1 Introduction

Quality control (QC) is an essential step in any analytical process. Data of poor quality can at best lead to the absence of positive results or, much worse, false positives that stem from uncaught faulty and noisy data and much wasted resources in pursuing red herrings.

Quality is often a relative concept that depends on the nature of the biological sample, the experimental settings, the analytical process and other factors. Research and development in the area of QC has generally lead to two types of work being disseminated. Firstly, the comparison of samples of variable quality and the identification of metrics that correlate with the quality of the data. These quality metrics could then, in later experiments, be used to assess their quality. Secondly, the design of domain-specific software to facilitate the collection, visualisation and interpretation of various QC metrics is also an area that has seen much development. QC is a prime example where standardisation and automation are of great benefit. While a great variety of QC metrics, software and pipelines have been described for any assay commonly used in modern biology, we present here a different tool for QC, whose main features are flexibility and versatility. The qcmetrics package is a general framework for QC that can accommodate any type of data. It provides a flexible framework to implement QC items that store relevant QC metrics with a specific visualisation mechanism. These individual items can be bundled into higher level QC containers that can be readily used to generate reports in various formats. As a result, it becomes easy to develop complete custom pipelines from scratch and automate the generation of reports. The pipelines can be easily updated to accommodate new QC items of better visualisation techniques.

Section 2 provides an overview of the framework. In section 3, we use proteomics data (subsection 3.2) to demonstrate the elaboration of QC pipelines: how to create individual QC objects, how to bundle them to create sets of QC metrics and how to generate reports in multiple formats. We also show how the above steps can be fully automated through simple wrapper functions. Although kept simple in the interest of time and space, these examples are meaningful and relevant. In section 4, we provide more detail about the report generation process, how reports can be customised and how new exports can be contributed. We proceed in section 4.3 to the consolidation of QC pipelines using R and elaborate on the development of dedicated QC packages with qcmetrics.

2 The QC classes

The package provides two types of QC containers. The QcMetric class stores data and visualisation functions for single metrics. Several such metrics can be bundled into QcMetrics instances, that can be used as input for automated report generation. Below, we will provide a quick overview of how to create respective QcMetric and QcMetrics instances. More details are available in the corresponding documentations.

2.1 The QcMetric class

A QC metric is composed of a description (name in the code chunk below), some QC data (qcdata) and a status that defines if the metric is deemed of acceptable quality (coded as TRUE), bad quality (coded as FALSE) or not yet evaluated (coded as NA). Individual metrics can be displayed as a short textual summary or plotted. To do the former, one can use the default show method.

library("qcmetrics")
qc <- QcMetric(name = "A test metric")
qcdata(qc, "x") <- rnorm(100)
qcdata(qc) ## all available qcdata
## [1] "x"
summary(qcdata(qc, "x")) ## get x
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -2.2147 -0.4942  0.1139  0.1089  0.6915  2.4016
show(qc) ## or just qc
## Object of class "QcMetric"
##  Name: A test metric 
##  Status: NA 
##  Data: x
status(qc) <- TRUE
qc
## Object of class "QcMetric"
##  Name: A test metric 
##  Status: TRUE 
##  Data: x

Plotting QcMetric instances requires to implement a plotting method that is relevant to the data at hand. We can use a plot replacement method to define our custom function. The code inside the plot uses qcdata to extract the relevant QC data from object that is then passed as argument to plot and uses the adequate visualisation to present the QC data.

plot(qc)
## Warning in x@plot(x, ...): No specific plot function defined
plot(qc) <- function(object, ... ) boxplot(qcdata(object, "x"), ...)
plot(qc)

2.2 The QcMetrics class

A QcMetrics object is essentially just a list of individual instances. It is also possible to set a list of metadata variables to describe the source of the QC metrics. The metadata can be passed as an QcMetadata object (the way it is stored in the QcMetrics instance) or directly as a named list. The QcMetadata is itself a list and can be accessed and set with metadata or mdata. When accessed, it is returned and displayed as a list.

