Description and usage of Spectra objects
21 November 2024
Package: Spectra
Authors: RforMassSpectrometry Package Maintainer [cre],
Laurent Gatto [aut] (ORCID: https://orcid.org/0000-0002-1520-2268),
Johannes Rainer [aut] (ORCID: https://orcid.org/0000-0002-6977-7147),
Sebastian Gibb [aut] (ORCID: https://orcid.org/0000-0001-7406-4443),
Philippine Louail [aut] (ORCID:
https://orcid.org/0009-0007-5429-6846),
Jan Stanstrup [ctb] (ORCID: https://orcid.org/0000-0003-0541-7369),
Nir Shahaf [ctb],
Mar Garcia-Aloy [ctb] (ORCID: https://orcid.org/0000-0002-1330-6610)
Last modified: 2024-10-23 23:25:12.511366
Compiled: Thu Nov 21 19:46:40 2024
1 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). For information on how to handle and (parallel) process large-scale data sets see the Large-scale data handling and processing with Spectra vignette.
2 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.
3 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 theMsBackendDataFramethis will be"<memory>", for an on-disk backend such as theMsBackendHdf5Peaksit 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 (0and1representing 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.
3.1 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,
olarity 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 MsBackendMemory 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, source = 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.mzMLThe 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 MsBackendMemory).
3.2 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:
spectraVariables(sps)## [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 2rtime(sps)## [1] NA NA NAWe 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 1.675Note 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 2We 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 TRUEThe $ 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 195.0876intensity(sps)## 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 40.994Peak 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.
peaksVariables(sps)## [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 13.240Note 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 3The 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 CaffeineSpectra 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:
dataOrigin(sps)## [1] NA NA NAdataOrigin 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:
dataStorage(sps)## [1] "<memory>" "<memory>" "<memory>"head(basename(dataStorage(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"Certain backends (such as the MsBackendMemory and MsBackendDataFrame)
support also additional peaks variables. At present, these must already be
present when the backend gets initialized. In future a dedicated function
allowing to add peaks variables will be available. Below we thus first extract
the full data (including peaks variables) from the sps spectra object and add
a column "peak_anno" with peak annotations for each individual
peak. Importantly, for peak variables, a value needs to be assigned to each
individual peak, even it it is NA (the lengths() of the new peak variable
must match lengths() of mz or intensity, i.e. the number of peaks per
spectrum).
## Extract the full data from a spectrum
spd <- spectraData(sps, columns = union(spectraVariables(sps),
peaksVariables(sps)))
## Add a new column with a *annotation* for each peak
spd$peak_anno <- list(c("a", NA_character_, "b", "c", "d"),
c("a", "b", "c", "d", "e", "f", "g"),
c("a", "b", "c", "d", "e", "f", "g", "h", "i"))
## lengths have to match:
lengths(spd$peak_anno)## [1] 5 7 9lengths(spd$mz)## [1] 5 7 9The parameter peaksVariables() (currently only available for the
backendInitialize() method of MsBackendMemory and MsBackendDataFrame)
allows to define which of the columns from an input data contain peaks variables
(in our case "mz", "intensity" and the additional "peak_anno" column).
sps2 <- Spectra(spd, backend = MsBackendMemory(),
peaksVariables = c("mz", "intensity", "peak_anno"))
peaksVariables(sps2)## [1] "mz" "intensity" "peak_anno"Full peak data can be extracted with the peaksData() function that has a
second parameter columns allowing to define which peak variables to
return. Below we extract the peak data for the second spectrum.
peaksData(sps2, columns = peaksVariables(sps2))[[2L]]## mz intensity peak_anno
## 1 83.10 6.685 a
## 2 96.12 4.381 b
## 3 97.14 3.022 c
## 4 109.14 16.708 d
## 5 124.08 100.000 e
## 6 125.10 4.565 f
## 7 170.16 40.643 gWe can also use the peaksData() function to extract the values for individual
peak variables.
## Peak annotations for the first spectrum
peaksData(sps2, "peak_anno")[[1L]]## peak_anno
## 1 a
## 2 <NA>
## 3 b
## 4 c
## 5 d## Peak annotations for the second spectrum
peaksData(sps2, "peak_anno")[[2L]]## peak_anno
## 1 a
## 2 b
## 3 c
## 4 d
## 5 e
## 6 f
## 7 gPeak variables can also be extracted using the $ method:
sps2$peak_anno## [[1]]
## [1] "a" NA "b" "c" "d"
##
## [[2]]
## [1] "a" "b" "c" "d" "e" "f" "g"
##
## [[3]]
## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i"Similar to spectra variables it is also possible to replace values for
existing peaks variables using the $<- function.
