MsQuality 1.2.1
Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval.
We present here the MsQuality package, which provides functionality to
calculate quality metrics for mass spectrometry-derived, spectral data at the
per-sample level. MsQuality relies on the
mzQC framework of quality metrics defined
by the Human Proteome Organization-Proteomics Standards Intitiative (HUPO-PSI).
These metrics quantify the quality of spectral raw files using a controlled
vocabulary. The package is especially addressed towards users that acquire
mass spectrometry data on a large scale (e.g. data sets from clinical settings
consisting of several thousands of samples): while it is easier to control
for high-quality data acquisition in small-scale experiments, typically run
in one or few batches, clinical data sets are often acquired over longer
time frames and are prone to higher technical variation that is often
unnoticed. MsQuality tries to address this problem by calculating metrics that
can be stored along the spectral data sets (raw files or feature-extracted
data sets). MsQuality, thus, facilitates the tracking of shifts in data
quality and quantifies the quality using multiple metrics. It should be thus
easier to identify samples that are of low quality (high-number of missing
values, termination of chromatographic runs, low instrument sensitivity, etc.).
We would like to note here that these metrics only give an indication of
data quality, and, before removing indicated low-quality samples from the
analysis more advanced analytics, e.g. using the implemented functionality
and visualizations in the MatrixQCvis package, should be scrutinized.
Also, data quality
should always be regarded in the context of the sample type and experimental
settings, i.e. quality metrics should always be compared with regard to the
sample type, experimental setup, instrumentation, etc..
The MsQuality package allows to calculate low-level quality metrics that
require minimum information on mass spectrometry data: retention time,
m/z values, and associated intensities.
The list included in the mzQC framework is excessive, also including
metrics that rely on more high-level information, that might not be readily
accessible from .raw or .mzML files, e.g. pump pressure mean, or rely
on alignment results, e.g. retention time mean shift, signal-to-noise ratio,
precursor errors (ppm).
The MsQuality package is built upon the Spectra and the MsExperiment
package.
Metrics will be calculated based on the information stored in a
Spectra object and the respective dataOrigin entries are used to
distinguish between the mass spectral data of multiple samples.
The MsExperiment serves as a container to
store the mass spectral data of multiple samples. MsQuality enables the user
to calculate quality metrics both on Spectra and MsExperiment objects.
MsQuality can be used for any type of experiment that can be represented as
a Spectra or MsExperiment object. This includes simple LC-MS data, DIA or
DDA-based data, ion mobility data or MS data in general. The tool can thus
be used for any type of targeted or untargeted metabolomics or proteomics
workflow. Also, we are not limited to data files in mzML format, but, through
Spectra and related MsBackend packages, data can be imported from a large
variety of formats, including some raw vendor formats.
In this vignette, we will (i) create some exemplary Spectra and MsExperiment
objects, (ii) calculate the quality metrics on these
data sets, and (iii) visualize some of the metrics.
Other R packages are available in Bioconductor that are able to assess
the quality of mass spectrometry data:
artMS
uses MaxQuant output and enables to calculate several QC metrics, e.g.
correlation matrix for technical replicates, calculation of total sum of
intensities in biological replicates, total peptide counts in biological
replicates, charge state distribution of PSMs identified in each biological
replicates, or MS1 scan counts in each biological replicate.
MSstatsQC
and the visualization tool
MSstatsQCgui
require csv files in long format from spectral processing tools such as
Skyline and Panorama autoQC or MSnbase objects. MSstatsQC enables to
generate individual, moving range, cumulative sum for mean, and/or
cumulative sum for variability control charts for each metric. Metrics
can be any kind of user-defined metric stored in the data columns for a given
peptide, e.g. retention time and peak area.
MQmetrics
provides a pipeline to analyze the quality of proteomics data sets from
MaxQuant files and focuses on proteomics-/MaxQuant-specific metrics, e.g.
proteins identified, peptides identified, or proteins versus
peptide/protein ratio.
MatrixQCvis
provides an interactive shiny-based interface to assess data quality at various
processing steps (normalization, transformation, batch correction, and
imputation) of rectangular matrices. The package includes several diagnostic
plots and metrics such as barplots of intensity distributions, plots to
visualize drifts, MA plots and Hoeffding’s D value calculation, and
dimension reduction plots and provides specific tools to analyze data sets
containing missing values as commonly observed in mass spectrometry.
proBatch
enables to assess batch effects in (prote)omics data sets and corrects these
batch effects in subsequent steps. Several tools to visualize data quality are
included in the proBatch packages, such as barplots of intensity
distributions, cluster and heatmap analysis tools, and PCA dimension
reduction plots. Additionally, proBatch enables to assess diagnostics at
the feature level, e.g. peptides or spike-ins.
To install this package, start R and enter:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
## to install from Bioconductor
BiocManager::install("MsQuality")
## to install from GitHub
BiocManager::install("tnaake/MsQuality")This will install this package and all eventually missing dependencies.
MsQuality is currently under active development. If you discover any bugs,
typos or develop ideas of improving MsQuality feel free to raise an issue
via GitHub or send a mail to the
developer.
Spectra and MsExperiment objectsLoad the Spectra package.
library("Spectra")
library("MsExperiment")
library("MsQuality")Spectra and MsExperiment objects from mzML filesThere are several options available to create a Spectra object. One way, as
outlined in the vignette of the Spectra package is
by specifying the location of mass spectrometry raw files in mzML, mzXML or
CDF format and using the MsBackendMzR backend. Here we load the example
files from the sciex data set of the msdata package and create a Spectra
object from the two provided mzML files. The example is taken from the
Spectra vignette.
## this example is taken from the Spectra vignette
fls <- dir(system.file("sciex", package = "msdata"), full.names = TRUE)
sps_sciex <- Spectra(fls, backend = MsBackendMzR())The data set consists of a single sample measured in two different
injections to the same LC-MS setup. An empty instance of an
MsExperiment object is created and populated with information on the samples
by assigning data on the samples (sampleData), information on the
mzML files (MsExperimentFiles) and spectral information (spectra).
In a last step, using linkSampleData, the relationships between the samples
and the spectral information are defined.
