21:54 2024]
#> MSnbase version: 2.29.5
#> - - - Meta data - - -
#> phenoData
@@ -1149,8 +1149,8 @@ Examples
#> Number of spectra: 3
#> MSn retention times: 45:27 - 45:27 minutes
#> - - - Processing information - - -
-#> Data loaded [Tue Apr 30 16:21:52 2024]
-#> Filter: select parent/children scans for 21946 [Tue Apr 30 16:21:53 2024]
+#> Data loaded [Tue Apr 30 17:21:53 2024]
+#> Filter: select parent/children scans for 21946 [Tue Apr 30 17:21:54 2024]
#> MSnbase version: 2.29.5
#> - - - Meta data - - -
#> phenoData
diff --git a/reference/Rplot004.png b/reference/Rplot004.png
index f34bbcb7..0cab001a 100644
Binary files a/reference/Rplot004.png and b/reference/Rplot004.png differ
diff --git a/reference/Rplot005.png b/reference/Rplot005.png
index e041be51..92e266ef 100644
Binary files a/reference/Rplot005.png and b/reference/Rplot005.png differ
diff --git a/reference/averageMSnSet-1.png b/reference/averageMSnSet-1.png
index 0821b4c0..84186a25 100644
Binary files a/reference/averageMSnSet-1.png and b/reference/averageMSnSet-1.png differ
diff --git a/reference/averageMSnSet.html b/reference/averageMSnSet.html
index a8bd75ae..f8b9cf1e 100644
--- a/reference/averageMSnSet.html
+++ b/reference/averageMSnSet.html
@@ -205,7 +205,7 @@ Examples
#> Loading required package: cluster
#> Loading required package: BiocParallel
#>
-#> This is pRoloc version 1.42.0
+#> This is pRoloc version 1.43.2
#> Visit https://lgatto.github.io/pRoloc/ to get started.
setStockcol(paste0(getStockcol(), "AA"))
plot2D(avg, cex = 7.7 * disp)
diff --git a/reference/bin-methods.html b/reference/bin-methods.html
index af6a5fc0..bf2d7516 100644
--- a/reference/bin-methods.html
+++ b/reference/bin-methods.html
@@ -133,7 +133,7 @@ Examples
#> - - - Processing information - - -
#> Data loaded: Wed May 11 18:54:39 2011
#> Updated from version 0.3.0 to 0.3.1 [Fri Jul 8 20:23:25 2016]
-#> Spectra binned: Tue Apr 30 16:22:02 2024
+#> Spectra binned: Tue Apr 30 17:22:04 2024
#> MSnbase version: 1.1.22