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Package: Spectra
Authors: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (https://orcid.org/0000-0002-1520-2268), Johannes Rainer [aut] (https://orcid.org/0000-0002-6977-7147), Sebastian Gibb [aut] (https://orcid.org/0000-0001-7406-4443), Jan Stanstrup [ctb] (https://orcid.org/0000-0003-0541-7369)
Last modified: 2022-09-02 05:20:51
Compiled: Fri Sep 2 05:41:41 2022

Introduction

The Spectra package provides a scalable and flexible infrastructure to represent, retrieve and handle mass spectrometry (MS) data. The Spectra object provides the user with a single standardized interface to access and manipulate MS data while supporting, through the concept of exchangeable backends, a large variety of different ways to store and retrieve mass spectrometry data. Such backends range from mzML/mzXML/CDF files, simple flat files, or database systems.

This vignette provides general examples and descriptions for the Spectra package. Additional information and tutorials are available, such as SpectraTutorials, MetaboAnnotationTutorials, or also in (Rainer et al. 2022).

Installation

The package can be installed with the BiocManager package. To install BiocManager use install.packages("BiocManager") and, after that, BiocManager::install("Spectra") to install Spectra.

General usage

Mass spectrometry data in Spectra objects can be thought of as a list of individual spectra, with each spectrum having a set of variables associated with it. Besides core spectra variables (such as MS level or retention time) an arbitrary number of optional variables can be assigned to a spectrum. The core spectra variables all have their own accessor method and it is guaranteed that a value is returned by it (or NA if the information is not available). The core variables and their data type are (alphabetically ordered):

  • acquisitionNum integer(1): the index of acquisition of a spectrum during a MS run.
  • centroided logical(1): whether the spectrum is in profile or centroid mode.
  • collisionEnergy numeric(1): collision energy used to create an MSn spectrum.
  • dataOrigin character(1): the origin of the spectrum’s data, e.g. the mzML file from which it was read.
  • dataStorage character(1): the (current) storage location of the spectrum data. This value depends on the backend used to handle and provide the data. For an in-memory backend like the MsBackendDataFrame this will be "<memory>", for an on-disk backend such as the MsBackendHdf5Peaks it will be the name of the HDF5 file where the spectrum’s peak data is stored.
  • intensity numeric: intensity values for the spectrum’s peaks.
  • isolationWindowLowerMz numeric(1): lower m/z for the isolation window in which the (MSn) spectrum was measured.
  • isolationWindowTargetMz numeric(1): the target m/z for the isolation window in which the (MSn) spectrum was measured.
  • isolationWindowUpperMz numeric(1): upper m/z for the isolation window in which the (MSn) spectrum was measured.
  • msLevel integer(1): the MS level of the spectrum.
  • mz numeric: the m/z values for the spectrum’s peaks.
  • polarity integer(1): the polarity of the spectrum (0 and 1 representing negative and positive polarity, respectively).
  • precScanNum integer(1): the scan (acquisition) number of the precursor for an MSn spectrum.
  • precursorCharge integer(1): the charge of the precursor of an MSn spectrum.
  • precursorIntensity numeric(1): the intensity of the precursor of an MSn spectrum.
  • precursorMz numeric(1): the m/z of the precursor of an MSn spectrum.
  • rtime numeric(1): the retention time of a spectrum.
  • scanIndex integer(1): the index of a spectrum within a (raw) file.
  • smoothed logical(1): whether the spectrum was smoothed.

For details on the individual variables and their getter/setter function see the help for Spectra (?Spectra). Also note that these variables are suggested, but not required to characterize a spectrum. Also, some only make sense for MSn, but not for MS1 spectra.

Creating Spectra objects

The simplest way to create a Spectra object is by defining a DataFrame with the corresponding spectra data (using the corresponding spectra variable names as column names) and passing that to the Spectra constructor function. Below we create such an object for a set of 3 spectra providing their MS level, polarity but also additional annotations such as their ID in HMDB (human metabolome database) and their name. The m/z and intensity values for each spectrum have to be provided as a list of numeric values.

library(Spectra)

spd <- DataFrame(
    msLevel = c(2L, 2L, 2L),
    polarity = c(1L, 1L, 1L),
    id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
    name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))

## Assign m/z and intensity values.
spd$mz <- list(
    c(109.2, 124.2, 124.5, 170.16, 170.52),
    c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
    c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
      111.0551, 123.0429, 138.0662, 195.0876))
spd$intensity <- list(
    c(3.407, 47.494, 3.094, 100.0, 13.240),
    c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
    c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))

sps <- Spectra(spd)
sps
## MSn data (Spectra) with 3 spectra in a MsBackendDataFrame backend:
##     msLevel     rtime scanIndex
##   <integer> <numeric> <integer>
## 1         2        NA        NA
## 2         2        NA        NA
## 3         2        NA        NA
##  ... 18 more variables/columns.

Alternatively, it is possible to import spectra data from mass spectrometry raw files in mzML/mzXML or CDF format. Below we create a Spectra object from two mzML files and define to use a MsBackendMzR backend to store the data (note that this requires the mzR package to be installed). This backend, specifically designed for raw MS data, keeps only a subset of spectra variables in memory while reading the m/z and intensity values from the original data files only on demand. See section Backends for more details on backends and their properties.

fls <- dir(system.file("sciex", package = "msdata"), full.names = TRUE)
sps_sciex <- Spectra(fls, backend = MsBackendMzR())
sps_sciex
## MSn data (Spectra) with 1862 spectra in a MsBackendMzR backend:
##        msLevel     rtime scanIndex
##      <integer> <numeric> <integer>
## 1            1     0.280         1
## 2            1     0.559         2
## 3            1     0.838         3
## 4            1     1.117         4
## 5            1     1.396         5
## ...        ...       ...       ...
## 1858         1   258.636       927
## 1859         1   258.915       928
## 1860         1   259.194       929
## 1861         1   259.473       930
## 1862         1   259.752       931
##  ... 33 more variables/columns.
## 
## file(s):
## 20171016_POOL_POS_1_105-134.mzML
## 20171016_POOL_POS_3_105-134.mzML

The Spectra object sps_sciex allows now to access spectra data from 1862 MS1 spectra and uses MsBackendMzR as backend (the Spectra object sps created in the previous code block uses the default MsBackendDataFrame).