qcm <- QcMetrics(qcdata = list(qc))
qcm
## Object of class "QcMetrics"
##  containing 1 QC metrics.
##  and no metadata variables.
metadata(qcm) <- list(author = "Prof. Who",
                      lab = "Big lab")
qcm
## Object of class "QcMetrics"
##  containing 1 QC metrics.
##  and 2 metadata variables.
mdata(qcm)
## $author
## [1] "Prof. Who"
## 
## $lab
## [1] "Big lab"

The metadata can be updated with the same interface. If new named items are passed, the metadata is updated by addition of the new elements. If a named item is already present, its value gets updated.

metadata(qcm) <- list(author = "Prof. Who",
                      lab = "Cabin lab",
                      University = "Universe-ity")
mdata(qcm)
## $author
## [1] "Prof. Who"
## 
## $lab
## [1] "Cabin lab"
## 
## $University
## [1] "Universe-ity"

The QcMetrics can then be passed to the qcReport method to generate reports, as described in more details below.

3 Creating QC pipelines

3.1 Microarray degradation

The Microarray degradation section has been removed since the packages it was depending on have been deprecated.

3.2 Proteomics raw data

To illustrate a simple QC analysis for proteomics data, we will download data set PXD00001 from the ProteomeXchange repository in the mzXML format (Pedrioli and others 2004). The MS2 spectra from that mass-spectrometry run are then read into Rand stored as an MSnExp experiment using the readMSData function from the MSnbase package (Gatto and Lilley 2012).

library("RforProteomics")
msfile <- getPXD000001mzXML()
library("MSnbase")
exp <- readMSData(msfile, verbose = FALSE)

In the interest of time, this code chunk has been pre-computed and a subset (1 in 3) of the exp instance is distributed with the package. The data is loaded with

load(system.file("extdata/exp.rda", package = "qcmetrics"))

The QcMetrics will consist of 3 items, namely a chromatogram constructed with the MS2 spectra precursor’s intensities, a figure illustrating the precursor charges in the MS space and an m/z delta plot illustrating the suitability of MS2 spectra for identification (see ?plotMzDelta or (Foster et al. 2011)).

qc1 <- QcMetric(name = "Chromatogram")
x <- rtime(exp)
y <- precursorIntensity(exp)
o <- order(x)
qcdata(qc1, "x") <- x[o]
qcdata(qc1, "y") <- y[o]
plot(qc1) <- function(object, ...)
    plot(qcdata(object, "x"),
         qcdata(object, "y"),
         col = "darkgrey", type ="l",
         xlab = "retention time",
         ylab = "precursor intensity")
qc2 <- QcMetric(name = "MS space")
qcdata(qc2, "p2d") <- plot2d(exp, z = "charge", plot = FALSE)
plot(qc2) <- function(object) {
    require("ggplot2")
    print(qcdata(object, "p2d"))
}
qc3 <- QcMetric(name = "m/z delta plot")
qcdata(qc3, "pmz") <- plotMzDelta(exp, plot = FALSE,
                                  verbose = FALSE)
plot(qc3) <- function(object)
    suppressWarnings(print(qcdata(object, "pmz")))

Note that we do not store the raw data in any of the above instances, but always pre-compute the necessary data or plots that are then stored as qcdata. If the raw data was to be needed in multiple QcMetric instances, we could re-use the same qcdata environment to avoid unnecessary copies using qcdata(qc2) <- qcenv(qc1) and implement different views through custom plot methods.

