Update to Bioconductor 3.22

Author: jorainer

.github/workflows/build_deploy.yaml

@@ -7,7 +7,7 @@ on:
 jobs:
   build-and-deploy:
     runs-on: ubuntu-latest
-    container: jorainer/xcms_tutorials:RELEASE_3_19
+    container: jorainer/xcms_tutorials:RELEASE_3_22
     steps:
       ## Most of these steps are the same as the ones in
       ## https://github.com/r-lib/actions/blob/master/examples/check-standard.yaml
@@ -52,4 +52,4 @@ jobs:
         ## Note that you need to run pkgdown::deploy_to_branch(new_process = FALSE)
         ## at least one locally before this will work. This creates the gh-pages
         ## branch (erasing anything you haven't version controlled!) and
-        ## makes the git history recognizable by pkgdown.
+        ## makes the git history recognizable by pkgdown.

.github/workflows/docker-push.yaml

@@ -32,7 +32,7 @@ jobs:
           push: true
           tag_with_ref: true
           tag_with_sha: true
-          tags: jorainer/xcms_tutorials:RELEASE_3_19
+          tags: jorainer/xcms_tutorials:RELEASE_3_22
       -
         name: Image digest
         run: echo ${{ steps.docker_build.outputs.digest }}
@@ -44,4 +44,4 @@ jobs:
           token: ${{ secrets.REPO_GHA_PAT }}
           repository: ${{ github.repository }}
           event-type: trigger-workflow-2
-          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}"}'
+          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}"}'

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.1.2
+Version: 1.2.0
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "[email protected]",
@@ -42,4 +42,4 @@ URL: https://jorainer.github.io/xcmsTutorials/
 BugReports: https://github.com/jorainer/xcmsTutorials/issues/new
 VignetteBuilder: knitr
 RoxygenNote: 7.2.3
-DockerImage: jorainer/xcms_tutorials:RELEASE_3_19
+DockerImage: jorainer/xcms_tutorials:RELEASE_3_19

Dockerfile

@@ -1,9 +1,9 @@
-FROM bioconductor/bioconductor_docker:RELEASE_3_19
+FROM bioconductor/bioconductor_docker:RELEASE_3_22
 
 LABEL name="jorainer/xcms_tutorials" \
       url="https://github.com/jorainer/xcmsTutorials" \
       maintainer="[email protected]" \
-      description="Docker container to run xcms tutorials. This version bases on the Bioconductor release 3.19 docker image." \
+      description="Docker container to run xcms tutorials. This version bases on the Bioconductor release 3.22 docker image." \
       license="Artistic-2.0"
 
 WORKDIR /home/rstudio
@@ -13,4 +13,4 @@ COPY --chown=rstudio:rstudio . /home/rstudio/
 ## Install the xcmsTutorials package and additional required packages
 RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE, type = 'source'); BiocManager::install(c('RCurl', 'xcms'), ask = FALSE, dependencies = TRUE, type = 'source')"
 
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, type = 'source', build_vignettes = TRUE, repos = BiocManager::repositories())"
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, type = 'source', build_vignettes = TRUE, repos = BiocManager::repositories())"

NEWS.md

@@ -1,3 +1,9 @@
+# xcmsTutorials 1.2
+
+## Changes in 1.2.0
+
+- Update to Bioconductor release 3.22 and add *xcms* reference.
+
 # xcmsTutorials 1.1
 