3.3 Filtering, aggregating and merging spectra data
Various functions are available to filter, subset and merge Spectra
objects. These can be generally subdivided into functions that subset or filter
spectra data and operations that filter mass peak data. A third category of
function allows to aggregate data within a Spectra or to merge and combine
multiple Spectra objects into one. Functions of the various categories are
described in the following subsections. Please refer to the function’s
documentation for more details and information.
3.3.1 Filter spectra data
These functions comprise subset operations that reduce the total number of
spectra in a Spectra object as well as filter functions that reduce the
content of the Spectra’s spectra data (i.e. the content of its
spectraVariables()). These functions thus don’t change or affect the mass
peaks data of the Spectra’s individual spectra.
[: operation to reduce aSpectraobject to selected elements.dropNaSpectraVariables(): dropsspectraVariables()that contain only missing values. The function returns aSpectraobject with the same number of elements, but with eventually fewer spectra variables.filterAcquisitionNum(): retains spectra with certain acquisition numbers.filterDataOrigin(): subsets to spectra from specific origins.filterDataStorage(): subsets to spectra from certain data storage files.filterEmptySpectra(): removes spectra without mass peaks.filterIsolationWindow(): keeps spectra with the providedmzin their isolation window (m/z range).filterMsLevel(): filters by MS level.filterPolarity(): filters by polarity.filterPrecursorCharge(): retains (MSn) spectra with specified precursor charge(s).filterPrecursorIsotopes(): identifies precursor ions (from fragment spectra) that could represent isotopes of the same molecule. For each of these spectra groups only the spectrum of the monoisotopic precursor ion is returned. MS1 spectra are returned without filtering.filterPrecursorMaxIntensity(): filters spectra keeping, for groups of spectra with similar precursor m/z, the one spectrum with the highest precursor intensity. All MS1 spectra are returned without filtering.filterPrecursorMzRange(): retains (MSn) spectra with a precursor m/z within the provided m/z range.filterPrecursorMzValues((): retains (MSn) spectra with precursor m/z value matching the provided value(s) considering also atoleranceandppm.filterPrecursorScan(): retains (parent and children) scans of an acquisition number.filterRanges(): filters aSpectraobject based on (multiple) user defined numeric ranges for one or more available (numeric) spectra variables.filterRt(): filters based on retention time range.filterValues(): filters aSpectraobject based on similarities of numeric values of one or more available spectra variables.selectSpectraVariables(): reduces the (spectra) data within the object to the selected spectra variables.
3.3.2 Filter or aggregate mass peak data
These function filter or aggregate the mass peak data (peaksData()) of each
spectrum in a Spectra without changing the total number of spectra.
combinePeaks(): groups peaks within each spectrum based on similarity of their m/z values and combines these into a single peak per peak group.deisotopeSpectra(): deisotopes each individual spectrum keeping only the monoisotopic peak for peaks groups of potential isotopologues.filterFourierTransformArtefacts(): removes (Orbitrap) fast fourier transform artifact peaks from spectra.filterIntensity(): filter each spectrum keeping only peaks with intensities meeting certain criteria.filterMzRange(): filters mass peaks keeping (or removing) those with an m/z within the provided m/z range.filterMzValues(): filters mass peaks within each spectrum keeping (or removing) those with an m/z matching the provided value(s).filterPeaksRanges(): filters mass peaks using any set of range-based filters on numeric spectra or peaks variables.filterPrecursorPeaks(): removes peaks with either an m/z value matching the precursor m/z of the respective spectrum (with parametermz = "==") or peaks with an m/z value larger or equal to the precursor m/z (with parametermz = ">=").reduceSpectra(): filters individual spectra keeping only the largest peak for groups of peaks with similar m/z values.
3.3.3 Merging, aggregating and splitting
c(): combine severalSpectrainto a singleSpectraobject.combineSpectra(): allows to combine the MS data from sets of spectra into a single spectrum per set. Thus, instead of filtering the data, this function aggregates it.joinSpectraData(): merge aDataFrameto the existing spectra data.split(): splits theSpectraobject based on a provided grouping factor.