Accessing spectrum data

As detailed above Spectra objects can contain an arbitrary number of properties of a spectrum (so called spectra variables). The available variables can be listed with the spectraVariables method:

##  [1] "msLevel"                 "rtime"                  
##  [3] "acquisitionNum"          "scanIndex"              
##  [5] "dataStorage"             "dataOrigin"             
##  [7] "centroided"              "smoothed"               
##  [9] "polarity"                "precScanNum"            
## [11] "precursorMz"             "precursorIntensity"     
## [13] "precursorCharge"         "collisionEnergy"        
## [15] "isolationWindowLowerMz"  "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz"  "id"                     
## [19] "name"
spectraVariables(sps_sciex)
##  [1] "msLevel"                  "rtime"                   
##  [3] "acquisitionNum"           "scanIndex"               
##  [5] "dataStorage"              "dataOrigin"              
##  [7] "centroided"               "smoothed"                
##  [9] "polarity"                 "precScanNum"             
## [11] "precursorMz"              "precursorIntensity"      
## [13] "precursorCharge"          "collisionEnergy"         
## [15] "isolationWindowLowerMz"   "isolationWindowTargetMz" 
## [17] "isolationWindowUpperMz"   "peaksCount"              
## [19] "totIonCurrent"            "basePeakMZ"              
## [21] "basePeakIntensity"        "ionisationEnergy"        
## [23] "lowMZ"                    "highMZ"                  
## [25] "mergedScan"               "mergedResultScanNum"     
## [27] "mergedResultStartScanNum" "mergedResultEndScanNum"  
## [29] "injectionTime"            "filterString"            
## [31] "spectrumId"               "ionMobilityDriftTime"    
## [33] "scanWindowLowerLimit"     "scanWindowUpperLimit"

The two Spectra contain a different set of variables: besides "msLevel", "polarity", "id" and "name", that were specified for the Spectra object sps, it contains more variables such as "rtime", "acquisitionNum" and "scanIndex". These are part of the core variables defining a spectrum and for all of these accessor methods exist. Below we use msLevel and rtime to access the MS levels and retention times for the spectra in sps.

msLevel(sps)
## [1] 2 2 2
rtime(sps)
## [1] NA NA NA

We did not specify retention times for the spectra in sps thus NA is returned for them. The Spectra object sps_sciex contains many more variables, all of which were extracted from the mzML files. Below we extract the retention times for the first spectra in the object.

head(rtime(sps_sciex))
## [1] 0.280 0.559 0.838 1.117 1.396 1.675

Note that in addition to the accessor functions it is also possible to use $ to extract a specific spectra variable. To extract the name of the compounds in sps we can use sps$name, or, to extract the MS levels sps$msLevel.

sps$name
## [1] "1-Methylhistidine" "1-Methylhistidine" "Caffeine"
sps$msLevel
## [1] 2 2 2

We could also replace specific spectra variables using either the dedicated method or $. Below we specify that all spectra in sps represent centroided data.

sps$centroided <- TRUE

centroided(sps)
## [1] TRUE TRUE TRUE

The $ operator can also be used to add arbitrary new spectra variables to a Spectra object. Below we add the SPLASH key to each of the spectra.

sps$splash <- c(
    "splash10-00di-0900000000-037d24a7d65676b7e356",
    "splash10-00di-0900000000-03e99316bd6c098f5d11",
    "splash10-000i-0900000000-9af60e39c843cb715435")

This new spectra variable will now be listed as an additional variable in the result of the spectraVariables function and we can directly access its content with sps$splash.

Each spectrum can have a different number of mass peaks, each consisting of a mass-to-charge (m/z) and associated intensity value. These can be extracted with the mz or intensity functions, each of which return a list of numeric values.

mz(sps)
## NumericList of length 3
## [[1]] 109.2 124.2 124.5 170.16 170.52
## [[2]] 83.1 96.12 97.14 109.14 124.08 125.1 170.16
## [[3]] 56.0494 69.0447 83.0603 109.0395 110.0712 111.0551 123.0429 138.0662 195.0876
## NumericList of length 3
## [[1]] 3.407 47.494 3.094 100 13.24
## [[2]] 6.685 4.381 3.022 16.708 100 4.565 40.643
## [[3]] 0.459 2.585 2.446 0.508 8.968 0.524 0.974 100 40.994

Peak data can also be extracted with the peaksData function that returns a list of numerical matrices with peak variables such as m/z and intensity values. Which peak variables are available in a Spectra object can be determined with the peaksVariables function.

## [1] "mz"        "intensity"

These can be passed to the peaksData function with parameter columns to extract the peak variables of interest. By default peaksData extracts m/z and intensity values.

pks <- peaksData(sps)
pks[[1]]
##          mz intensity
## [1,] 109.20     3.407
## [2,] 124.20    47.494
## [3,] 124.50     3.094
## [4,] 170.16   100.000
## [5,] 170.52    13.240

Note that we would get the same result by using the as method to coerce a Spectra object to a list or SimpleList:

as(sps, "SimpleList")
## List of length 3

The spectraData function returns a DataFrame with the full data for each spectrum (except m/z and intensity values), or with selected spectra variables (which can be specified with the columns parameter). Below we extract the spectra data for variables "msLevel", "id" and "name".

spectraData(sps, columns = c("msLevel", "id", "name"))
## DataFrame with 3 rows and 3 columns
##     msLevel          id              name
##   <integer> <character>       <character>
## 1         2 HMDB0000001 1-Methylhistidine
## 2         2 HMDB0000001 1-Methylhistidine
## 3         2 HMDB0001847          Caffeine