Let’s now combine the three items into a QcMetrics object, decorate it with custom metadata using the MIAPE information from the MSnExp object and generate a report.

protqcm <- QcMetrics(qcdata = list(qc1, qc2, qc3))
metadata(protqcm) <- list(
    data = "PXD000001",
    instrument = experimentData(exp)@instrumentModel,
    source = experimentData(exp)@ionSource,
    analyser = experimentData(exp)@analyser,
    detector = experimentData(exp)@detectorType,
    manufacurer = experimentData(exp)@instrumentManufacturer)

The status column of the summary table is empty as we have not set the QC items statuses yet.

qcReport(protqcm, reportname = "protqc")
Proteomics QC report

Figure 1: Proteomics QC report

The complete pdf report is available with:

browseURL(example_reports("protqc"))

3.3 Processed N15 labelling data

In this section, we describe a set of N15 metabolic labelling QC metrics (Krijgsveld et al. 2003). The data is a phospho-enriched N15 labelled Arabidopsis thaliana sample prepared as described in (Groen et al. 2013). The data was processed with in-house tools and is available as an MSnSet instance. Briefly, MS2 spectra were search with the Mascot engine and identification scores adjusted with Mascot Percolator. Heavy and light pairs were then searched in the survey scans and N15 incorporation was estimated based on the peptide sequence and the isotopic envelope of the heavy member of the pair (the inc feature variable). Heavy and light peptides isotopic envelope areas were finally integrated to obtain unlabelled and N15 quantitation data. The psm object provides such data for PSMs (peptide spectrum matches) with a posterior error probability < 0.05 that can be uniquely matched to proteins.

We first load the MSnbase package (required to support the MSnSet data structure) and example data that is distributed with the qcmetrics package. We will make use of the ggplot2 plotting package.

library("ggplot2")
library("MSnbase")
data(n15psm)
psm
## MSnSet (storageMode: lockedEnvironment)
## assayData: 1772 features, 2 samples 
##   element names: exprs 
## protocolData: none
## phenoData: none
## featureData
##   featureNames: 3 5 ... 4499 (1772 total)
##   fvarLabels: Protein_Accession Protein_Description ... inc (21 total)
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
##   pubMedIds: 23681576 
## Annotation:  
## - - - Processing information - - -
## Subset [22540,2][1999,2] Tue Sep 17 01:34:09 2013 
## Removed features with more than 0 NAs: Tue Sep 17 01:34:09 2013 
## Dropped featureData's levels Tue Sep 17 01:34:09 2013 
##  MSnbase version: 1.9.7

The first QC item examines the N15 incorporation rate, available in the inc feature variable. We also defined a median incorporation rate threshold tr equal to 97.5 that is used to set the QC status.

## incorporation rate QC metric
qcinc <- QcMetric(name = "15N incorporation rate")
qcdata(qcinc, "inc") <- fData(psm)$inc
qcdata(qcinc, "tr") <- 97.5
status(qcinc) <- median(qcdata(qcinc, "inc")) > qcdata(qcinc, "tr")

Next, we implement a custom show method, that prints 5 summary values of the variable’s distribution.

show(qcinc) <- function(object) {
    qcshow(object, qcdata = FALSE)
    cat(" QC threshold:", qcdata(object, "tr"), "\n")
    cat(" Incorporation rate\n")
    print(summary(qcdata(object, "inc")))
    invisible(NULL)
}

We then define the metric’s plot function that represent the distribution of the PSM’s incorporation rates as a boxplot, shows all the individual rates as jittered dots and represents the tr threshold as a dotted red line.

plot(qcinc) <- function(object) {
    inc <- qcdata(object, "inc")
    tr <- qcdata(object, "tr")
    lab <- "Incorporation rate"
    dd <- data.frame(inc = qcdata(qcinc, "inc"))
    p <- ggplot(dd, aes(factor(""), inc)) +
        geom_jitter(colour = "#4582B370", size = 3) +
    geom_boxplot(fill = "#FFFFFFD0", colour = "#000000",
                 outlier.size = 0) +
    geom_hline(yintercept = tr, colour = "red",
               linetype = "dotted", size = 1) +
    labs(x = "", y = "Incorporation rate")
    p
}

N15 experiments of good quality are characterised by high incorporation rates, which allow to deconvolute the heavy and light peptide isotopic envelopes and accurate quantification.