## this example is taken from the Spectra vignette
lmse <- MsExperiment()
sd <- DataFrame(sample_id = c("QC1", "QC2"),
sample_name = c("QC Pool", "QC Pool"),
injection_idx = c(1, 3))
sampleData(lmse) <- sd
## add mzML files to the experiment
experimentFiles(lmse) <- MsExperimentFiles(mzML_files = fls)
## add the Spectra object to the experiment
spectra(lmse) <- sps_sciex
## use linkSampleData to establish and define relationships between sample
## annotations and MS data
lmse <- linkSampleData(lmse, with = "experimentFiles.mzML_file",
sampleIndex = c(1, 2), withIndex = c(1, 2))Spectra and MsExperiment objects from (feature-extracted) intensity tablesAnother common approach is the creation of Spectra objects from a
DataFrames using the MsBackendDataFrame backend.
We will use here the data set of Lee et al. (2019), that contains metabolite level
information measured by reverse phase liquid chromatography (RPLC) coupled
to mass spectrometry and hydrophilic interaction liquid chromatography (HILIC)
coupled to mass spectrometry
(derived from the file STables - rev1.xlsx in the Supplementary Information).
In a separate step (see documentation for Lee2019_meta_vals and
Lee2019), we have created a list containing Spectra objects for
each samples (objects sps_l_rplc and sps_l_hilic) and MsExperiment
objects containing the data of all samples (objects msexp_rplc and
msexp_hilic). We will load here these objects:
data("Lee2019", package = "MsQuality")The final data set contains 541 paired samples (i.e. 541 samples derived from RPLC and 541 samples derived from HILIC).
We will combine the sps_rplc and sps_hilic objects in the following and
calculate on this combined document the metrics.
sps_comb <- c(sps_rplc, sps_hilic)The most important function to assess the data quality and to calculate the
metrics is the calculateMetrics function. The function takes
a Spectra or MsExperiment object as input, a character vector of metrics
to be calculated, and, optionally a list of parameters passed to the
quality metrics functions.
Spectra and MsExperiment objectsCurrently, the following metrics are included:
qualityMetrics(sps_comb)## [1] "chromatographyDuration" "ticQuartersRtFraction"
## [3] "rtOverMsQuarters" "ticQuartileToQuartileLogRatio"
## [5] "numberSpectra" "numberEmptyScans"
## [7] "medianPrecursorMz" "rtIqr"
## [9] "rtIqrRate" "areaUnderTic"
## [11] "areaUnderTicRtQuantiles" "extentIdentifiedPrecursorIntensity"
## [13] "medianTicRtIqr" "medianTicOfRtRange"
## [15] "mzAcquisitionRange" "rtAcquisitionRange"
## [17] "precursorIntensityRange" "precursorIntensityQuartiles"
## [19] "precursorIntensityMean" "precursorIntensitySd"
## [21] "msSignal10xChange" "ratioCharge1over2"
## [23] "ratioCharge3over2" "ratioCharge4over2"
## [25] "meanCharge" "medianCharge"The following list gives a brief explanation for these metrics. Further
information may be found at the
HUPO-PSI mzQC project page or in the
respective help file for the quality metric (accessible by e.g. entering
?chromatographyDuration to the R console).
We also give explanation on how the metric is calculated in MsQuality.
Currently, all quality metrics can be calculated for both Spectra and
MsExperiment objects.
chromatographyDuration, chromatography duration (MS:4000053), “The retention time duration of the chromatography in seconds.” [PSI:MS]; Longer duration may indicate a better chromatographic separation of compounds which depends, however, also on the sampling/scan rate of the MS instrument.
The metric is calculated as follows:
Spectra object is obtained,ticQuartersRtFraction, TIC quarters RT fraction (MS:4000054), “The interval when the respective quarter of the TIC accumulates divided by retention time duration.” [PSI:MS]; The metric informs about the dynamic range of the acquisition along the chromatographic separation. The metric provides information on the sample (compound) flow along the chromatographic run, potentially revealing poor chromatographic performance, such as the absence of a signal for a significant portion of the run.
The metric is calculated as follows:
Spectra object is ordered according to the retention time,probs argument,
e.g. when probs is set to c(0, 0.25, 0.5, 0.75, 1) the
0%, 25%, 50%, 75%, and 100% quantile is calculated,rtOverMsQuarters, MS1 quarter RT fraction (MS:4000055),
“The interval used for acquisition of the first, second, third, and fourth
quarter of all MS1 events divided by retention time duration.” [PSI:MS],
msLevel = 1L;
The metric informs about the dynamic range of the acquisition along the
chromatographic separation. For MS1 scans, the values are expected to be in
a similar range across samples of the same type.
The metric is calculated as follows:
Spectra object is determined
(taking into account all the MS levels),Spectra object is filtered according to the MS level and
subsequently ordered according to the retention timertOverMsQuarters, MS2 quarter RT fraction (MS:4000056),
“The interval used for acquisition of the first, second, third, and fourth
quarter of all MS2 events divided by retention time duration.” [PSI:MS],
msLevel = 2L;
The metric informs about the dynamic range of the acquisition along the
chromatographic separation. For MS2 scans, the comparability of the values
depends on the acquisition mode and settings to select ions for fragmentation.
The metric is calculated as follows:
Spectra object is determined
(taking into account all the MS levels),Spectra object is filtered according to the MS level and
subsequently ordered according to the retention timeticQuartileToQuartileLogRatio, MS1 TIC-change quartile ratios
(MS:4000057), "“The log ratios of successive TIC-change quartiles. The TIC
changes are the list of MS1 total ion current (TIC) value changes from
one to the next scan, produced when each MS1 TIC is subtracted from the
preceding MS1 TIC. The metric’s value triplet represents the log ratio of the
TIC-change Q2 to Q1, Q3 to Q2, TIC-change-max to Q3” [PSI:MS],
mode = "TIC_change", relativeTo = "previous", msLevel = 1L;
The metric informs about the dynamic range of the acquisition along the
chromatographic separation.This metric evaluates the stability (similarity)
of MS1 TIC values from scan to scan along the LC run. High log ratios
representing very large intensity differences between pairs of scans might
be due to electrospray instability or presence of a chemical contaminant.
The metric is calculated as follows:
ionCount) of the Spectra object is calculated
per scan event (with spectra ordered by retention time),log values of the ratios are returned.ticQuartileToQuartileLogRatio, MS1 TIC quartile ratios (MS:4000058),
“The log ratios of successive TIC quartiles. The metric’s value triplet
represents the log ratios of TIC-Q2 to TIC-Q1, TIC-Q3 to TIC-Q2,
TIC-max to TIC-Q3.” [PSI:MS], mode = "TIC",
relativeTo = "previous", msLevel = 1L;
The metric informs about the dynamic range of the acquisition along the
chromatographic separation. The ratios provide information on the distribution
of the TIC values for one LC-MS run. Within an experiment, with the same
LC setup, values should be comparable between samples.