Spectra are one-dimensional objects storing spectra, even from different files or samples, in a single list. Specific variables have thus to be used to define the originating file from which they were extracted or the sample in which they were measured. The data origin of each spectrum can be extracted with the dataOrigin function. For sps, the Spectra created from a DataFrame, this will be NA because we did not specify the data origin:

## [1] NA NA NA

dataOrigin for sps_sciex, the Spectra which was initialized with data from mzML files, in contrast, returns the originating file names:

head(basename(dataOrigin(sps_sciex)))
## [1] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"
## [3] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"
## [5] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"

The current data storage location of a spectrum can be retrieved with the dataStorage variable, which will return an arbitrary string for Spectra that use an in-memory backend or the file where the data is stored for on-disk backends:

## [1] "<memory>" "<memory>" "<memory>"
## [1] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"
## [3] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"
## [5] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"

Filtering, subsetting and merging

Apart from classical subsetting operations such as [ and split, a set of filter functions are defined for Spectra objects (for detailed help please see the ?Spectra help):

  • filterAcquisitionNum: retain spectra with certain acquisition numbers.
  • filterDataOrigin: subset to spectra from specific origins.
  • filterDataStorage: subset to spectra from certain data storage files.
  • filterEmptySpectra: remove spectra without mass peaks.
  • filterMzRange: subset spectra keeping only peaks with an m/z within the provided m/z range.
  • filterMzValues: subset spectra keeping or removing peaks matching provided m/z value(s).
  • filterIsolationWindow: keep spectra with the provided mz in their isolation window (m/z range).
  • filterMsLevel: filter by MS level.
  • filterPolarity: filter by polarity.
  • filterPrecursorMzRange: retain (MSn) spectra with a precursor m/z within the provided m/z range.
  • filterPrecursorMzValues: retain (MSn) spectra with precursor m/z value matching the provided value(s) considering also a tolerance and ppm.
  • filterPrecursorCharge: retain (MSn) spectra with speified precursor charge(s).
  • filterPrecursorScan: retain (parent and children) scans of an acquisition number.
  • filterRt: filter based on retention time ranges.

In the example below we select all spectra measured in the second mzML file and subsequently filter them to retain spectra measured between 175 and 189 seconds in the measurement run.

fls <- unique(dataOrigin(sps_sciex))
file_2 <- filterDataOrigin(sps_sciex, dataOrigin = fls[2])
length(file_2)
## [1] 931
sps_sub <- filterRt(file_2, rt = c(175, 189))
length(sps_sub)
## [1] 50

In addition, Spectra support also subsetting with [. Below we perform the filtering above with [ -based subsetting.

sps_sciex[sps_sciex$dataOrigin == fls[2] &
          sps_sciex$rtime >= 175 &
          sps_sciex$rtime <= 189]
## MSn data (Spectra) with 50 spectra in a MsBackendMzR backend:
##       msLevel     rtime scanIndex
##     <integer> <numeric> <integer>
## 1           1   175.212       628
## 2           1   175.491       629
## 3           1   175.770       630
## 4           1   176.049       631
## 5           1   176.328       632
## ...       ...       ...       ...
## 46          1   187.768       673
## 47          1   188.047       674
## 48          1   188.326       675
## 49          1   188.605       676
## 50          1   188.884       677
##  ... 33 more variables/columns.
## 
## file(s):
## 20171016_POOL_POS_3_105-134.mzML

The equivalent using filter function is shown below, with the added benefit that the filtering is recorded in the processing slot.

sps_sciex |>
    filterDataOrigin(fls[2]) |>
    filterRt(c(175, 189))
## MSn data (Spectra) with 50 spectra in a MsBackendMzR backend:
##       msLevel     rtime scanIndex
##     <integer> <numeric> <integer>
## 1           1   175.212       628
## 2           1   175.491       629
## 3           1   175.770       630
## 4           1   176.049       631
## 5           1   176.328       632
## ...       ...       ...       ...
## 46          1   187.768       673
## 47          1   188.047       674
## 48          1   188.326       675
## 49          1   188.605       676
## 50          1   188.884       677
##  ... 33 more variables/columns.
## 
## file(s):
## 20171016_POOL_POS_3_105-134.mzML
## Processing:
##  Filter: select data origin(s) /__w/_temp/Library/msdata/sciex/20171016_POOL_POS_3_105-134.mzML [Fri Sep  2 05:41:45 2022]
##  Filter: select retention time [175..189] on MS level(s) 1 [Fri Sep  2 05:41:45 2022]

Note that the use of the filter functions might be more efficient for some backends, depending on their implementation, (e.g. database-based backends could translate the filter function into a SQL condition to perform the subsetting already within the database).

Multiple Spectra objects can also be combined into a single Spectra with the c or the concatenateSpectra function. The resulting Spectra object will contain an union of the spectra variables of the individual objects. Below we combine the Spectra object sps with an additional object containing another MS2 spectrum for Caffeine.

caf_df <- DataFrame(msLevel = 2L, name = "Caffeine",
                    id = "HMDB0001847",
                    instrument = "Agilent 1200 RRLC; Agilent 6520 QTOF",
                    splash = "splash10-0002-0900000000-413259091ba7edc46b87",
                    centroided = TRUE)
caf_df$mz <- list(c(110.0710, 138.0655, 138.1057, 138.1742, 195.9864))
caf_df$intensity <- list(c(3.837, 32.341, 0.84, 0.534, 100))

caf <- Spectra(caf_df)

Next we combine the two objects.

sps <- concatenateSpectra(sps, caf)
sps
## MSn data (Spectra) with 4 spectra in a MsBackendDataFrame backend:
##     msLevel     rtime scanIndex
##   <integer> <numeric> <integer>
## 1         2        NA        NA
## 2         2        NA        NA
## 3         2        NA        NA
## 4         2        NA        NA
##  ... 20 more variables/columns.
## Processing:
##  Merge 2 Spectra into one [Fri Sep  2 05:41:45 2022]

The resulting object contains now the data for all 4 MS2 spectra and an union of all spectra variables from both objects.

