R Package Reference for Mass Spectrometry

This appendix summarises the R and Bioconductor packages used throughout the book. For each package it lists the source (CRAN, Bioconductor, or GitHub), its role in an MS workflow, the key functions demonstrated in the text, and the chapters where it appears. Install Bioconductor packages with BiocManager::install() and CRAN packages with install.packages(); pin exact versions with renv (Chapter 3).

Code

install.packages("BiocManager")
BiocManager::install(c(
  "Spectra", "xcms", "QFeatures", "MsExperiment", "MsCoreUtils",
  "MetaboAnnotation", "PSMatch", "limma", "DEP"
))

Note

Package versions evolve. Run sessionInfo() (printed at the end of each chapter) to see what was loaded at render time. Install Bioconductor packages with BiocManager::install() and pin exact versions in your own project with renv::snapshot() (Chapter 3).

Data infrastructure and raw-spectra handling

Package	Source	Role	Key functions	Chapters
mzR	Bioc	Low-level parser for mzML/mzXML/mzIdentML via C++ backends	`openMSfile()`, `header()`, `peaks()`	4, 5
Spectra	Bioc	Modern, backend-agnostic container for raw MS spectra	`Spectra()`, `filterMsLevel()`, `filterRt()`, `plotSpectra()`, `combineSpectra()`	2, 4, 5, 6, 11
MsBackendMzR	Bioc	On-disk mzML/mzXML backend for `Spectra` (low memory)	`MsBackendMzR()`	4, 5
MsBackendSql / MsBackendHdf5Peaks	Bioc	SQL/HDF5 backends for very large datasets	`MsBackendSql()`, `MsBackendHdf5Peaks()`	4
MsCoreUtils	Bioc	Vectorised numerical helpers shared across the ecosystem	`closest()`, `bin()`, `normalizeMethods()`, `impute_matrix()`	2, 6, 17, 18
MsQuality	Bioc	Automated per-sample QC metrics based on the HUPO-PSI mzQC standard	`qualityMetrics()`, `calculateMetrics()`, `plotMetric()`	6
ProtGenerics	Bioc	Shared S4 generics (`mz()`, `intensity()`, `rtime()`)	generic definitions	5, 6
MsExperiment	Bioc	Links raw files, spectra, and sample metadata in one object	`MsExperiment()`, `sampleData()`, `spectra()`	5, 6, 16
MsIO / MsDataHub / MsBackendMetaboLights	Bioc	Import/serialisation and access to public example data	`readMsObject()`, dataset accessors	5, 6
scp	Bioc	Single-cell proteomics: extends QFeatures with SingleCellExperiment assays	`scp_qc()`, `normalizeScp()`, `joinAssays()`, `readSCP()`	E
scater	Bioc	Single-cell QC and visualisation (PCA, UMAP, t-SNE) for SCE objects	`runPCA()`, `runUMAP()`, `plotPCA()`, `plotColData()`	E

Quantitative containers

Package	Source	Role	Key functions	Chapters
SummarizedExperiment	Bioc	Feature × sample matrix with row/column metadata	`SummarizedExperiment()`, `assay()`, `colData()`, `rowData()`	5, 6, 7
QFeatures	Bioc	Linked PSM → peptide → protein hierarchy	`readQFeatures()`, `aggregateFeatures()`, `filterFeatures()`, `filterNA()`	6, 9, 12, 13
MultiAssayExperiment	Bioc	Coordinates multiple omics assays on shared samples	`MultiAssayExperiment()`, `intersectColumns()`	22
S4Vectors / IRanges	Bioc	Foundational S4 vector and range classes	`DataFrame()`, `IRanges()`	5, 6