The metric is calculated as follows:
ionCount) of the Spectra object is calculated
per scan event (with spectra ordered by retention time),log values of the ratios are returned.numberSpectra, number of MS1 spectra MS:4000059),
“The number of MS1 events in the run.” [PSI:MS], msLevel = 1L;
An unusual low number may indicate incomplete sampling/scan rate of the MS
instrument, low sample volume and/or failed injection of a sample.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,length of Spectra) and
returned.numberSpectra, number of MS2 spectra (MS:4000060),
“The number of MS2 events in the run.” [PSI:MS], msLevel = 2L;
An unusual low number may indicate incomplete sampling/scan rate of the MS
instrument, low sample volume and/or failed injection of a sample.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,length of Spectra) and
returned.mzAcquisitionRange, m/z acquisition range (MS:4000069), “Upper and lower limit of m/z precursor values at which MSn spectra are recorded.” [PSI:MS]; The metric informs about the dynamic range of the acquisition. Based on the used MS instrument configuration, the values should be similar. Variations between measurements may arise when employing acquisition in DDA mode.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are obtained,rtAcquisitionRange, retention time acquisition range (MS:4000070), “Upper and lower limit of retention time at which spectra are recorded.” [PSI:MS]; An unusual low range may indicate incomplete sampling and/or a premature or failed LC run.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra
object are obtained,msSignal10xChange, MS1 signal jump (10x) count (MS:4000097),
“The number of times where MS1 TIC increased more than 10-fold between adjacent
MS1 scans. An unusual high count of signal jumps or falls can indicate
ESI stability issues.” [PSI:MS], change = "jump", msLevel = 1L;
An unusual high count of signal jumps or falls may indicate ESI stability
issues.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,msSignal10xChange, MS1 signal fall (10x) count (MS:4000098),
“The number of times where MS1 TIC decreased more than 10-fold between adjacent
MS1 scans. An unusual high count of signal jumps or falls can indicate
ESI stability issues.” [PSI:MS], change = "fall", msLevel = 1L;
An unusual high count of signal jumps or falls may indicate ESI stability
issues.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,numberEmptyScans, number of empty MS1 scans (MS:4000099),
“Number of MS1 scans where the scans’ peaks intensity sums to 0 (i.e. no
peaks or only 0-intensity peaks).” [PSI:MS], msLevel = 1L;
An unusual high number may indicate incomplete sampling/scan rate of the MS
instrument, low sample volume and/or failed injection of a sample.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,NULL, NA, or
that have a sum of 0 are obtained and returned.numberEmptyScans, number of empty MS2 scans (MS:4000100),
“Number of MS2 scans where the scans’ peaks intensity sums to 0 (i.e. no
peaks or only 0-intensity peaks).” [PSI:MS], msLevel = 2L;
An unusual high number may indicate incomplete sampling/scan rate of the MS
instrument, low sample volume and/or failed injection of a sample.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,NULL, NA, or
that have a sum of 0 are obtained and returned.numberEmptyScans, number of empty MS3 scans (MS:4000101),
“Number of MS3 scans where the scans’ peaks intensity sums to 0 (i.e. no
peaks or only 0-intensity peaks).” [PSI:MS], msLevel = 3L;
An unusual high number may indicate incomplete sampling/scan rate of the MS
instrument, low sample volume and/or failed injection of a sample.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,NULL, NA, or
that have a sum of 0 are obtained and returned.precursorIntensityQuartiles,
MS2 precursor intensity distribution Q1, Q2, Q3 (MS:4000116),
“From the distribution of MS2 precursor intensities, the quartiles
Q1, Q2, Q3.” [PSI:MS], identificationLevel = "all";
The intensity distribution of the precursors informs about the dynamic range
of the acquisition.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are obtained,NA values are removed) and returned.precursorIntensityMean, MS2 precursor intensity distribution mean
(MS:4000117), “From the distribution of MS2 precursor intensities, the mean.”
[PSI:MS], identificationLevel = "all";
The intensity distribution of the precursors informs about the dynamic range
of the acquisition.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,NA values are removed) and returned.precursorIntensitySd, MS2 precursor intensity distribution sigma
(MS:4000118), “From the distribution of MS2 precursor intensities, the sigma
value.” [PSI:MS], identificationLevel = "all";
The intensity distribution of the precursors informs about the dynamic range of
the acquisition.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,NA values are removed) and returned.medianPrecursorMz,
MS2 precursor median m/z of identified quantification data points
(MS:4000152),
“Median m/z value for MS2 precursors of all quantification data points after
user-defined acceptance criteria are applied. These data points may be for
example XIC profiles, isotopic pattern areas, or reporter ions
(see MS:1001805). The used type should be noted in the metadata or
analysis methods section of the recording file for the respective run. In
case of multiple acceptance criteria (FDR) available in proteomics, PSM-level
FDR should be used for better comparability.” [PSI:MS],
identificationLevel = "identified", msLevel = 1L;
The m/z distribution informs about the dynamic range of the acquisition.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,NAs are removed).rtIqr,
interquartile RT period for identified quantification data points
(MS:4000153), “The interquartile retention time period, in seconds, for all
quantification data points after user-defined acceptance criteria are applied
over the complete run. These data points may be for example XIC profiles,
isotopic pattern areas, or reporter ions (see MS:1001805). The used type
should be noted in the metadata or analysis methods section of the recording
file for the respective run. In case of multiple acceptance criteria (FDR)
available in proteomics, PSM-level FDR should be used for better
comparability.”