##  [1] "msLevel"                 "rtime"                  
##  [3] "acquisitionNum"          "scanIndex"              
##  [5] "dataStorage"             "dataOrigin"             
##  [7] "centroided"              "smoothed"               
##  [9] "polarity"                "precScanNum"            
## [11] "precursorMz"             "precursorIntensity"     
## [13] "precursorCharge"         "collisionEnergy"        
## [15] "isolationWindowLowerMz"  "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz"  "id"                     
## [19] "name"                    "splash"                 
## [21] "instrument"

The second object had an additional spectra variable instrument that was not present in sps and all the spectra in this object will thus get a value of NA for this variable.

sps$instrument
## [1] NA                                    
## [2] NA                                    
## [3] NA                                    
## [4] "Agilent 1200 RRLC; Agilent 6520 QTOF"

Sometimes not all spectra variables might be required (e.g. also because many of them are empty). This might be specifically interesting also for Spectra containing the data from very large experiments, because it can significantly reduce the object’s size in memory. In such cases the selectSpectraVariables function can be used to retain only specified spectra variables.

Data manipulations

Some analyses require manipulation of the mass peak data (i.e. the m/z and/or intensity values). One example would be to remove all peaks from a spectrum that have an intensity lower than a certain threshold. Below we perform such an operation with the replaceIntensitiesBelow function to replace peak intensities below 10 in each spectrum in sps with a value of 0.

sps_rep <- replaceIntensitiesBelow(sps, threshold = 10, value = 0)

As a result intensities below 10 were set to 0 for all peaks.

intensity(sps_rep)
## NumericList of length 4
## [[1]] 0 47.494 0 100 13.24
## [[2]] 0 0 0 16.708 100 0 40.643
## [[3]] 0 0 0 0 0 0 0 100 40.994
## [[4]] 0 32.341 0 0 100

Zero-intensity peaks (and peaks with missing intensities) can then be removed with the filterIntensity function specifying a lower required intensity level or optionally also an upper intensity limit.

sps_rep <- filterIntensity(sps_rep, intensity = c(0.1, Inf))
intensity(sps_rep)
## NumericList of length 4
## [[1]] 47.494 100 13.24
## [[2]] 16.708 100 40.643
## [[3]] 100 40.994
## [[4]] 32.341 100

The filterIntensity supports also a user-provided function to be passed with parameter intensity which would allow e.g. to remove peaks smaller than the median peak intensity of a spectrum. See examples in the ?filterIntensity help page for details.

Note that any data manipulations on Spectra objects are not immediately applied to the peak data. They are added to a so called processing queue which is applied each time peak data is accessed (with the peaksData, mz or intensity functions). Thanks to this processing queue data manipulation operations are also possible for read-only backends (e.g. mzML-file based backends or database-based backends). The information about the number of such processing steps can be seen below (next to Lazy evaluation queue).

sps_rep
## MSn data (Spectra) with 4 spectra in a MsBackendDataFrame backend:
##     msLevel     rtime scanIndex
##   <integer> <numeric> <integer>
## 1         2        NA        NA
## 2         2        NA        NA
## 3         2        NA        NA
## 4         2        NA        NA
##  ... 20 more variables/columns.
## Lazy evaluation queue: 2 processing step(s)
## Processing:
##  Merge 2 Spectra into one [Fri Sep  2 05:41:45 2022]
##  Signal <= 10 in MS level(s) 2 set to 0 [Fri Sep  2 05:41:46 2022]
##  Remove peaks with intensities outside [0.1, Inf] in spectra of MS level(s) 2. [Fri Sep  2 05:41:46 2022]

It is possible to add also custom functions to the processing queue of a Spectra object. Such a function must take a peaks matrix as its first argument, have ... in the function definition and must return a peaks matrix (a peaks matrix is a numeric two-column matrix with the first column containing the peaks’ m/z values and the second the corresponding intensities). Below we define a function that divides the intensities of each peak by a value which can be passed with argument y.

## Define a function that takes a matrix as input, divides the second
## column by parameter y and returns it. Note that ... is required in
## the function's definition.
divide_intensities <- function(x, y, ...) {
    x[, 2] <- x[, 2] / y
    x
}

## Add the function to the procesing queue
sps_2 <- addProcessing(sps_rep, divide_intensities, y = 2)
sps_2
## MSn data (Spectra) with 4 spectra in a MsBackendDataFrame backend:
##     msLevel     rtime scanIndex
##   <integer> <numeric> <integer>
## 1         2        NA        NA
## 2         2        NA        NA
## 3         2        NA        NA
## 4         2        NA        NA
##  ... 20 more variables/columns.
## Lazy evaluation queue: 3 processing step(s)
## Processing:
##  Merge 2 Spectra into one [Fri Sep  2 05:41:45 2022]
##  Signal <= 10 in MS level(s) 2 set to 0 [Fri Sep  2 05:41:46 2022]
##  Remove peaks with intensities outside [0.1, Inf] in spectra of MS level(s) 2. [Fri Sep  2 05:41:46 2022]

Object sps_2 has now 3 processing steps in its lazy evaluation queue. Calling intensity on this object will now return intensities that are half of the intensities of the original objects sps.

intensity(sps_2)
## NumericList of length 4
## [[1]] 23.747 50 6.62
## [[2]] 8.354 50 20.3215
## [[3]] 50 20.497
## [[4]] 16.1705 50
intensity(sps_rep)
## NumericList of length 4
## [[1]] 47.494 100 13.24
## [[2]] 16.708 100 40.643
## [[3]] 100 40.994
## [[4]] 32.341 100

Alternatively we could define a function that returns the maximum peak from each spectrum (note: we use the unname function to remove any names from the results):

max_peak <- function(x, ...) {
    unname(x[which.max(x[, 2]), , drop = FALSE])
}

sps_2 <- addProcessing(sps_rep, max_peak)
lengths(sps_2)
## [1] 1 1 1 1
intensity(sps_2)
## NumericList of length 4
## [[1]] 100
## [[2]] 100
## [[3]] 100
## [[4]] 100