 ## Changes in 1.1.2
@@ -66,4 +72,4 @@
   Philippine Louail.
 
 
-# xcmsTutorials 0.1
+# xcmsTutorials 0.1

vignettes/references.bib

@@ -42,6 +42,22 @@ @article{Katajamaa:2006jh
 month = mar
 }
 
+@article{louail_xcms_2025,
+	title = {xcms in {Peak} {Form}: {Now} {Anchoring} a {Complete} {Metabolomics} {Data} {Preprocessing} and {Analysis} {Software} {Ecosystem}},
+	issn = {0003-2700},
+	shorttitle = {xcms in {Peak} {Form}},
+	url = {https://doi.org/10.1021/acs.analchem.5c04338},
+	doi = {10.1021/acs.analchem.5c04338},
+	abstract = {High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing data set scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms maintains long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.},
+	urldate = {2025-12-10},
+	journal = {Analytical Chemistry},
+	author = {Louail, Philippine and Brunius, Carl and Garcia-Aloy, Mar and Kumler, William and Storz, Norman and Stanstrup, Jan and Treutler, Hendrik and Vangeenderhuysen, Pablo and Witting, Michael and Neumann, Steffen and Rainer, Johannes},
+	month = dec,
+	year = {2025},
+	note = {Publisher: American Chemical Society},
+	file = {Full Text PDF:/home/jo/Zotero/storage/H3PSMZ3N/Louail et al. - 2025 - xcms in Peak Form Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ec.pdf:application/pdf},
+}
+
 @article{Smith:2006ic,
 author = {Smith, Colin A and Want, Elizabeth J and O'Maille, Grace and Abagyan, Ruben and Siuzdak, Gary},
 title = {{XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.}},
@@ -134,4 +150,4 @@ @article{naake_msquality_2023
 	pmid = {37812234},
 	keywords = {mass spectrometry, metabolomics, proteomics, quality control, R},
 	pages = {btad618},
-}
+}

vignettes/xcms-preprocessing.Rmd

@@ -34,13 +34,14 @@ opts_chunk$set(message = FALSE, error = FALSE, warning = FALSE,
 # Abstract
 
 In this document we discuss liquid chromatography (LC) mass spectrometry (MS)
-data handling and exploration using the `r Biocpkg("MsExperiment")` and
-`r Biocpkg("Spectra")` Bioconductor packages and perform the preprocessing of a
-small LC-MS data set using the `r Biocpkg("xcms")` package. Functionality from
-the `r Biocpkg("MetaboCoreUtils")` and `r Biocpkg("MsCoreUtils")` packages are
-used for general tasks frequently performed during metabolomics data
-analysis. Ultimately, the functionality from these packages can be combined to
-build custom, data set-specific (and reproducible) analysis workflows.
+data handling and exploration using the `r Biocpkg("MsExperiment")` and `r
+Biocpkg("Spectra")` Bioconductor packages and perform the preprocessing of a
+small LC-MS data set using the `r Biocpkg("xcms")` package
+[@louail_xcms_2025]. Functionality from the `r Biocpkg("MetaboCoreUtils")` and
+`r Biocpkg("MsCoreUtils")` packages are used for general tasks frequently
+performed during metabolomics data analysis. Ultimately, the functionality from
+these packages can be combined to build custom, data set-specific (and
+reproducible) analysis workflows.
 
 In the present workshop, we first focus on data import, access and visualization
 which is followed by the description of a simple data centroiding approach and
@@ -1257,7 +1258,7 @@ In *xcms*, the alignment can be performed with the `adjustRtime()` function and
 one of the available alignment algorithms, that can be selected, and configured,
 with the respective parameter objects:
 
-- `PeakGroupsParam`: the *peakGroups* [@Smith:2006ic] method aligns samples
+- `PeakGroupsParam`: the *peakGroups* [@louail_xcms_2025] method aligns samples
   based on the retention times of a set of so called *anchor peaks* (or
   housekeeping peaks) in the different samples of an experiment. These peaks are
   supposed to represent signal from ions expected to be present in most of the
@@ -1451,7 +1452,7 @@ respective parameter objects:
 
 - `PeakDensityParam`: performs a simple and fast correspondence analysis based
   on the density of chromatographic peaks (from different samples) along the
-  retention time axis within slices of small *m/z* ranges [@Smith:2006ic].
+  retention time axis within slices of small *m/z* ranges [@louail_xcms_2025].
 
 Both methods group chromatographic peaks from different samples with similar
 *m/z* and retention times into features. For our example we use the *peak
@@ -2352,4 +2353,4 @@ Thank you to [Philippine Louail](https://github.com/philouail) for fixing typos
 and suggesting improvements.
 
 
-# References
+# References