The second metric inspects the log2 fold-changes of the PSMs, unique peptides with modifications, unique peptide sequences (not taking modifications into account) and proteins. These respective data sets are computed with the combineFeatures function (see ?combineFeatures for details).

fData(psm)$modseq <- ## pep seq + PTM
    paste(fData(psm)$Peptide_Sequence,
          fData(psm)$Variable_Modifications, sep = "+")
pep <- combineFeatures(psm,
                       as.character(fData(psm)$Peptide_Sequence),
                       "median", verbose = FALSE)
modpep <- combineFeatures(psm,
                          fData(psm)$modseq,
                          "median", verbose = FALSE)
prot <- combineFeatures(psm,
                        as.character(fData(psm)$Protein_Accession),
                        "median", verbose = FALSE)

The log2 fold-changes for all the features are then computed and stored as QC data of our next QC item. We also store a pair of values explfc that defined an interval in which we expect our median PSM log2 fold-change to be.

## calculate log fold-change
qclfc <- QcMetric(name = "Log2 fold-changes")
qcdata(qclfc, "lfc.psm") <-
    log2(exprs(psm)[,"unlabelled"] / exprs(psm)[, "N15"])
qcdata(qclfc, "lfc.pep") <-
    log2(exprs(pep)[,"unlabelled"] / exprs(pep)[, "N15"])
qcdata(qclfc, "lfc.modpep") <-
    log2(exprs(modpep)[,"unlabelled"] / exprs(modpep)[, "N15"])
qcdata(qclfc, "lfc.prot") <-
    log2(exprs(prot)[,"unlabelled"] / exprs(prot)[, "N15"])
qcdata(qclfc, "explfc") <- c(-0.5, 0.5)

status(qclfc) <-
    median(qcdata(qclfc, "lfc.psm")) > qcdata(qclfc, "explfc")[1] &
    median(qcdata(qclfc, "lfc.psm")) < qcdata(qclfc, "explfc")[2]

As previously, we provide a custom show method that displays summary values for the four fold-changes. The plot function illustrates the respective log2 fold-change densities and the expected median PSM fold-change range (red rectangle). The expected 0 log2 fold-change is shown as a dotted black vertical line and the observed median PSM value is shown as a blue dashed line.

show(qclfc) <- function(object) {
    qcshow(object, qcdata = FALSE) ## default
    cat(" QC thresholds:", qcdata(object, "explfc"), "\n")
    cat(" * PSM log2 fold-changes\n")
    print(summary(qcdata(object, "lfc.psm")))
    cat(" * Modified peptide log2 fold-changes\n")
    print(summary(qcdata(object, "lfc.modpep")))
    cat(" * Peptide log2 fold-changes\n")
    print(summary(qcdata(object, "lfc.pep")))
    cat(" * Protein log2 fold-changes\n")
    print(summary(qcdata(object, "lfc.prot")))
    invisible(NULL)
}
plot(qclfc) <- function(object) {
    x <- qcdata(object, "explfc")
    plot(density(qcdata(object, "lfc.psm")),
         main = "", sub = "", col = "red",
         ylab = "", lwd = 2,
         xlab = expression(log[2]~fold-change))
    lines(density(qcdata(object, "lfc.modpep")),
          col = "steelblue", lwd = 2)
    lines(density(qcdata(object, "lfc.pep")),
          col = "blue", lwd = 2)
    lines(density(qcdata(object, "lfc.prot")),
          col = "orange")
    abline(h = 0, col = "grey")
    abline(v = 0, lty = "dotted")
    rect(x[1], -1, x[2], 1, col = "#EE000030",
         border = NA)
    abline(v = median(qcdata(object, "lfc.psm")),
           lty = "dashed", col = "blue")
    legend("topright",
           c("PSM", "Peptides", "Modified peptides", "Proteins"),
           col = c("red", "steelblue", "blue", "orange"), lwd = 2,
           bty = "n")
}

A good quality experiment is expected to have a tight distribution centred around 0. Major deviations would indicate incomplete incorporation, errors in the respective amounts of light and heavy material used, and a wide distribution would reflect large variability in the data.