Each spectrum in sps_2 thus contains only a single peak. The parameter spectraVariables of the addProcessing function allows in addition to define spectra variables that should be passed (in addition to the peaks matrix) to the user-provided function. This would enable for example to calculate neutral loss spectra from a Spectra by subtracting the precursor m/z from each m/z of a spectrum (note that there would also be a dedicated neutralLoss function to perform this operation more efficiently). Our tool example does not have precursor m/z values defined, thus we first set them to arbitrary values. Then we define a function neutral_loss that calculates the difference between the precursor m/z and the fragment peak’s m/z. In addition we need to ensure the peaks in the resulting spectra are ordered by (the delta) m/z values. Note that, in order to be able to access the precursor m/z of the spectrum within our function, we have to add a parameter to the function that has the same name as the spectrum variable we want to access (in our case precursorMz).

sps_rep$precursorMz <- c(150, 20, 30, 40)

neutral_loss <- function(x, precursorMz, ...) {
    x[, "mz"] <- precursorMz - x[, "mz"]
    x[order(x[, "mz"]), , drop = FALSE]
}

We have then to call addProcessing with spectraVariables = "precursorMz" to specify that this spectra variable is passed along to our function.

sps_3 <- addProcessing(sps_rep, neutral_loss, spectraVariables = "precursorMz")
mz(sps_rep)
## NumericList of length 4
## [[1]] 124.2 170.16 170.52
## [[2]] 109.14 124.08 170.16
## [[3]] 138.0662 195.0876
## [[4]] 138.0655 195.9864
mz(sps_3)
## NumericList of length 4
## [[1]] -20.52 -20.16 25.8
## [[2]] -150.16 -104.08 -89.14
## [[3]] -165.0876 -108.0662
## [[4]] -155.9864 -98.0655

As we can see, the precursor m/z was subtracted from each m/z of the respective spectrum. A better version of the function, that only calculates neutral loss spectra for MS level 2 spectra would be the neutral_loss function below. Since we are accessing also the spectrum’s MS level we have to call addProcessing adding also the spectra variable msLevel to the spectraVariables parameter. Note however that the msLevel spectra variable is by default renamed to spectrumMsLevel prior passing it to the function. We have thus to use a parameter called spectrumMsLevel in the neutral_loss function instead of msLevel.

neutral_loss <- function(x, spectrumMsLevel, precursorMz, ...) {
    if (spectrumMsLevel == 2L) {
        x[, "mz"] <- precursorMz - x[, "mz"]
        x <- x[order(x[, "mz"]), , drop = FALSE]
    }
    x
}
sps_3 <- addProcessing(sps_rep, neutral_loss,
                       spectraVariables = c("msLevel", "precursorMz"))
mz(sps_3)
## NumericList of length 4
## [[1]] -20.52 -20.16 25.8
## [[2]] -150.16 -104.08 -89.14
## [[3]] -165.0876 -108.0662
## [[4]] -155.9864 -98.0655

Using the same concept it would also be possible to provide any spectrum-specific user-defined value to the processing function. This variable could simply be added first as a new spectra variable to the Spectra object and then this variable could be passed along to the function in the same way we passed the precursor m/z to our function above.

Since all data manipulations above did not change the original intensity or m/z values, it is possible to restore the original data. This can be done with the reset function which will empty the lazy evaluation queue and call the reset method on the storage backend. Below we call reset on the sps_2 object and hence restore the data to its original state.

sps_2_rest <- reset(sps_2)

intensity(sps_2_rest)
## NumericList of length 4
## [[1]] 3.407 47.494 3.094 100 13.24
## [[2]] 6.685 4.381 3.022 16.708 100 4.565 40.643
## [[3]] 0.459 2.585 2.446 0.508 8.968 0.524 0.974 100 40.994
## [[4]] 3.837 32.341 0.84 0.534 100
## NumericList of length 4
## [[1]] 3.407 47.494 3.094 100 13.24
## [[2]] 6.685 4.381 3.022 16.708 100 4.565 40.643
## [[3]] 0.459 2.585 2.446 0.508 8.968 0.524 0.974 100 40.994
## [[4]] 3.837 32.341 0.84 0.534 100

Finally, for Spectra that use a writeable backend, such as the MsBackendDataFrame or MsBackendHdf5Peaks, it is possible to apply the processing queue to the peak data and write that back to the data storage with the applyProcessing function. Below we use this to make all data manipulations on peak data of the sps_rep object persistent.

length(sps_rep@processingQueue)
## [1] 2
sps_rep <- applyProcessing(sps_rep)
length(sps_rep@processingQueue)
## [1] 0
sps_rep
## MSn data (Spectra) with 4 spectra in a MsBackendDataFrame backend:
##     msLevel     rtime scanIndex
##   <integer> <numeric> <integer>
## 1         2        NA        NA
## 2         2        NA        NA
## 3         2        NA        NA
## 4         2        NA        NA
##  ... 20 more variables/columns.
## Processing:
##  Merge 2 Spectra into one [Fri Sep  2 05:41:45 2022]
##  Signal <= 10 in MS level(s) 2 set to 0 [Fri Sep  2 05:41:46 2022]
##  Remove peaks with intensities outside [0.1, Inf] in spectra of MS level(s) 2. [Fri Sep  2 05:41:46 2022]
##  ...1 more processings. Use 'processingLog' to list all.

Before applyProcessing the lazy evaluation queue contained 2 processing steps, which were then applied to the peak data and written to the data storage. Note that calling reset after applyProcessing can no longer restore the data.

Visualizing Spectra

The Spectra package provides the following functions to visualize spectra data: - plotSpectra: plot each spectrum in Spectra in its own panel. - plotSpectraOverlay: plot multiple spectra into the same plot.