Feature detection and metabolite annotation

Package	Source	Role	Key functions	Chapters
xcms	Bioc	Chromatographic peak detection, alignment, correspondence	`findChromPeaks()`, `adjustRtime()`, `groupChromPeaks()`, `fillChromPeaks()`	7, 16
CAMERA	Bioc	Adduct/isotope grouping and annotation of xcms peaks	`xsAnnotate()`, `groupFWHM()`, `findIsotopes()`, `findAdducts()`	10
MetaboCoreUtils	Bioc	Exact-mass calculators, adduct definitions, isotopes	`calculateMass()`, `mass2mz()`, `adducts()`, `isotopologues()`	10, 11
MetaboAnnotation	Bioc	Parameterised m/z, spectra, and library matching	`matchValues()`, `matchSpectra()`, `MatchForwardReverseParam()`	11
CompoundDb	Bioc	Build/query local compound and spectral libraries	`CompDb()`, `createCompDb()`, `Spectra()` accessor	11

Identification (proteomics)

Package	Source	Role	Key functions	Chapters
PSMatch	Bioc	PSM handling, target–decoy FDR, shared-peptide graphs	`PSM()`, `filterPsmDecoy()`, `filterPsmRank()`, `makePeptideProteinGraph()`	8, 9
MSnbase	Bioc	Established MS data structures (predecessor to Spectra)	`readMSData()`, `MSnSet`, `normalise()`	2, 5, 13
igraph	CRAN	Graph analysis for protein-group inference	`graph_from_data_frame()`, `components()`	9

Quantification, normalisation, and missing data

Package	Source	Role	Key functions	Chapters
DEP	Bioc	End-to-end LFQ differential expression pipeline	`make_se()`, `normalize_vsn()`, `impute()`, `test_diff()`	13, 14
MSstats	Bioc	Linear mixed models for label-free, DDA, DIA, and SRM quantification	`dataProcess()`, `groupComparison()`, `groupComparisonPlots()`	12, 13, 15
MSstatsTMT	Bioc	TMT-specific differential abundance with channel normalisation and purity correction	`proteinSummarization()`, `groupComparison()`	12, 14
MSstatsPTM	Bioc	PTM quantification adjusted for parent protein abundance changes	`PTMsummarization()`, `groupComparisonPTM()`	12
MSstatsLiP	Bioc	Limited proteolysis (LiP) structural proteomics	`LiPsummarization()`	12
MSstatsConvert	Bioc	Import/conversion layer for search-engine exports (DIA-NN, FragPipe, MaxQuant, Spectronaut, Skyline)	`MSstatsConvert()`, `MSstatsLog()`	12
MSstatsShiny	Bioc	GUI for interactive exploration of MSstats results	(GUI application)	12
MSstatsBig	Bioc	Large-scale DIA datasets with out-of-memory processing	`dataProcessBig()`	12
vsn	Bioc	Variance-stabilising normalisation	`justvsn()`, `normalizeVSN()`	14, 17
preprocessCore	Bioc	Quantile normalisation	`normalize.quantiles()`	17
limma	Bioc	`normalizeBetweenArrays()` (cyclic loess, quantile)	`normalizeBetweenArrays()`	17
sva	Bioc	Batch correction (ComBat) and surrogate variables	`ComBat()`, `sva()`	17
impute	Bioc	KNN imputation	`impute.knn()`	18
pcaMethods	Bioc	BPCA / probabilistic PCA imputation	`pca()` with `method = "bpca"`	18
naniar	CRAN	Missingness visualisation and diagnostics	`vis_miss()`, `gg_miss_var()`	18

Statistical modelling and machine learning

Package	Source	Role	Key functions	Chapters
limma	Bioc	Linear models + empirical Bayes for differential abundance	`lmFit()`, `eBayes()`, `topTable()`, `makeContrasts()`	19, 20
variancePartition	Bioc	Variance decomposition, mixed models (`dream`)	`fitExtractVarPartModel()`, `dream()`	20
lme4	CRAN	Linear mixed-effects models	`lmer()`	20
broom / broom.mixed	CRAN	Tidy model output	`tidy()`, `glance()`	20, 21
randomForest	CRAN	Random-forest classification	`randomForest()`	21
glmnet	CRAN	Regularised (LASSO/elastic-net) regression	`cv.glmnet()`	21
tidymodels / rsample	CRAN	Resampling, nested cross-validation, workflows	`nested_cv()`, `vfold_cv()`	21
pROC	CRAN	ROC curves with bootstrap confidence intervals	`roc()`, `ci.auc()`	21
survival / survminer	CRAN	Survival analysis and Kaplan–Meier plots	`coxph()`, `survfit()`, `ggsurvplot()`	21
msqrob2	Bioc	Robust ridge regression and hurdle/mixed-model workflows for LFQ	`MsqRob()`, `msqrob()`	20
proDA	Bioc	Probabilistic dropout analysis for label-free proteomics	`proDA()`, `test_diff()`, `median_normalization()`	20
DEqMS	Bioc	Variance adjustment by peptide/PSM count for protein-level testing	`spectraCounteBayes()`, `outputResult()`	20
limpa	Bioc	Detection-probability-based quantification + limma differential analysis	`dpcQuant()`, `dpcDE()`, `dpc()`	20
PolySTest	Bioc	Combined quantitative + missingness testing for low-replication designs	`PolySTest()`, `MissingStats()`	20