Our last QC item inspects the number of features that have been identified in the experiment. We also investigate how many peptides (with or without considering the modification) have been observed at the PSM level and the number of unique peptides per protein. Here, we do not specify any expected values as the number of observed features is experiment specific; the QC status is left as NA.

## number of features
qcnb <- QcMetric(name = "Number of features")
qcdata(qcnb, "count") <- c(
    PSM = nrow(psm),
    ModPep = nrow(modpep),
    Pep = nrow(pep),
    Prot = nrow(prot))
qcdata(qcnb, "peptab") <-
    table(fData(psm)$Peptide_Sequence)
qcdata(qcnb, "modpeptab") <-
    table(fData(psm)$modseq)
qcdata(qcnb, "upep.per.prot") <-
    fData(psm)$Number_Of_Unique_Peptides

The counts are displayed by the new show and plotted as bar charts by the plot methods.

show(qcnb) <- function(object) {
    qcshow(object, qcdata = FALSE)
    print(qcdata(object, "count"))
}
plot(qcnb) <- function(object) {
    par(mar = c(5, 4, 2, 1))
    layout(matrix(c(1, 2, 1, 3, 1, 4), ncol = 3))
    barplot(qcdata(object, "count"), horiz = TRUE, las = 2)
    barplot(table(qcdata(object, "modpeptab")),
            xlab = "Modified peptides")
    barplot(table(qcdata(object, "peptab")),
            xlab = "Peptides")
    barplot(table(qcdata(object, "upep.per.prot")),
            xlab = "Unique peptides per protein ")
}

In the code chunk below, we combine the 3 QC items into a QcMetrics instance and generate a report using meta data extracted from the psm MSnSet instance.

n15qcm <- QcMetrics(qcdata = list(qcinc, qclfc, qcnb))
qcReport(n15qcm, reportname = "n15qcreport",
         title = expinfo(experimentData(psm))["title"],
         author = expinfo(experimentData(psm))["contact"],
         clean = FALSE)

Once an appropriate set of quality metrics has been identified, the generation of the QcMetrics instances can be wrapped up for automation.

We provide such a wrapper function for this examples: the n15qc function fully automates the above pipeline. The names of the feature variable columns and the thresholds for the two first QC items are provided as arguments. In case no report name is given, a custom title with date and time is used, to avoid overwriting existing reports.

N15 QC report

Figure 2: N15 QC report

The complete pdf report is available with

browseURL(example_reports("n15qc"))

4 Report generation

The report generation is handled by dedicated packages, in particular knitr (Xie 2013) and markdown (Allaire et al. 2013).

4.1 Custom reports

4.1.1 QcMetric sections

The generation of the sections for QcMetric instances is controlled by a function passed to the qcto argument. This function takes care of transforming an instance of class QcMetric into a character that can be inserted into the report. For the tex and pdf reports, Qc2Tex is used; the Rmd and html reports make use of Qc2Rmd. These functions take an instance of class QcMetrics and the index of the QcMetric to be converted.

qcmetrics:::Qc2Tex
## function (object, i) 
## {
##     c(paste0("\\section{", name(object[[i]]), "}"), paste0("<<", 
##         name(object[[i]]), ", echo=FALSE>>="), paste0("show(object[[", 
##         i, "]])"), "@\n", "\\begin{figure}[!hbt]", "<<dev='pdf', echo=FALSE, fig.width=5, fig.height=5, fig.align='center'>>=", 
##         paste0("plot(object[[", i, "]])"), "@", "\\end{figure}", 
##         "\\clearpage")
## }
## <bytecode: 0x561d60097058>
## <environment: namespace:qcmetrics>
qcmetrics:::Qc2Tex(n15qcm, 1)
##  [1] "\\section{15N incorporation rate}"                                        
##  [2] "<<15N incorporation rate, echo=FALSE>>="                                  
##  [3] "show(object[[1]])"                                                        
##  [4] "@\n"                                                                      
##  [5] "\\begin{figure}[!hbt]"                                                    
##  [6] "<<dev='pdf', echo=FALSE, fig.width=5, fig.height=5, fig.align='center'>>="
##  [7] "plot(object[[1]])"                                                        
##  [8] "@"                                                                        
##  [9] "\\end{figure}"                                                            
## [10] "\\clearpage"

Let’s investigate how to customise these sections depending on the QcMetric status, the goal being to highlight positive QC results (i.e. when the status is TRUE) with green circles (or smileys), negative results with red cirlces (or frownies) and use en empty black circle if status is NA after the section title (the respective symbols are from the LaTeX package wasysym).