Below we use plotSpectra to plot the 4 spectra from the sps object using their names (as provided in spectra variable "name") as plot titles.

plotSpectra(sps, main = sps$name)

It is also possible to label individual peaks in each plot. Below we use the m/z value of each peak as its label. In the example we define a function that accesses information from each spectrum (z) and returns a character for each peak with the text that should be used as label. Parameters labelSrt, labelPos and labelOffset define the rotation of the label text and its position relative to the x and y coordinates of the peak.

plotSpectra(sps, main = sps$name,
            labels = function(z) format(mz(z)[[1L]], digits = 4),
            labelSrt = -30, labelPos = 2, labelOffset = 0.1)

These plots are rather busy and for some peaks the m/z values are overplotted. Below we define a label function that will only indicate the m/z of peaks with an intensity higher than 30.

mzLabel <- function(z) {
    z <- peaksData(z)[[1L]]
    lbls <- format(z[, "mz"], digits = 4)
    lbls[z[, "intensity"] < 30] <- ""
    lbls
}
plotSpectra(sps, main = sps$name, labels = mzLabel,
            labelSrt = -30, labelPos = 2, labelOffset = 0.1)

Sometimes it might be of interest to plot multiple spectra into the same plot (e.g. to directly compare peaks from multiple spectra). This can be done with plotSpectraOverlay which we use below to create an overlay-plot of our 4 example spectra, using a different color for each spectrum.

cols <- c("#E41A1C80", "#377EB880", "#4DAF4A80", "#984EA380")
plotSpectraOverlay(sps, lwd = 2, col = cols)
legend("topleft", col = cols, legend = sps$name, pch = 15)

Lastly, plotSpectraMirror allows to plot two spectra against each other as a mirror plot which is ideal to visualize spectra comparison results. Below we plot a spectrum of 1-Methylhistidine against one of Caffeine.

plotSpectraMirror(sps[1], sps[3])

The upper panel shows the spectrum from 1-Methylhistidine, the lower the one of Caffeine. None of the peaks of the two spectra match. Below we plot the two spectra of 1-Methylhistidine and the two of Caffeine against each other matching peaks with a ppm of 50.

par(mfrow = c(1, 2))
plotSpectraMirror(sps[1], sps[2], main = "1-Methylhistidine", ppm = 50)
plotSpectraMirror(sps[3], sps[4], main = "Caffeine", ppm = 50)

See also ?plotSpectra for more plotting options and examples.

Aggregating spectra data

The Spectra package provides the combineSpectra function that allows to aggregate multiple spectra into a single one. The main parameters of this function are f, which defines the grouping of the spectra, and FUN which allows to define the function that performs the actual aggregation. The default aggregation function is combinePeaks (see ?combinePeaks for details) that combines multiple spectra into a single spectrum with all peaks from all input spectra (with additional paramter peaks = "union"), or peaks that are present in a certain proportion of input spectra (with parameter peaks = "intersect"; parameter minProp allows to define the minimum required proportion of spectra in which a peak needs to be present. Below we use this function to combine the spectra for 1-methylhistidine and caffeine into a single spectrum for each compound. We use the spectra variable $name, that contains the names of the compounds, to define which spectra should be grouped together.

sps_agg <- combineSpectra(sps, f = sps$name)

As a result, the 4 spectra got aggregated into two.

plotSpectra(sps_agg, main = sps_agg$name)

By default, all peaks present in all spectra are reported. As an alternative, by specifying peaks = "intersect" and minProp = 1, we could combine the spectra keeping only peaks that are present in both input spectra.

sps_agg <- combineSpectra(sps, f = sps$name, peaks = "intersect", minProp = 1)
plotSpectra(sps_agg, main = sps_agg$name)

This results thus in a single peak for 1-methylhistidine and none for caffeine - why? The reason for that is that the difference of the peaks’ m/z values is larger than the default tolerance used for the peak grouping (the defaults for combinePeaks is tolerance = 0 and ppm = 0). We could however already see in the previous section that the reported peaks’ m/z values have a larger measurement error (most likely because the fragment spectra were measured on different instruments with different precision). Thus, we next increase the tolerance and ppm parameters to group also peaks with a larger difference in their m/z values.

sps_agg <- combineSpectra(sps, f = sps$name, peaks = "intersect",
                          minProp = 1, tolerance = 0.2)
plotSpectra(sps_agg, main = sps_agg$name)

Whether in a real analysis we would be OK with such a large tolerance is however questionable. Note: which m/z and intensity is reported for the aggregated spectra can be defined with the parameters intensityFun and mzFun of combinePeaks (see ?combinePeaks for more information).

While the combinePeaks function is indeed helpful to combine peaks from different spectra, the combineSpectra function would in addition also allow us to provide our own, custom, peak aggregation function. As a simple example, instead of combining the spectra, we would like to select one of the input spectra as representative spectrum for grouped input spectra. combineSpectra supports any function that takes a list of peak matrices as input and returns a single peak matrix as output. We thus define below a function that calculates the total signal (TIC) for each input peak matrix, and returns the one peak matrix with the largest TIC.

#' function to select and return the peak matrix with the largest tic from
#' the provided list of peak matrices.
maxTic <- function(x, ...) {
    tic <- vapply(x, function(z) sum(z[, "intensity"], na.rm = TRUE),
                  numeric(1))
    x[[which.max(tic)]]
}

We can now use this function with combineSpectra to select for each compound the spectrum with the largest TIC.

sps_agg <- combineSpectra(sps, f = sps$name, FUN = maxTic)
plotSpectra(sps_agg, main = sps_agg$name)

Comparing spectra

Spectra can be compared with the compareSpectra function, that allows to calculate similarities between spectra using a variety of methods. However, peaks from the compared spectra have to be first matched before similarities can be calculated. compareSpectra uses by default the [joinPeaks()] function from the MsCoreUtils package but supports also other mapping functions to be passed with the MAPFUN parameter (see ?joinPeaks man page in MsCoreUtils for more details). The similarity calculation function can be specified with the FUN parameter and defaults to [ndotproduct()], the normalized dot-product. For more details, see also (Rainer et al. 2022) or the SpectraTutorials tutorial.