Interpretation, integration, and enrichment

Package	Source	Role	Key functions	Chapters
mixOmics	CRAN	Multi-block PLS-DA (DIABLO), sparse multivariate	`block.splsda()`, `plotDiablo()`, `circosPlot()`	22
clusterProfiler	Bioc	Over-representation and GSEA pathway enrichment	`enrichKEGG()`, `gseKEGG()`, `enrichGO()`	22
pathview	Bioc	Render KEGG pathway maps with data overlay	`pathview()`	22
org.Hs.eg.db (and organism `.db`)	Bioc	Gene/protein ID annotation mappings	`mapIds()`, `select()`	22
corrplot	CRAN	Correlation-matrix visualisation	`corrplot()`	22

Visualisation and reporting

Package	Source	Role	Key functions	Chapters
ggplot2	CRAN	Grammar-of-graphics plotting (used throughout)	`ggplot()`, `geom_*()`, `facet_wrap()`	all
ggrepel	CRAN	Non-overlapping text labels (volcano/PCA plots)	`geom_text_repel()`	19, 20
patchwork	CRAN	Compose multiple ggplots	`+`, `/`, `plot_layout()`	7, 17
pheatmap / ComplexHeatmap	CRAN/Bioc	Annotated heatmaps	`pheatmap()`, `Heatmap()`	13, 17, 19
ComplexUpset	CRAN	UpSet plots for set overlaps	`upset()`	8
factoextra	CRAN	PCA/clustering visualisation helpers	`fviz_pca_ind()`, `fviz_eig()`	13, 17
gt / knitr	CRAN	Publication tables	`gt()`, `kable()`	many

Reproducibility toolchain

Package	Source	Role	Key functions	Chapters
renv	CRAN	Project-local library + version lockfile	`init()`, `snapshot()`, `restore()`	3
targets	CRAN	Pipeline caching and dynamic branching	`tar_make()`, `tar_target()`, `tar_read()`	3, 23
tarchetypes	CRAN	Target factories, including Quarto reports	`tar_quarto()`	3, 23
here	CRAN	Project-root-relative file paths	`here()`	3
quarto	CRAN	Render Quarto documents from R	`quarto_render()`	3, 23
sessioninfo	CRAN	Rich session/environment capture	`session_info()`	many
testthat	CRAN	Unit testing of custom functions	`test_that()`, `expect_*()`	3
tidyverse (dplyr, tidyr, purrr, stringr, readr, tibble)	CRAN	Data wrangling used across chapters	`mutate()`, `filter()`, `pivot_longer()`, `map()`	many

Mass spectrometry imaging

Package	Source	Role	Key functions	Chapters
Cardinal	Bioc	Statistical analysis of MS imaging experiments: preprocessing, PCA, spatial segmentation	`readImzML()`, `image()`, `normalize()`, `reduceBaseline()`, `peakPick()`, `PCA()`, `spatialShrunkenCentroids()`	App F
CardinalIO	Bioc	Low-level I/O for imzML/ibd files (used by Cardinal)	`readImzML()`, `writeImzML()`	App F
CardinalWorkflows	Bioc	Documented example MSI datasets for reproducible demonstration	`exampleMSIData("pig206")`	App F

Example-data packages

Package	Source	Role	Chapters
msdata	Bioc	Bundled mzML/mzid/mzTab example files (no downloads)	4, 5, 6, 7
faahKO	Bioc	Classic xcms metabolomics example dataset	7, 16
MsDataHub	Bioc	Curated example MS datasets via `ExperimentHub`	5
scpdata	Bioc	Single-cell proteomics example datasets (SCoPE2, TMTpro, diaPASEF)	E

All runnable examples in this book use bundled example data (msdata, faahKO, MsDataHub) so they reproduce without external downloads.