Qc2Tex2

To use this specific sectioning code, we pass our new function as qcto when generating the report. To generate smiley labels, use Qc2Tex3.

qcReport(n15qcm, reportname = "report", qcto = Qc2Tex2)
qcReport(n15qcm, reportname = "report", qcto = Qc2Tex3) ## for smiley/frowney
Customised QC report

Figure 3: Customised QC report

The complete pdf report is available with:

browseURL(example_reports("custom"))

4.2 New report types

A reporting function is a function that

  • Converts the appropriate QC item sections (for example the Qc2Tex2 function described above).

  • Optionally includes the QC item sections into addition header and footer, either by writing these directly or by inserting the sections into an appropriate template. The reporting functions that are available in qcmetrics can be found in ?qcReport: reporting_tex for type tex, reporting\_pdf for type pdf, … These functions should use the same arguments as qcReport insofar as possible.

  • Once written to a report source file, the final report type is generated. knit is used to convert the Rnw source to tex which is compiled into pdf using tools::texi2pdf. The Rmd content is directly written into a file which is knitted and converted to html using knit2html (which call markdownTOHTML).

New reporting_abc functions can be called directly or passed to qcReport using the reporter argument.

4.3 QC packages

While the examples presented in section 3 are flexible and fast ways to design QC pipeline prototypes, a more robust mechanism is desirable for production pipelines. The R packaging mechanism is ideally suited for this as it provides versioning, documentation, unit testing and easy distribution and installation facilities.

While the detailed description of package development is out of the scope of this document, it is of interest to provide an overview of the development of a QC package. Taking the wrapper function, it could be used the create the package structure

package.skeleton("N15QC", list = "n15qc")

The DESCRIPTION file would need to be updated. The packages qcmetrics, and MSnbas would need to be specified as dependencies in the Imports: line and imported in the NAMESPACE file. The documentation file N15QC/man/n15qc.Rd and the (optional) would need to be updated. q

5 Conclusions

R and Bioconductor are well suited for the analysis of high throughput biology data. They provide first class statistical routines, excellent graph capabilities and an interface of choice to import and manipulate various omics data, as demonstrated by the wealth of packages that provide functionalities for QC.

The qcmetrics package is different than existing R packages and QC systems in general. It proposes a unique domain-independent framework to design QC pipelines and is thus suited for any use case. The examples presented in this document illustrated the application of qcmetrics on data containing single or multiple samples or experimental runs from different technologies. It is also possible to automate the generation of QC metrics for a set of repeated (and growing) analyses of standard samples to establish lab memory types of QC reports, that track a set of metrics for controlled standard samples over time. It can be applied to raw data or processed data and tailored to suite precise needs. The popularisation of integrative approaches that combine multiple types of data in novel ways stresses out the need for flexible QC development.

qcmetrics is a versatile software that allows rapid and easy QC pipeline prototyping and development and supports straightforward migration to production level systems through its well defined packaging mechanism.

Acknowledgements: Many thanks to Arnoud Groen for providing the N15 data and Andrzej Oles for helpful comments and suggestions about the package and this document.