[PSI:MS], identificationLevel = "identified";
Longer duration may indicate a better chromatographic separation of compounds
which depends, however, also on the sampling/scan rate of the MS instrument.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,NA values are removed).rtIqrRate,
rate of the interquartile RT period for identified quantification data points
(MS:4000154), “The rate of identified quantification data points for the
interquartile retention time period, in identified quantification data points
per second. These data points may be for example XIC profiles, isotopic
pattern areas, or reporter ions (see MS:1001805). The used type should
be noted in the metadata or analysis methods section of the recording
file for the respective run. In case of multiple acceptance criteria (FDR)
available in proteomics, PSM-level FDR should be used for better
comparability.” [PSI:MS],
identificationLevel = "identified";
Higher rates may indicate a more efficient sampling and identification.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,NA values are removed),areaUnderTic, area under TIC (MS:4000155), “The area under the total ion chromatogram.” [PSI:MS]; The metric informs about the dynamic range of the acquisition. Differences between samples of an experiment may indicate differences in the dynamic range and/or in the sample content.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,areaUnderTicRtQuantiles, area under TIC RT quantiles (MS:4000156), “The area under the total ion chromatogram of the retention time quantiles. Number of quantiles are given by the n-tuple.” [PSI:MS]; The metric informs about the dynamic range of the acquisition. Differences between samples of an experiment may indicate differences in the dynamic range and/or in the sample content. The metric informs about the dynamic range of the acquisition along the chromatographic separation. Differences between samples of an experiment may indicate differences in chromatographic performance, differences in the dynamic range and/or in the sample content.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object is ordered according to the retention time,extentIdentifiedPrecursorIntensity,
extent of identified MS2 precursor intensity (MS:4000157),
“Ratio of 95th over 5th percentile of MS2 precursor intensity for all
quantification data points after user-defined acceptance criteria are
applied. The used type of identification should be noted in the metadata or
analysis methods section of the recording file for the respective run.
In case of multiple acceptance criteria (FDR) available in proteomics,
PSM-level FDR should be used for better comparability.” [PSI:MS],
identificationLevel = "identified";
The metric informs about the dynamic range of the acquisition.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,medianTicRtIqr, median of TIC values in the RT range in which the middle
half of quantification data points are identified (MS:4000158),
“Median of TIC values in the RT range in which half of quantification data
points are identified (RT values of Q1 to Q3 of identifications). These
data points may be for example XIC profiles, isotopic pattern areas, or
reporter ions (see MS:1001805). The used type should be noted in the metadata
or analysis methods section of the recording file for the respective run.
In case of multiple acceptance criteria (FDR) available in proteomics,
PSM-level FDR should be used for better comparability.” [PSI:MS],
identificationLevel = "identified";
The metric informs about the dynamic range of the acquisition along the
chromatographic separation.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object is ordered according to the retention time,Spectra object),NA values are
removed) and the median value is returned.medianTicOfRtRange, median of TIC values in the shortest RT range in
which half of the quantification data points are identified (MS:4000159),
“Median of TIC values in the shortest RT range in which half of the
quantification data points are identified. These data points may be for
example XIC profiles, isotopic pattern areas, or reporter ions
(see MS:1001805). The used type should be noted in the metadata or analysis
methods section of the recording file for the respective run. In case of
multiple acceptance criteria (FDR) available in proteomics, PSM-level FDR
should be used for better comparability.” [PSI:MS],
identificationLevel = "identified";
The metric informs about the dynamic range of the acquisition along the
chromatographic separation.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object is ordered according to the retention time,Spectra object is obtained and
the number for half of the features is calculated,NA values are removed)
and return it.precursorIntensityRange, MS2 precursor intensity range (MS:4000160), “Minimum and maximum MS2 precursor intensity recorded.” [PSI:MS]; The metric informs about the dynamic range of the acquisition.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,precursorIntensityQuartiles,
identified MS2 precursor intensity distribution Q1, Q2, Q3 (MS:4000161),
“From the distribution of identified MS2 precursor intensities, the quartiles
Q1, Q2, Q3. The used type of identification should be noted in the metadata
or analysis methods section of the recording file for the respective run.
In case of multiple acceptance criteria (FDR) available in proteomics,
PSM-level FDR should be used for better comparability.” [PSI:MS],
identificationLevel = "identified";
The metric informs about the dynamic range of the acquisition in relation to
identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are obtained,NA values are removed) and returned.precursorIntensityQuartiles,
unidentified MS2 precursor intensity distribution Q1, Q2, Q3 (MS:4000162),
“From the distribution of unidentified MS2 precursor intensities, the
quartiles Q1, Q2, Q3. The used type of identification should be noted in the
metadata or analysis methods section of the recording file for the respective
run. In case of multiple acceptance criteria (FDR) available in proteomics,
PSM-level FDR should be used for better comparability.”
[PSI:MS], identificationLevel = "unidentified";
The metric informs about the dynamic range of the acquisition in relation to
identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,NA values are removed) and returned.precursorIntensityMean,
identified MS2 precursor intensity distribution mean (MS:4000163),
“From the distribution of identified MS2 precursor intensities, the mean.
The intensity distribution of the identified precursors informs about the
dynamic range of the acquisition in relation to identifiability. The used
type of identification should be noted in the metadata or analysis methods
section of the recording file for the respective run. In case of multiple
acceptance criteria (FDR) available in proteomics, PSM-level FDR should be
used for better comparability.”
[PSI:MS], identificationLevel = "identified";
The metric informs about the dynamic range of the acquisition in relation to
identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,NA values are removed) and returned.precursorIntensityMean,
unidentified MS2 precursor intensity distribution mean (MS:4000164),
“From the distribution of unidentified MS2 precursor intensities, the mean.
The used type of identification should be noted in the metadata or analysis
methods section of the recording file for the respective run. In case of
multiple acceptance criteria (FDR) available in proteomics, PSM-level FDR
should be used for better comparability.” [PSI:MS],
identificationLevel = "unidentified";
The metric informs about the dynamic range of the acquisition in relation
to identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,NA values are removed) and returned.precursorIntensitySd,
identified MS2 precursor intensity distribution sigma (MS:4000165),
“From the distribution of identified MS2 precursor intensities, the sigma
value. The used type of identification should be noted in the metadata or
analysis methods section of the recording file for the respective run. In
case of multiple acceptance criteria (FDR) available in proteomics, PSM-level
FDR should be used for better comparability.” [PSI:MS],
identificationLevel = "identified";
The metric informs about the dynamic range of the acquisition in relation to
identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,NA values are removed) and returned.precursorIntensitySD,
unidentified MS2 precursor intensity distribution sigma (MS:4000166),
“From the distribution of unidentified MS2 precursor intensities, the sigma
value. The used type of identification should be noted in the metadata or
analysis methods section of the recording file for the respective run. In
case of multiple acceptance criteria (FDR) available in proteomics, PSM-level
FDR should be used for better comparability.” [PSI:MS],
identificationLevel = "unidentified";
The metric informs about the dynamic range of the acquisition in relation to
identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,Spectra object are
obtained,NA values are removed) and returned.ratioCharge1over2,
ratio of 1+ over 2+ of all MS2 known precursor charges (MS:4000167),
“The ratio of 1+ over 2+ MS2 precursor charge count of all spectra.” [PSI:MS],
identificationLevel = "all";
High ratios of 1+/2+ MS2 precursor charge count may indicate inefficient
ionization.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,ratioCharge1over2,
ratio of 1+ over 2+ of identified MS2 known precursor charges (MS:4000168),
"“The ratio of 1+ over 2+ MS2 precursor charge count of identified spectra.