Broader ecosystem catalog

This appendix lists only packages that are demonstrated in the book. The Bioconductor Mass Spectrometry ecosystem contains hundreds of additional packages. For exhaustive discovery, three CSV catalogs are maintained in the companion repository:

bioconductor_ms_packages.csv — all Bioconductor Mass Spectrometry packages (generated 2025 from Bioconductor ≥ 3.20)
MS_omics_tools_all_sources.csv — comprehensive MS omics tool index
MS_omics_tools_github.csv — GitHub-hosted MS analysis tools

These catalogs are reference documents, not part of the book narrative. A package’s presence in a catalog does not imply endorsement or that it has been tested against the book’s workflows.

Where to find more

R for Mass Spectrometry initiative: https://www.rformassspectrometry.org/ — coordinated Spectra, QFeatures, MsCoreUtils, MetaboAnnotation, PSMatch documentation and tutorials.
Bioconductor biocViews MassSpectrometry, Proteomics, Metabolomics: https://bioconductor.org/packages/release/BiocViews.html.
Each package’s vignette (browseVignettes("Spectra")) is the authoritative, version-matched reference.

# R Package Reference for Mass Spectrometry {#sec-appendix-packages .unnumbered} This appendix summarises the R and Bioconductor packages used throughout the book. For each package it lists the **source** (CRAN, Bioconductor, or GitHub), its **role** in an MS workflow, the **key functions** demonstrated in the text, and the **chapters** where it appears. Install Bioconductor packages with `BiocManager::install()` and CRAN packages with `install.packages()`; pin exact versions with `renv` (Chapter 3). ```{r} #| eval: false install.packages("BiocManager") BiocManager::install(c( "Spectra", "xcms", "QFeatures", "MsExperiment", "MsCoreUtils", "MetaboAnnotation", "PSMatch", "limma", "DEP" )) ``` ::: {.callout-note} Package versions evolve. Run `sessionInfo()` (printed at the end of each chapter) to see what was loaded at render time. Install Bioconductor packages with `BiocManager::install()` and pin exact versions in your own project with `renv::snapshot()` (Chapter 3). ::: ## Data infrastructure and raw-spectra handling | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **mzR** | Bioc | Low-level parser for mzML/mzXML/mzIdentML via C++ backends | `openMSfile()`, `header()`, `peaks()` | 4, 5 | | **Spectra** | Bioc | Modern, backend-agnostic container for raw MS spectra | `Spectra()`, `filterMsLevel()`, `filterRt()`, `plotSpectra()`, `combineSpectra()` | 2, 4, 5, 6, 11 | | **MsBackendMzR** | Bioc | On-disk mzML/mzXML backend for `Spectra` (low memory) | `MsBackendMzR()` | 4, 5 | | **MsBackendSql / MsBackendHdf5Peaks** | Bioc | SQL/HDF5 backends for very large datasets | `MsBackendSql()`, `MsBackendHdf5Peaks()` | 4 | | **MsCoreUtils** | Bioc | Vectorised numerical helpers shared across the ecosystem | `closest()`, `bin()`, `normalizeMethods()`, `impute_matrix()` | 2, 6, 17, 18 | | **MsQuality** | Bioc | Automated per-sample QC metrics based on the HUPO-PSI mzQC standard | `qualityMetrics()`, `calculateMetrics()`, `plotMetric()` | 6 | | **ProtGenerics** | Bioc | Shared S4 generics (`mz()`, `intensity()`, `rtime()`) | generic definitions | 5, 6 | | **MsExperiment** | Bioc | Links raw files, spectra, and sample metadata in one object | `MsExperiment()`, `sampleData()`, `spectra()` | 5, 6, 16 | | **MsIO / MsDataHub / MsBackendMetaboLights** | Bioc | Import/serialisation and access to public example data | `readMsObject()`, dataset accessors | 5, 6 | | **scp** | Bioc | Single-cell proteomics: extends QFeatures with SingleCellExperiment assays | `scp_qc()`, `normalizeScp()`, `joinAssays()`, `readSCP()` | E | | **scater** | Bioc | Single-cell QC and visualisation (PCA, UMAP, t-SNE) for SCE objects | `runPCA()`, `runUMAP()`, `plotPCA()`, `plotColData()` | E | ## Quantitative containers | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **SummarizedExperiment** | Bioc | Feature × sample matrix with row/column metadata | `SummarizedExperiment()`, `assay()`, `colData()`, `rowData()` | 5, 6, 7 | | **QFeatures** | Bioc | Linked PSM → peptide → protein hierarchy | `readQFeatures()`, `aggregateFeatures()`, `filterFeatures()`, `filterNA()` | 6, 9, 12, 13 | | **MultiAssayExperiment** | Bioc | Coordinates multiple omics assays on shared samples | `MultiAssayExperiment()`, `intersectColumns()` | 22 | | **S4Vectors / IRanges** | Bioc | Foundational S4 vector and range classes | `DataFrame()`, `IRanges()` | 5, 6 | ## Feature detection and metabolite annotation | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **xcms** | Bioc | Chromatographic peak detection, alignment, correspondence | `findChromPeaks()`, `adjustRtime()`, `groupChromPeaks()`, `fillChromPeaks()` | 7, 16 | | **CAMERA** | Bioc | Adduct/isotope grouping and annotation of xcms peaks | `xsAnnotate()`, `groupFWHM()`, `findIsotopes()`, `findAdducts()` | 10 | | **MetaboCoreUtils** | Bioc | Exact-mass calculators, adduct definitions, isotopes | `calculateMass()`, `mass2mz()`, `adducts()`, `isotopologues()` | 10, 11 | | **MetaboAnnotation** | Bioc | Parameterised m/z, spectra, and library matching | `matchValues()`, `matchSpectra()`, `MatchForwardReverseParam()` | 11 | | **CompoundDb** | Bioc | Build/query local compound and spectral libraries | `CompDb()`, `createCompDb()`, `Spectra()` accessor | 11 | ## Identification (proteomics) | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **PSMatch** | Bioc | PSM handling, target–decoy FDR, shared-peptide graphs | `PSM()`, `filterPsmDecoy()`, `filterPsmRank()`, `makePeptideProteinGraph()` | 8, 9 | | **MSnbase** | Bioc | Established MS data structures (predecessor to Spectra) | `readMSData()`, `MSnSet`, `normalise()` | 2, 5, 13 | | **igraph** | CRAN | Graph analysis for protein-group inference | `graph_from_data_frame()`, `components()` | 9 | ## Quantification, normalisation, and missing data | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **DEP** | Bioc | End-to-end LFQ differential expression pipeline | `make_se()`, `normalize_vsn()`, `impute()`, `test_diff()` | 13, 14 | | **MSstats** | Bioc | Linear mixed models for label-free, DDA, DIA, and SRM quantification | `dataProcess()`, `groupComparison()`, `groupComparisonPlots()` | 12, 13, 15 | | **MSstatsTMT** | Bioc | TMT-specific differential abundance with channel normalisation and purity correction | `proteinSummarization()`, `groupComparison()` | 12, 14 | | **MSstatsPTM** | Bioc | PTM quantification adjusted for parent protein abundance changes | `PTMsummarization()`, `groupComparisonPTM()` | 12 | | **MSstatsLiP** | Bioc | Limited proteolysis (LiP) structural proteomics | `LiPsummarization()` | 12 | | **MSstatsConvert** | Bioc | Import/conversion layer for search-engine exports (DIA-NN, FragPipe, MaxQuant, Spectronaut, Skyline) | `MSstatsConvert()`, `MSstatsLog()` | 12 | | **MSstatsShiny** | Bioc | GUI for interactive exploration of MSstats results | (GUI application) | 12 | | **MSstatsBig** | Bioc | Large-scale DIA datasets with out-of-memory processing | `dataProcessBig()` | 12 | | **vsn** | Bioc | Variance-stabilising normalisation | `justvsn()`, `normalizeVSN()` | 14, 17 | | **preprocessCore** | Bioc | Quantile normalisation | `normalize.quantiles()` | 17 | | **limma** | Bioc | `normalizeBetweenArrays()` (cyclic loess, quantile) | `normalizeBetweenArrays()` | 17 | | **sva** | Bioc | Batch correction (ComBat) and surrogate variables | `ComBat()`, `sva()` | 17 | | **impute** | Bioc | KNN imputation | `impute.knn()` | 18 | | **pcaMethods** | Bioc | BPCA / probabilistic PCA imputation | `pca()` with `method = "bpca"` | 18 | | **naniar** | CRAN | Missingness visualisation and diagnostics | `vis_miss()`, `gg_miss_var()` | 18 | ## Statistical modelling and machine learning | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **limma** | Bioc | Linear models + empirical Bayes for differential abundance | `lmFit()`, `eBayes()`, `topTable()`, `makeContrasts()` | 19, 20 | | **variancePartition** | Bioc | Variance decomposition, mixed models (`dream`) | `fitExtractVarPartModel()`, `dream()` | 20 | | **lme4** | CRAN | Linear mixed-effects models | `lmer()` | 20 | | **broom / broom.mixed** | CRAN | Tidy model output | `tidy()`, `glance()` | 20, 21 | | **randomForest** | CRAN | Random-forest classification | `randomForest()` | 21 | | **glmnet** | CRAN | Regularised (LASSO/elastic-net) regression | `cv.glmnet()` | 21 | | **tidymodels / rsample** | CRAN | Resampling, nested cross-validation, workflows | `nested_cv()`, `vfold_cv()` | 21 | | **pROC** | CRAN | ROC curves with bootstrap confidence intervals | `roc()`, `ci.auc()` | 21 | | **survival / survminer** | CRAN | Survival analysis and Kaplan–Meier plots | `coxph()`, `survfit()`, `ggsurvplot()` | 21 | | **msqrob2** | Bioc | Robust ridge regression and hurdle/mixed-model workflows for LFQ | `MsqRob()`, `msqrob()` | 20 | | **proDA** | Bioc | Probabilistic dropout analysis for label-free proteomics | `proDA()`, `test_diff()`, `median_normalization()` | 20 | | **DEqMS** | Bioc | Variance adjustment by peptide/PSM count for protein-level testing | `spectraCounteBayes()`, `outputResult()` | 20 | | **limpa** | Bioc | Detection-probability-based quantification + limma differential analysis | `dpcQuant()`, `dpcDE()`, `dpc()` | 20 | | **PolySTest** | Bioc | Combined quantitative + missingness testing for low-replication designs | `PolySTest()`, `MissingStats()` | 20 | ## Interpretation, integration, and enrichment | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **mixOmics** | CRAN | Multi-block PLS-DA (DIABLO), sparse multivariate | `block.splsda()`, `plotDiablo()`, `circosPlot()` | 22 | | **clusterProfiler** | Bioc | Over-representation and GSEA pathway enrichment | `enrichKEGG()`, `gseKEGG()`, `enrichGO()` | 22 | | **pathview** | Bioc | Render KEGG pathway maps with data overlay | `pathview()` | 22 | | **org.Hs.eg.db** (and organism `.db`) | Bioc | Gene/protein ID annotation mappings | `mapIds()`, `select()` | 22 | | **corrplot** | CRAN | Correlation-matrix visualisation | `corrplot()` | 22 | ## Visualisation and reporting | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **ggplot2** | CRAN | Grammar-of-graphics plotting (used throughout) | `ggplot()`, `geom_*()`, `facet_wrap()` | all | | **ggrepel** | CRAN | Non-overlapping text labels (volcano/PCA plots) | `geom_text_repel()` | 19, 20 | | **patchwork** | CRAN | Compose multiple ggplots | `+`, `/`, `plot_layout()` | 7, 17 | | **pheatmap / ComplexHeatmap** | CRAN/Bioc | Annotated heatmaps | `pheatmap()`, `Heatmap()` | 