6 Session information

All software and respective versions used to produce this document are listed below.

sessionInfo()
## R version 4.3.2 Patched (2023-11-13 r85521)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## 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] ggplot2_3.4.4       MSnbase_2.28.1      ProtGenerics_1.34.0
##  [4] S4Vectors_0.40.2    Biobase_2.62.0      BiocGenerics_0.48.1
##  [7] mzR_2.36.0          Rcpp_1.0.12         qcmetrics_1.40.1   
## [10] BiocStyle_2.30.0   
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.4          impute_1.76.0         xfun_0.41            
##  [4] bslib_0.6.1           lattice_0.22-5        vctrs_0.6.5          
##  [7] tools_4.3.2           generics_0.1.3        parallel_4.3.2       
## [10] tibble_3.2.1          fansi_1.0.6           highr_0.10           
## [13] cluster_2.1.6         vsn_3.70.0            pkgconfig_2.0.3      
## [16] lifecycle_1.0.4       compiler_4.3.2        statmod_1.5.0        
## [19] munsell_0.5.0         codetools_0.2-19      ncdf4_1.22           
## [22] clue_0.3-65           htmltools_0.5.7       sass_0.4.8           
## [25] yaml_2.3.8            preprocessCore_1.64.0 pillar_1.9.0         
## [28] jquerylib_0.1.4       MASS_7.3-60.0.1       BiocParallel_1.36.0  
## [31] limma_3.58.1          affy_1.80.0           cachem_1.0.8         
## [34] magick_2.8.2          iterators_1.0.14      foreach_1.5.2        
## [37] MALDIquant_1.22.2     pcaMethods_1.94.0     tidyselect_1.2.0     
## [40] digest_0.6.34         dplyr_1.1.4           pander_0.6.5         
## [43] bookdown_0.37         fastmap_1.1.1         grid_4.3.2           
## [46] colorspace_2.1-0      cli_3.6.2             magrittr_2.0.3       
## [49] XML_3.99-0.16.1       utf8_1.2.4            withr_3.0.0          
## [52] scales_1.3.0          rmarkdown_2.25        affyio_1.72.0        
## [55] mzID_1.40.0           evaluate_0.23         knitr_1.45           
## [58] IRanges_2.36.0        doParallel_1.0.17     rlang_1.1.3          
## [61] xtable_1.8-4          glue_1.7.0            BiocManager_1.30.22  
## [64] jsonlite_1.8.8        R6_2.5.1              plyr_1.8.9           
## [67] zlibbioc_1.48.0       MsCoreUtils_1.14.1

References

Allaire, JJ, J Horner, V Marti, and N Porte. 2013. Markdown: Markdown Rendering for R. http://CRAN.R-project.org/package=markdown.

Foster, K M, S Degroeve, L Gatto, M Visser, R Wang, K Griss, R Apweiler, and L Martens. 2011. “A Posteriori Quality Control for the Curation and Reuse of Public Proteomics Data.” Proteomics 11 (11): 2182–94. https://doi.org/10.1002/pmic.201000602.

Gatto, L, and K S Lilley. 2012. “MSnbase – an R/Bioconductor Package for Isobaric Tagged Mass Spectrometry Data Visualization, Processing and Quantitation.” Bioinformatics 28 (2): 288–9. https://doi.org/10.1093/bioinformatics/btr645.

Groen, A, L Thomas, K Lilley, and C Marondedze. 2013. “Identification and Quantitation of Signal Molecule-Dependent Protein Phosphorylation.” Methods Mol Biol 1016: 121–37. https://doi.org/10.1007/978-1-62703-441-8_9.

Krijgsveld, J, R F Ketting, T Mahmoudi, J Johansen, M Artal-Sanz, C P Verrijzer, R H Plasterk, and A J Heck. 2003. “Metabolic Labeling of c. Elegans and d. Melanogaster for Quantitative Proteomics.” Nat Biotechnol 21 (8): 927–31. https://doi.org/10.1038/nbt848.

Pedrioli, P G A, and others. 2004. “A Common Open Representation of Mass Spectrometry Data and Its Application to Proteomics Research.” Nat. Biotechnol. 22 (11): 1459–66. https://doi.org/10.1038/nbt1031.

Xie, Y. 2013. Dynamic Documents with R and Knitr. Chapman; Hall/CRC. http://yihui.name/knitr/.