Index file for the qcmetrics package vignette
29 October 2024
Abstract
The qcmetrics package is a framework that provides simple data containers for quality metrics and support for automatic report generation. This document briefly illustrates the core data structures and then demonstrates the generation and automation of quality control reports for microarray and proteomics data.
Package
qcmetrics 1.45.0
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 2.4016show(qc) ## or just qc## Object of class "QcMetric"
## Name: A test metric
## Status: NA
## Data: xstatus(qc) <- TRUE
qc## Object of class "QcMetric"
## Name: A test metric
## Status: TRUE
## Data: xPlotting 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 definedplot(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 et al. 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")
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: 1.9.7The 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_PeptidesThe 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.
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: 0x652b0eba7f80>
## <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).
Qc2Tex2To 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
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
Qc2Tex2function 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
qcmetricscan be found in?qcReport:reporting_texfor type tex,reporting\_pdffor typepdf, … These functions should use the same arguments asqcReportinsofar as possible.Once written to a report source file, the final report type is generated.
knitis used to convert the Rnw source to tex which is compiled into pdf usingtools::texi2pdf. The Rmd content is directly written into a file which is knitted and converted to html usingknit2html(which callmarkdownTOHTML).
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 Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
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## [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_3.5.1 MSnbase_2.33.0 ProtGenerics_1.39.0
## [4] S4Vectors_0.45.0 Biobase_2.67.0 BiocGenerics_0.53.0
## [7] mzR_2.41.0 Rcpp_1.0.13 qcmetrics_1.45.0
## [10] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] rlang_1.1.4 magrittr_2.0.3
## [3] clue_0.3-65 matrixStats_1.4.1
## [5] compiler_4.5.0 vctrs_0.6.5
## [7] reshape2_1.4.4 stringr_1.5.1
## [9] pkgconfig_2.0.3 crayon_1.5.3
## [11] fastmap_1.2.0 magick_2.8.5
## [13] XVector_0.47.0 pander_0.6.5
## [15] utf8_1.2.4 rmarkdown_2.28
## [17] UCSC.utils_1.3.0 preprocessCore_1.69.0
## [19] tinytex_0.53 purrr_1.0.2
## [21] xfun_0.48 MultiAssayExperiment_1.33.0
## [23] zlibbioc_1.53.0 cachem_1.1.0
## [25] GenomeInfoDb_1.43.0 jsonlite_1.8.9
## [27] highr_0.11 DelayedArray_0.33.0
## [29] BiocParallel_1.41.0 parallel_4.5.0
## [31] cluster_2.1.6 R6_2.5.1
## [33] bslib_0.8.0 stringi_1.8.4
## [35] limma_3.63.0 GenomicRanges_1.59.0
## [37] jquerylib_0.1.4 bookdown_0.41
## [39] SummarizedExperiment_1.37.0 iterators_1.0.14
## [41] knitr_1.48 IRanges_2.41.0
## [43] Matrix_1.7-1 igraph_2.1.1
## [45] tidyselect_1.2.1 abind_1.4-8
## [47] yaml_2.3.10 doParallel_1.0.17
## [49] codetools_0.2-20 affy_1.85.0
## [51] lattice_0.22-6 tibble_3.2.1
## [53] plyr_1.8.9 withr_3.0.2
## [55] evaluate_1.0.1 pillar_1.9.0
## [57] affyio_1.77.0 BiocManager_1.30.25
## [59] MatrixGenerics_1.19.0 foreach_1.5.2
## [61] MALDIquant_1.22.3 ncdf4_1.23
## [63] generics_0.1.3 munsell_0.5.1
## [65] scales_1.3.0 xtable_1.8-4
## [67] glue_1.8.0 lazyeval_0.2.2
## [69] tools_4.5.0 mzID_1.45.0
## [71] QFeatures_1.17.0 vsn_3.75.0
## [73] XML_3.99-0.17 grid_4.5.0
## [75] impute_1.81.0 tidyr_1.3.1
## [77] MsCoreUtils_1.19.0 colorspace_2.1-1
## [79] GenomeInfoDbData_1.2.13 PSMatch_1.11.0
## [81] cli_3.6.3 fansi_1.0.6
## [83] S4Arrays_1.7.0 dplyr_1.1.4
## [85] AnnotationFilter_1.31.0 pcaMethods_1.99.0
## [87] gtable_0.3.6 sass_0.4.9
## [89] digest_0.6.37 SparseArray_1.7.0
## [91] htmltools_0.5.8.1 lifecycle_1.0.4
## [93] httr_1.4.7 statmod_1.5.0
## [95] MASS_7.3-61