13, 17, 19 | | **ComplexUpset** | CRAN | UpSet plots for set overlaps | `upset()` | 8 | | **factoextra** | CRAN | PCA/clustering visualisation helpers | `fviz_pca_ind()`, `fviz_eig()` | 13, 17 | | **gt / knitr** | CRAN | Publication tables | `gt()`, `kable()` | many | ## Reproducibility toolchain | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---| | **renv** | CRAN | Project-local library + version lockfile | `init()`, `snapshot()`, `restore()` | 3 | | **targets** | CRAN | Pipeline caching and dynamic branching | `tar_make()`, `tar_target()`, `tar_read()` | 3, 23 | | **tarchetypes** | CRAN | Target factories, including Quarto reports | `tar_quarto()` | 3, 23 | | **here** | CRAN | Project-root-relative file paths | `here()` | 3 | | **quarto** | CRAN | Render Quarto documents from R | `quarto_render()` | 3, 23 | | **sessioninfo** | CRAN | Rich session/environment capture | `session_info()` | many | | **testthat** | CRAN | Unit testing of custom functions | `test_that()`, `expect_*()` | 3 | | **tidyverse** (dplyr, tidyr, purrr, stringr, readr, tibble) | CRAN | Data wrangling used across chapters | `mutate()`, `filter()`, `pivot_longer()`, `map()` | many | ## Mass spectrometry imaging | Package | Source | Role | Key functions | Chapters | |---|---|---|---|---|---| | **Cardinal** | Bioc | Statistical analysis of MS imaging experiments: preprocessing, PCA, spatial segmentation | `readImzML()`, `image()`, `normalize()`, `reduceBaseline()`, `peakPick()`, `PCA()`, `spatialShrunkenCentroids()` | App F | | **CardinalIO** | Bioc | Low-level I/O for imzML/ibd files (used by Cardinal) | `readImzML()`, `writeImzML()` | App F | | **CardinalWorkflows** | Bioc | Documented example MSI datasets for reproducible demonstration | `exampleMSIData("pig206")` | App F | ## Example-data packages | Package | Source | Role | Chapters | |---|---|---|---| | **msdata** | Bioc | Bundled mzML/mzid/mzTab example files (no downloads) | 4, 5, 6, 7 | | **faahKO** | Bioc | Classic xcms metabolomics example dataset | 7, 16 | | **MsDataHub** | Bioc | Curated example MS datasets via `ExperimentHub` | 5 | | **scpdata** | Bioc | Single-cell proteomics example datasets (SCoPE2, TMTpro, diaPASEF) | E | All runnable examples in this book use bundled example data (`msdata`, `faahKO`, `MsDataHub`) so they reproduce without external downloads. ## Broader ecosystem catalog This appendix lists only packages that are demonstrated in the book. The Bioconductor Mass Spectrometry ecosystem contains hundreds of additional packages. For exhaustive discovery, three CSV catalogs are maintained in the companion repository: - `bioconductor_ms_packages.csv` — all Bioconductor Mass Spectrometry packages (generated 2025 from Bioconductor ≥ 3.20) - `MS_omics_tools_all_sources.csv` — comprehensive MS omics tool index - `MS_omics_tools_github.csv` — GitHub-hosted MS analysis tools These catalogs are reference documents, not part of the book narrative. A package's presence in a catalog does not imply endorsement or that it has been tested against the book's workflows. ## Where to find more - **R for Mass Spectrometry** initiative: <https://www.rformassspectrometry.org/> — coordinated `Spectra`, `QFeatures`, `MsCoreUtils`, `MetaboAnnotation`, `PSMatch` documentation and tutorials. - **Bioconductor** biocViews `MassSpectrometry`, `Proteomics`, `Metabolomics`: <https://bioconductor.org/packages/release/BiocViews.html>. - Each package's vignette (`browseVignettes("Spectra")`) is the authoritative, version-matched reference.