The used type of identification should be noted in the metadata or analysis
methods section of the recording file for the respective run. In case of
multiple acceptance criteria (FDR) available in proteomics, PSM-level FDR
should be used for better comparability.” [PSI:MS],
identificationLevel = "identified";
High ratios of 1+/2+ MS2 precursor charge count may indicate inefficient
ionization in relation to identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,ratioCharge3over2,
ratio of 3+ over 2+ of all MS2 known precursor charges (MS:4000169),
“The ratio of 3+ over 2+ MS2 precursor charge count of all spectra.” [PSI:MS],
identificationLevel = "all";
Higher ratios of 3+/2+ MS2 precursor charge count may indicate e.g.
preference for longer peptides.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,ratioCharge3over2,
ratio of 3+ over 2+ of identified MS2 known precursor charges (MS:4000170),
“The ratio of 3+ over 2+ MS2 precursor charge count of identified spectra.
The used type of identification should be noted in the metadata or analysis
methods section of the recording file for the respective run. In case of
multiple acceptance criteria (FDR) available in proteomics, PSM-level
FDR should be used for better comparability.” [PSI:MS],
identificationLevel = "identified";
Higher ratios of 3+/2+ MS2 precursor charge count may indicate e.g.
preference for longer peptides in relation to identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,ratioCharge4over2,
ratio of 4+ over 2+ of all MS2 known precursor charges (MS:4000171),
“The ratio of 4+ over 2+ MS2 precursor charge count of all spectra.”
[PSI:MS], identificationLevel = "all";
Higher ratios of 3+/2+ MS2 precursor charge count may indicate e.g.
preference for longer peptides.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,ratioCharge4over2,
ratio of 4+ over 2+ of identified MS2 known precursor charges (MS:4000172),
“The ratio of 4+ over 2+ MS2 precursor charge count of identified spectra.
The used type of identification should be noted in the metadata or analysis
methods section of the recording file for the respective run. In case of
multiple acceptance criteria (FDR) available in proteomics, PSM-level FDR
should be used for better comparability.” [PSI:MS],
identificationLevel = "identified";
Higher ratios of 3+/2+ MS2 precursor charge count may indicate e.g.
preference for longer peptides in relation to identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,meanCharge, mean MS2 precursor charge in all spectra (MS:4000173),
“Mean MS2 precursor charge in all spectra” [PSI:MS],
identificationLevel = "all";
Higher charges may indicate inefficient ionization or e.g. preference for
longer peptides.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,meanCharge, mean MS2 precursor charge in identified spectra (MS:4000174),
“Mean MS2 precursor charge in identified spectra. The used type of
identification should be noted in the metadata or analysis methods section
of the recording file for the respective run. In case of multiple acceptance
criteria (FDR) available in proteomics, PSM-level FDR should be used for
better comparability.” [PSI:MS], identificationLevel = "identified";
Higher charges may indicate inefficient ionization or e.g. preference for
longer peptides in relation to identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,medianCharge, median MS2 precursor charge in all spectra (MS:4000175),
“Median MS2 precursor charge in all spectra” [PSI:MS],
identificationLevel = "all";
Higher charges may indicate inefficient ionization and/or e.g. preference
for longer peptides.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,medianCharge, median MS2 precursor charge in identified spectra
(MS:4000176), “Median MS2 precursor charge in identified spectra. The used
type of identification should be noted in the metadata or analysis methods
section of the recording file for the respective run. In case of multiple
acceptance criteria (FDR) available in proteomics, PSM-level FDR should be
used for better comparability.” [PSI:MS],
identificationLevel = "identified";
Higher charges may indicate inefficient ionization and/or e.g. preference for
longer peptides in relation to identifiability.
The metric is calculated as follows:
Spectra object is filtered according to the MS level,data.frame-formatThe most important function to assess the data quality and to calculate the
metrics is the calculateMetrics function. The function takes
a Spectra or MsExperiment object as input, a character vector of metrics
to be calculated, a Boolean value to the filterEmptySpectra argument,
and, optionally a list of parameters passed to the quality metrics functions.
The filterEmptySpectra argument specifies if zero-intensity, Inf-intensityor zero-length entries should be removed (filterEmptySpectra = TRUE). By default, the entries are taken as they are (filterEmptySpectra = FALSE). The argument can be set toTRUEto compute metrics that are close to the implementation of theQuaMetersoftware. Prior to calculating the metrics, the implementation ofQuaMeterskips all spectra withdefaultArrayLength=0`
(in .mzML files) at any MS level.
When passing a Spectra/MsExperiment object to the function, a data.frame
returned by calculateMetrics with the metrics specified by the argument
metrics. By default, qualityMetrics(object) is taken to specify
the calculation of quality metrics. calculateMetrics also accepts a list
of parameters passed to the individual quality metrics functions. For
each quality metrics functions, the relevant parameters are selected based on
the accepted arguments.
Additional arguments can be given to the quality metrics functions.
For example, the function ticQuartileToQuartileLogRatio function has the
arguments relativeTo, mode, and msLevel. relativeTo specifies to which
quantile the log TIC quantile is relatively related to (either to the 1st
quantile or the respective previous one). mode (either "TIC_change" or
"TIC") specifies if the quantiles are taken from the changes between TICs
of scan events or the TICs directly. One Spectra/MsExperiment object may
also contain more than one msLevel, e.g. if it also contains information on
MS\(^2\) or MS\(^3\) features. If the user adds the arguments
relativeTo = "Q1", mode = "TIC", msLevel = c(1L, 2L)),
ticQuartileToQuartileLogRatio is run with the parameter combinations
relativeTo = "Q1", mode = "TIC", msLevel = c(1L, 2L).