Below we calculate pairwise similarities between all spectra in sps accepting a 50 ppm difference of peaks’ m/z values for being considered matching.

compareSpectra(sps, ppm = 50)
##           [,1]      [,2]      [,3]      [,4]
## [1,] 1.0000000 0.1380817 0.0000000 0.0000000
## [2,] 0.1380817 1.0000000 0.0000000 0.0000000
## [3,] 0.0000000 0.0000000 1.0000000 0.1817149
## [4,] 0.0000000 0.0000000 0.1817149 1.0000000

The resulting matrix represents the result from the pairwise comparison. As expected, the first two and the last two spectra are similar, albeit only moderately while the spectra from 1-Methylhistidine don’t share any similarity with those of Caffeine.

Another way of comparing spectra would be to bin the spectra and to cluster them based on similar intensity values. Spectra binning ensures that the binned m/z values are comparable across all spectra. Below we bin our spectra using a bin size of 0.1 (i.e. all peaks with an m/z smaller than 0.1 are aggregated into one binned peak.

sps_bin <- bin(sps, binSize = 0.1)

All spectra will now have the same number of m/z values.

lengths(sps_bin)
## [1] 1400 1400 1400 1400

Most of the intensity values for these will however be 0 (because in the original spectra no peak for the respective m/z bin was present).

intensity(sps_bin)
## NumericList of length 4
## [[1]] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [[2]] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [[3]] 0.459 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 40.994 0 0 0 0 0 0 0 0 0
## [[4]] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100

We’re next creating an intensity matrix for our Spectra object, each row being one spectrum and columns representing the binned m/z values.

intmat <- do.call(rbind, intensity(sps_bin))

We can now identify those columns (m/z bins) with only 0s across all spectra and remove these.

zeros <- colSums(intmat) == 0
intmat <- intmat[, !zeros]
intmat
##       [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]   [,8]  [,9] [,10] [,11] [,12]
## [1,] 0.000 0.000 0.000 0.000 0.000 0.000 0.000  0.000 3.407 0.000 0.000 0.000
## [2,] 0.000 0.000 0.000 6.685 4.381 3.022 0.000 16.708 0.000 0.000 0.000 0.000
## [3,] 0.459 2.585 2.446 0.000 0.000 0.000 0.508  0.000 0.000 8.968 0.524 0.974
## [4,] 0.000 0.000 0.000 0.000 0.000 0.000 0.000  0.000 0.000 3.837 0.000 0.000
##      [,13]  [,14] [,15] [,16]   [,17] [,18]   [,19] [,20]  [,21] [,22]
## [1,]     0 47.494 3.094 0.000   0.000 0.000 100.000 13.24  0.000     0
## [2,]   100  0.000 0.000 4.565   0.000 0.000  40.643  0.00  0.000     0
## [3,]     0  0.000 0.000 0.000 100.000 0.000   0.000  0.00 40.994     0
## [4,]     0  0.000 0.000 0.000  32.341 1.374   0.000  0.00  0.000   100

The associated m/z values for the bins can be extracted with mz from the binned Spectra object. Below we use these as column names for the intensity matrix.

colnames(intmat) <- mz(sps_bin)[[1L]][!zeros]

This intensity matrix could now for example be used to cluster the spectra based on their peak intensities.

heatmap(intmat)

As expected, the first 2 and the last 2 spectra are more similar and are clustered together.

Exporting spectra

Spectra data can be exported with the export method. This method takes the Spectra that is supposed to be exported and the backend (parameter backend) which should be used to export the data and additional parameters for the export function of this backend. The backend thus defines the format of the exported file. Note however that not all MsBackend classes might support data export. The backend classes currently supporting data export and its format are: - MsBackendMzR (Spectra package): export data in mzML and mzXML format. Can not export all custom, user specified spectra variables. - MsBackendMgf (MsBackendMgf package): exports data in Mascot Generic Format (mgf). Exports all spectra variables as individual spectrum fields in the mgf file. - MsBackendMsp (MsBackendMsp): exports data in NIST MSP format. - MsBackendMassbank (MsBackendMassbank) exports data in Massbank text file format.

In the example below we use the MsBackendMzR to export all spectra from the variable sps to an mzML file. We thus pass the data, the backend that should be used for the export and the file name of the result file (a temporary file) to the export function (see also the help page of the export,MsBackendMzR function for additional supported parameters).

fl <- tempfile()
export(sps, MsBackendMzR(), file = fl)
## Writing file file11614c8f2aa7...OK

To evaluate which of the spectra variables were exported, we load the exported data again and identify spectra variables in the original file which could not be exported (because they are not defined variables in the mzML standard).

## [1] "id"         "name"       "splash"     "instrument"

These additional variables were thus not exported. How data export is performed and handled depends also on the used backend. The MsBackendMzR for example exports all spectra by default to a single file (specified with the file parameter), but it allows also to specify for each individual spectrum in the Spectra to which file it should be exported (parameter file has thus to be of length equal to the number of spectra). As an example we export below the spectrum 1 and 3 to one file and spectra 2 and 4 to another.

fls <- c(tempfile(), tempfile())
export(sps, MsBackendMzR(), file = fls[c(1, 2, 1, 2)])
## Writing file file1161c301baa...OK
## Writing file file11614cf8af36...OK

A more realistic use case for mzML export would be to export MS data after processing, such as smoothing (using the smooth function) and centroiding (using the pickPeaks function) of raw profile-mode MS data.