The results based on these parameter combinations are returned and the used parameters are returned as attributes to the returned vector.
Here, we would like to calculate the metrics of all included quality
metrics functions (qualityMetrics(object)) and additionally pass the
parameter relativeTo = "Q1" and relativeTo = "previous". For computational
reasons, we will restrict the calculation of the metrics to the first
sample and to RPLC samples.
## subset the Spectra objects
sps_comb_subset <- sps_comb[grep("Sample.1_", sps_comb$dataOrigin), ]
## for RPLC and HILIC
metrics_sps_Q1 <- calculateMetrics(object = sps_comb_subset,
metrics = qualityMetrics(sps_comb_subset), filterEmptySpectra = FALSE,
relativeTo = "Q1", msLevel = 1L)
metrics_sps_Q1## chromatographyDuration ticQuartersRtFraction.0%
## Sample.1_RPLC 18.214 0
## Sample.1_HILIC 16.000 0
## ticQuartersRtFraction.25% ticQuartersRtFraction.50%
## Sample.1_RPLC 0.08098166 0.08098166
## Sample.1_HILIC 0.34375000 0.34375000
## ticQuartersRtFraction.75% ticQuartersRtFraction.100%
## Sample.1_RPLC 0.1495004 1
## Sample.1_HILIC 0.5375000 1
## rtOverMsQuarters.Quarter1 rtOverMsQuarters.Quarter2
## Sample.1_RPLC 0.02893379 0.08806413
## Sample.1_HILIC 0.15625000 0.47500000
## rtOverMsQuarters.Quarter3 rtOverMsQuarters.Quarter4
## Sample.1_RPLC 0.3102009 1
## Sample.1_HILIC 0.6216875 1
## ticQuartileToQuartileLogRatio.Q2/Q1
## Sample.1_RPLC NaN
## Sample.1_HILIC -Inf
## ticQuartileToQuartileLogRatio.Q3/Q1
## Sample.1_RPLC NaN
## Sample.1_HILIC NaN
## ticQuartileToQuartileLogRatio.Q4/Q1 numberSpectra
## Sample.1_RPLC NaN 190
## Sample.1_HILIC NaN 165
## numberEmptyScans medianPrecursorMz rtIqr rtIqrRate
## Sample.1_RPLC 0 198.05 5.10125 18.42686
## Sample.1_HILIC 0 179.10 7.44700 11.14543
## areaUnderTic areaUnderTicRtQuantiles.25%
## Sample.1_RPLC 655624525 25612593.2
## Sample.1_HILIC 149945016 927493.9
## areaUnderTicRtQuantiles.50% areaUnderTicRtQuantiles.75%
## Sample.1_RPLC 389734297 233673968
## Sample.1_HILIC 100961764 40996727
## areaUnderTicRtQuantiles.100% extentIdentifiedPrecursorIntensity
## Sample.1_RPLC 5692460 36467.884
## Sample.1_HILIC 5460065 8713.906
## medianTicRtIqr medianTicOfRtRange mzAcquisitionRange.min
## Sample.1_RPLC 7496.006 13858.935 60.1
## Sample.1_HILIC 2195.400 2086.417 73.0
## mzAcquisitionRange.max rtAcquisitionRange.min
## Sample.1_RPLC 1377.6 0.986
## Sample.1_HILIC 784.1 1.100
## rtAcquisitionRange.max precursorIntensityRange.min
## Sample.1_RPLC 19.2 100.6492
## Sample.1_HILIC 17.1 100.0895
## precursorIntensityRange.max precursorIntensityQuartiles.Q1
## Sample.1_RPLC 349282909 1667.3911
## Sample.1_HILIC 92021751 300.5038
## precursorIntensityQuartiles.Q2 precursorIntensityQuartiles.Q3
## Sample.1_RPLC 6746.195 75003.90
## Sample.1_HILIC 2304.383 33382.25
## precursorIntensityMean precursorIntensitySd msSignal10xChange
## Sample.1_RPLC 3450655.4 26563228 56
## Sample.1_HILIC 908757.7 7729451 47
## ratioCharge1over2 ratioCharge3over2 ratioCharge4over2 meanCharge
## Sample.1_RPLC NaN NaN NaN NaN
## Sample.1_HILIC NaN NaN NaN NaN
## medianCharge
## Sample.1_RPLC NA
## Sample.1_HILIC NA
## attr(,"chromatographyDuration")
## [1] "MS:4000053"
## attr(,"names1")
## [1] "Q1"
## attr(,"names2")
## [1] "Q2"
## attr(,"names3")
## [1] "Q3"
## attr(,"names4")
## [1] "100%"
## attr(,"names5")
## [1] "100%"
## attr(,"ticQuartersRtFraction")
## [1] "MS:4000054"
## attr(,"rtOverMsQuarters")
## [1] "MS:4000055"
## attr(,"numberSpectra")
## [1] "MS:4000059"
## attr(,"numberEmptyScans")
## [1] "MS:4000099"
## attr(,"areaUnderTic")
## [1] "MS:4000155"
## attr(,"areaUnderTicRtQuantiles")
## [1] "MS:4000156"
## attr(,"mzAcquisitionRange")
## [1] "MS:4000069"
## attr(,"rtAcquisitionRange")
## [1] "MS:4000070"
## attr(,"precursorIntensityRange")
## [1] "MS:4000160"
## attr(,"precursorIntensityQuartiles")
## [1] "MS:4000116"
## attr(,"precursorIntensityMean")
## [1] "MS:4000117"
## attr(,"precursorIntensitySd")
## [1] "MS:4000118"
## attr(,"msSignal10xChange")
## [1] "MS:4000097"
## attr(,"ratioCharge1over2")
## [1] "MS:4000167"
## attr(,"ratioCharge3over2")
## [1] "MS:4000169"
## attr(,"ratioCharge4over2")
## [1] "MS:4000171"
## attr(,"meanCharge")
## [1] "MS:4000173"
## attr(,"medianCharge")
## [1] "MS:4000175"
## attr(,"relativeTo")
## [1] "Q1"
## attr(,"msLevel")
## [1] 1metrics_sps_previous <- calculateMetrics(object = sps_comb_subset,
metrics = qualityMetrics(sps_comb_subset), filterEmptySpectra = FALSE,
relativeTo = "previous", msLevel = 1L)
metrics_sps_previous## chromatographyDuration ticQuartersRtFraction.0%
## Sample.1_RPLC 18.214 0
## Sample.1_HILIC 16.000 0
## ticQuartersRtFraction.25% ticQuartersRtFraction.50%
## Sample.1_RPLC 0.08098166 0.08098166
## Sample.1_HILIC 0.34375000 0.34375000
## ticQuartersRtFraction.75% ticQuartersRtFraction.100%
## Sample.1_RPLC 0.1495004 1
## Sample.1_HILIC 0.5375000 1
## rtOverMsQuarters.Quarter1 rtOverMsQuarters.Quarter2
## Sample.1_RPLC 0.02893379 0.08806413
## Sample.1_HILIC 0.15625000 0.47500000
## rtOverMsQuarters.Quarter3 rtOverMsQuarters.Quarter4
## Sample.1_RPLC 0.3102009 1
## Sample.1_HILIC 0.6216875 1
## ticQuartileToQuartileLogRatio.Q2/Q1
## Sample.1_RPLC NaN
## Sample.1_HILIC -Inf
## ticQuartileToQuartileLogRatio.Q3/Q2
## Sample.1_RPLC 5.831233
## Sample.1_HILIC Inf
## ticQuartileToQuartileLogRatio.Q4/Q3 numberSpectra
## Sample.1_RPLC 8.915513 190
## Sample.1_HILIC 8.982638 165
## numberEmptyScans medianPrecursorMz rtIqr rtIqrRate
## Sample.1_RPLC 0 198.05 5.10125 18.42686
## Sample.1_HILIC 0 179.10 7.44700 11.14543
## areaUnderTic areaUnderTicRtQuantiles.25%
## Sample.1_RPLC 655624525 25612593.2
## Sample.1_HILIC 149945016 927493.9
## areaUnderTicRtQuantiles.50% areaUnderTicRtQuantiles.75%
## Sample.1_RPLC 389734297 233673968
## Sample.1_HILIC 100961764 40996727
## areaUnderTicRtQuantiles.100% extentIdentifiedPrecursorIntensity
## Sample.1_RPLC 5692460 36467.884
## Sample.1_HILIC 5460065 8713.906
## medianTicRtIqr medianTicOfRtRange mzAcquisitionRange.min
## Sample.1_RPLC 7496.006 13858.935 60.1
## Sample.1_HILIC 2195.400 2086.417 73.0
## mzAcquisitionRange.max rtAcquisitionRange.min
## Sample.1_RPLC 1377.6 0.986
## Sample.1_HILIC 784.1 1.100
## rtAcquisitionRange.max precursorIntensityRange.min
## Sample.1_RPLC 19.2 100.6492
## Sample.1_HILIC 17.1 100.0895
## precursorIntensityRange.max precursorIntensityQuartiles.Q1
## Sample.1_RPLC 349282909 1667.3911
## Sample.1_HILIC 92021751 300.5038
## precursorIntensityQuartiles.Q2 precursorIntensityQuartiles.Q3
## Sample.1_RPLC 6746.195 75003.90
## Sample.1_HILIC 2304.383 33382.25
## precursorIntensityMean precursorIntensitySd msSignal10xChange
## Sample.1_RPLC 3450655.4 26563228 56
## Sample.1_HILIC 908757.7 7729451 47
## ratioCharge1over2 ratioCharge3over2 ratioCharge4over2 meanCharge
## Sample.1_RPLC NaN NaN NaN NaN
## Sample.1_HILIC NaN NaN NaN NaN
## medianCharge
## Sample.1_RPLC NA
## Sample.1_HILIC NA
## attr(,"chromatographyDuration")
## [1] "MS:4000053"
## attr(,"names1")
## [1] "Q1"
## attr(,"names2")
## [1] "Q2"
## attr(,"names3")
## [1] "Q3"
## attr(,"names4")
## [1] "100%"
## attr(,"names5")
## [1] "100%"
## attr(,"ticQuartersRtFraction")
## [1] "MS:4000054"
## attr(,"rtOverMsQuarters")
## [1] "MS:4000055"
## attr(,"ticQuartileToQuartileLogRatio")
## [1] "MS:4000057"
## attr(,"numberSpectra")
## [1] "MS:4000059"
## attr(,"numberEmptyScans")
## [1] "MS:4000099"
## attr(,"areaUnderTic")
## [1] "MS:4000155"
## attr(,"areaUnderTicRtQuantiles")
## [1] "MS:4000156"
## attr(,"mzAcquisitionRange")
## [1] "MS:4000069"
## attr(,"rtAcquisitionRange")
## [1] "MS:4000070"
## attr(,"precursorIntensityRange")
## [1] "MS:4000160"
## attr(,"precursorIntensityQuartiles")
## [1] "MS:4000116"
## attr(,"precursorIntensityMean")
## [1] "MS:4000117"
## attr(,"precursorIntensitySd")
## [1] "MS:4000118"
## attr(,"msSignal10xChange")
## [1] "MS:4000097"
## attr(,"ratioCharge1over2")
## [1] "MS:4000167"
## attr(,"ratioCharge3over2")
## [1] "MS:4000169"
## attr(,"ratioCharge4over2")
## [1] "MS:4000171"
## attr(,"meanCharge")
## [1] "MS:4000173"
## attr(,"medianCharge")
## [1] "MS:4000175"
## attr(,"relativeTo")
## [1] "previous"
## attr(,"msLevel")
## [1] 1Alternatively, an MsExperiment object might be passed to
calculateMetrics. The function will iterate over the samples (referring
to rows in sampleData(msexp))) and calculate the quality metrics on the
corresponding Spectras.
mzQC-formatBy default, a data.frame object containing the metric values as entries
are returned by the the function calculateMetrics. Alternatively, the
function also allows the user to export the metrics in a format defined by
the rmzqc package by setting the argument format to "mzQC" (default:
format = "data.frame"). In that case, only the metrics that comply to the
mzQC specification will be written to the returned object.
The object can be exported and validated using the functionality of the rmzqc
package (see the documentation of rmzqc for further information).
There are in total 541 samples
respectively in the objects msexp_rplc and msexp_hilic. To improve
the visualization and interpretability, we will only calculate the metrics
from the first 20 of these samples.
In this example here, we will remove zero-length and zero-intensity entries
prior to calculating the metrics. To do this, we set the filterEmptySpectra
argument to TRUE within the calculateMetrics function.