Changing backends

In the previous sections we learned already that a Spectra object can use different backends for the actual data handling. It is also possible to change the backend of a Spectra to a different one with the setBackend function. We could for example change the (MsBackendMzR) backend of the sps_sciex object to a MsBackendDataFrame backend to enable use of the data even without the need to keep the original mzML files. Below we change the backend of sps_sciex to the in-memory MsBackendDataFrame backend.

print(object.size(sps_sciex), units = "Mb")
## 0.4 Mb
sps_sciex <- setBackend(sps_sciex, MsBackendDataFrame())
sps_sciex
## MSn data (Spectra) with 1862 spectra in a MsBackendDataFrame backend:
##        msLevel     rtime scanIndex
##      <integer> <numeric> <integer>
## 1            1     0.280         1
## 2            1     0.559         2
## 3            1     0.838         3
## 4            1     1.117         4
## 5            1     1.396         5
## ...        ...       ...       ...
## 1858         1   258.636       927
## 1859         1   258.915       928
## 1860         1   259.194       929
## 1861         1   259.473       930
## 1862         1   259.752       931
##  ... 33 more variables/columns.
## Processing:
##  Switch backend from MsBackendMzR to MsBackendDataFrame [Fri Sep  2 05:41:52 2022]

With the call the full peak data was imported from the original mzML files into the object. This has obviously an impact on the object’s size, which is now much larger than before.

print(object.size(sps_sciex), units = "Mb")
## 52.4 Mb

The dataStorage spectrum variable has now changed, while dataOrigin still keeps the information about the originating files:

head(dataStorage(sps_sciex))
## [1] "<memory>" "<memory>" "<memory>" "<memory>" "<memory>" "<memory>"
head(basename(dataOrigin(sps_sciex)))
## [1] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"
## [3] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"
## [5] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_1_105-134.mzML"

Parallel processing notes

Most functions on Spectra support (and use) parallel processing out of the box. Peak data access and manipulation methods perform by default parallel processing on a per-file basis (i.e. using the dataStorage variable as splitting factor). Spectra uses BiocParallel for parallel processing and all functions use the default registered parallel processing setup of that package.

Backends

Backends allow to use different backends to store mass spectrometry data while providing via the Spectra class a unified interface to use that data. This is a further abstraction to the on-disk and in-memory data modes from MSnbase (Gatto, Gibb, and Rainer 2020). The Spectra package defines a set of example backends but any object extending the base MsBackend class could be used instead. The default backends are:

  • MsBackendDataFrame: the mass spectrometry data is stored (in-memory) in a DataFrame. Keeping the data in memory guarantees high performance but has also, depending on the number of mass peaks in each spectrum, a much higher memory footprint.

  • MsBackendMzR: this backend keeps only general spectra variables in memory and relies on the mzR package to read mass peaks (m/z and intensity values) from the original MS files on-demand.

  • MsBackendHdf5Peaks: similar to MsBackendMzR this backend reads peak data only on-demand from disk while all other spectra variables are kept in memory. The peak data are stored in Hdf5 files which guarantees scalability.

All of the above mentioned backends support changing all of their their spectra variables, except the MsBackendMzR that does not support changing m/z or intensity values for the mass peaks.

With the example below we load the data from a single mzML file and use a MsBackendHdf5Peaks backend for data storage. The hdf5path parameter allows us to specify the storage location of the HDF5 file.

library(msdata)
fl <- proteomics(full.names = TRUE)[5]

sps_tmt <- Spectra(fl, backend = MsBackendHdf5Peaks(), hdf5path = tempdir())
head(basename(dataStorage(sps_tmt)))
## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.h5"
## [2] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.h5"
## [3] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.h5"
## [4] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.h5"
## [5] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.h5"
## [6] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.h5"

A (possibly incomplete) list of R packages providing additional backends that add support for additional data types or storage options is provided below:

  • MsBackendMgf: support for import/export of mass spectrometry files in mascot generic format (MGF).
  • MsBackendMassbank: support for import/export and access of MassBank files/databases.
  • MsBackendHmdb: support for import of MS2 spectra from files in the xml-file format of the Human Metabolome Database (HMDB).
  • MsBackendMsp: import MS2 spectra from files in MSP format.

Session information

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] msdata_0.37.0        Spectra_1.7.2        ProtGenerics_1.29.0 
## [4] BiocParallel_1.31.12 S4Vectors_0.35.1     BiocGenerics_0.43.1 
## [7] BiocStyle_2.25.0    
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.9          highr_0.9           bslib_0.4.0        
##  [4] compiler_4.2.1      BiocManager_1.30.18 jquerylib_0.1.4    
##  [7] rhdf5filters_1.9.0  tools_4.2.1         ncdf4_1.19         
## [10] digest_0.6.29       rhdf5_2.41.1        clue_0.3-61        
## [13] jsonlite_1.8.0      evaluate_0.16       memoise_2.0.1      
## [16] rlang_1.0.5         cli_3.3.0           yaml_2.3.5         
## [19] parallel_4.2.1      pkgdown_2.0.6.9000  xfun_0.32          
## [22] fastmap_1.1.0       cluster_2.1.4       stringr_1.4.1      
## [25] knitr_1.40          desc_1.4.1          fs_1.5.2           
## [28] sass_0.4.2          systemfonts_1.0.4   IRanges_2.31.2     
## [31] MsCoreUtils_1.9.1   rprojroot_2.0.3     Biobase_2.57.1     
## [34] R6_2.5.1            textshaping_0.3.6   rmarkdown_2.16     
## [37] bookdown_0.28       Rhdf5lib_1.19.2     mzR_2.31.1         
## [40] purrr_0.3.4         magrittr_2.0.3      codetools_0.2-18   
## [43] htmltools_0.5.3     MASS_7.3-58.1       ragg_1.2.2         
## [46] stringi_1.7.8       cachem_1.0.6

References

Gatto, Laurent, Sebastian Gibb, and Johannes Rainer. 2020. MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data.” Journal of Proteome Research, September. https://doi.org/10.1021/acs.jproteome.0c00313.
Rainer, Johannes, Andrea Vicini, Liesa Salzer, Jan Stanstrup, Josep M. Badia, Steffen Neumann, Michael A. Stravs, et al. 2022. “A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.” Metabolites 12 (2): 173. https://doi.org/10.3390/metabo12020173.