## subset the MsExperiment objects
msexp_rplc_subset <- msexp_rplc[1:20]
msexp_hilic_subset <- msexp_hilic[1:20]
## define metrics
metrics_sps <- c("chromatographyDuration", "ticQuartersRtFraction", "rtOverMsQuarters",
"ticQuartileToQuartileLogRatio", "numberSpectra", "medianPrecursorMz",
"rtIqr", "rtIqrRate", "areaUnderTic")
## for RPLC-derived MsExperiment
metrics_rplc_msexp <- calculateMetrics(object = msexp_rplc_subset,
metrics = qualityMetrics(msexp_rplc_subset), filterEmptySpectra = TRUE,
relativeTo = "Q1", msLevel = 1L)
## for HILIC-derived MsExperiment
metrics_hilic_msexp <- calculateMetrics(object = msexp_hilic_subset,
metrics = qualityMetrics(msexp_hilic_subset), filterEmptySpectra = TRUE,
relativeTo = "Q1", msLevel = 1L)When passing an MsExperiment object to calculateMetrics a data.frame
object is returned with the samples (derived from the rownames of
sampleData(msexp)) in the rows and the metrics in columns.
We will show here the objects metrics_rplc_msexp and metrics_hilic_msexp
## [1] "metrics_rplc_msexp"## [1] "metrics_hilic_msexp"The quality metrics can be most easily compared when graphically visualized.
The MsQuality package offers the possibility to graphically display the
metrics using the plotMetric and shinyMsQuality functions. The
plotMetric function will create one plot based on a single metric.
shinyMsQuality, on the other hand, opens a shiny application that allows
to browse through all the metrics stored in the object.
As a way of example, we will plot here the number of features. A high number of missing features might indicate low data quality, however, also different sample types might exhibit contrasting number of detected features. As a general rule, only samples of the same type should be compared to adjust for sample type-specific effects.
metrics_msexp <- rbind(metrics_rplc_msexp, metrics_hilic_msexp)
plotMetric(qc = metrics_msexp, metric = "numberSpectra")Similarly, we are able to display the area under the TIC for the retention time quantiles. This plot gives information on the perceived signal (TIC) for the differnt retention time quantiles and could indicate drifts or interruptions of sensitivity during the run.
plotMetric(qc = metrics_msexp, metric = "ticQuartileToQuartileLogRatio")Alternatively, to browse through all metrics that were calculated in an
interactive way, we can use the shinyMsQuality function.
shinyMsQuality(qc = metrics_msexp)All software and respective versions to build this vignette are listed here:
## 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] MsExperiment_1.4.0 Spectra_1.12.0 ProtGenerics_1.34.0
## [4] BiocParallel_1.36.0 S4Vectors_0.40.2 BiocGenerics_0.48.1
## [7] MsQuality_1.2.1 knitr_1.45 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 testthat_3.2.1
## [3] rlang_1.1.3 magrittr_2.0.3
## [5] shinydashboard_0.7.2 clue_0.3-65
## [7] matrixStats_1.2.0 compiler_4.3.2
## [9] vctrs_0.6.5 stringr_1.5.1
## [11] pkgconfig_2.0.3 MetaboCoreUtils_1.10.0
## [13] crayon_1.5.2 fastmap_1.1.1
## [15] XVector_0.42.0 ellipsis_0.3.2
## [17] labeling_0.4.3 utf8_1.2.4
## [19] promises_1.2.1 rmarkdown_2.25
## [21] purrr_1.0.2 xfun_0.42
## [23] MultiAssayExperiment_1.28.0 zlibbioc_1.48.0
## [25] cachem_1.0.8 GenomeInfoDb_1.38.6
## [27] jsonlite_1.8.8 later_1.3.2
## [29] DelayedArray_0.28.0 parallel_4.3.2
## [31] cluster_2.1.6 R6_2.5.1
## [33] RColorBrewer_1.1-3 stringi_1.8.3
## [35] bslib_0.6.1 brio_1.1.4
## [37] GenomicRanges_1.54.1 jquerylib_0.1.4
## [39] Rcpp_1.0.12 bookdown_0.37
## [41] SummarizedExperiment_1.32.0 IRanges_2.36.0
## [43] httpuv_1.6.14 Matrix_1.6-5
## [45] igraph_2.0.2 tidyselect_1.2.0
## [47] abind_1.4-5 yaml_2.3.8
## [49] codetools_0.2-19 curl_5.2.0
## [51] lattice_0.22-5 tibble_3.2.1
## [53] withr_3.0.0 Biobase_2.62.0
## [55] shiny_1.8.0 evaluate_0.23
## [57] ontologyIndex_2.11 pillar_1.9.0
## [59] BiocManager_1.30.22 MatrixGenerics_1.14.0
## [61] plotly_4.10.4 generics_0.1.3
## [63] RCurl_1.98-1.14 ggplot2_3.4.4
## [65] munsell_0.5.0 scales_1.3.0
## [67] R6P_0.3.0 xtable_1.8-4
## [69] glue_1.7.0 lazyeval_0.2.2
## [71] tools_4.3.2 data.table_1.15.0
## [73] QFeatures_1.12.0 fs_1.6.3
## [75] grid_4.3.2 jsonvalidate_1.3.2
## [77] tidyr_1.3.1 crosstalk_1.2.1
## [79] MsCoreUtils_1.14.1 msdata_0.42.0
## [81] colorspace_2.1-0 GenomeInfoDbData_1.2.11
## [83] cli_3.6.2 fansi_1.0.6
## [85] S4Arrays_1.2.0 viridisLite_0.4.2
## [87] dplyr_1.1.4 AnnotationFilter_1.26.0
## [89] gtable_0.3.4 sass_0.4.8
## [91] digest_0.6.34 SparseArray_1.2.4
## [93] htmlwidgets_1.6.4 htmltools_0.5.7
## [95] lifecycle_1.0.4 httr_1.4.7
## [97] mime_0.12 rmzqc_0.5.3
## [99] MASS_7.3-60.0.1Lee, H.-J., D. M. Kremer, P. Sajjakulnukit, L. Zhang, and C. A. Lyssiotis. 2019. “A Large-Scale Analysis of Targeted Metabolomics Data from Heterogeneous Biological Samples Provides Insights into Metabolite Dynamics.” Metabolomics, 103. https://doi.org/10.1007/s11306-019-1564-8.