3.3.4 Examples and use cases for filter operations
In this example, we use the filterValues() function to retain spectra with a
base peak m/z close to 100 (+/- 30 ppm) and a retention time around 230 (+/- 5
s).
sps_sub <- filterValues(sps_sciex, spectraVariables = c("basePeakMZ", "rtime"),
values = c(123.089, 230), tolerance = c(0,5),
ppm = c(30, 0), match = "all")
length(sps_sub)## [1] 72Then, we demonstrate the usage of the filterRanges() function to filter
spectra based on ranges of values for variables such as base peak m/z, peak
count, and retention time.
sps_ranges <- filterRanges(sps_sciex,
spectraVariables = c("basePeakMZ","peaksCount",
"rtime"),
ranges = c(123.09,124, 3500, 3520, 259, 260),
match = "all")
length(sps_ranges)## [1] 1Only one spectrum matches all the ranges. Another option for filterValues()
and filterRanges() is to use the parameter match = "any", which retains
spectra that match any one of the conditions instead of having to match all of
them. Let’s run the code once again but change the match parameter this time:
sps_ranges <- filterRanges(sps_sciex,
spectraVariables = c("basePeakMZ",
"peaksCount", "rtime"),
ranges = c(123.09, 124, 3500, 3520, 259, 260),
match = "any")
length(sps_ranges)## [1] 473We can see many more spectra passed the filtering step this time.
In the example below we use specific functions to 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] 931sps_sub <- filterRt(file_2, rt = c(175, 189))
length(sps_sub)## [1] 50In 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.mzMLThe 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) /home/biocbuild/bbs-3.21-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_3_105-134.mzML [Thu Nov 21 19:46:41 2024]
## Filter: select retention time [175..189] on MS level(s) 1 [Thu Nov 21 19:46:41 2024]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 MsBackendMemory 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 [Thu Nov 21 19:46:41 2024]The resulting object contains now the data for all 4 MS2 spectra and an union of all spectra variables from both objects.
spectraVariables(sps)## [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.
3.4 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 100Zero-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 100The 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 MsBackendMemory 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 [Thu Nov 21 19:46:41 2024]
## Signal <= 10 in MS level(s) 2 set to 0 [Thu Nov 21 19:46:41 2024]
## Remove peaks with intensities outside [0.1, Inf] in spectra of MS level(s) 2. [Thu Nov 21 19:46:41 2024]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 MsBackendMemory 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 [Thu Nov 21 19:46:41 2024]
## Signal <= 10 in MS level(s) 2 set to 0 [Thu Nov 21 19:46:41 2024]
## Remove peaks with intensities outside [0.1, Inf] in spectra of MS level(s) 2. [Thu Nov 21 19:46:41 2024]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 50intensity(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 100Alternatively 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 1intensity(sps_2)## NumericList of length 4
## [[1]] 100
## [[2]] 100
## [[3]] 100
## [[4]] 100Each 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(170.5, 170.5, 195.1, 195.1)
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 195.9864mz(sps_3)## NumericList of length 4
## [[1]] -0.0200000000000102 0.340000000000003 46.3
## [[2]] 0.340000000000003 46.42 61.36
## [[3]] 0.0123999999999853 57.0338
## [[4]] -0.886400000000009 57.0345As 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]] -0.0200000000000102 0.340000000000003 46.3
## [[2]] 0.340000000000003 46.42 61.36
## [[3]] 0.0123999999999853 57.0338
## [[4]] -0.886400000000009 57.0345Using 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.
Another example for spectra processing potentially helpful for spectral matching
against reference fragment spectra libraries would be a function that removes
fragment peaks with an m/z matching the precursor m/z of a spectrum. Below we
define such a function that takes the peaks matrix and the precursor m/z as
input and evaluates with the closest() function from the
MsCoreUtils whether the spectrum contains peaks with an m/z value
matching the one of the precursor (given tolerance and ppm). The returned
peaks matrix contains all peaks except those matching the precursor m/z.
library(MsCoreUtils)
remove_precursor <- function(x, precursorMz, tolerance = 0.1, ppm = 0, ...) {
if (!is.na(precursorMz)) {
keep <- is.na(closest(x[, "mz"], precursorMz, tolerance = tolerance,
ppm = ppm, .check = FALSE))
x[keep, , drop = FALSE]
} else x
}We can now again add this processing step to our Spectra object. As a result,
peaks matching the precursor m/z (with tolerance = 0.1 and ppm = 0) will be
removed.
sps_4 <- addProcessing(sps_rep, remove_precursor,
spectraVariables = "precursorMz")
peaksData(sps_4) |> as.list()## [[1]]
## mz intensity
## [1,] 124.20 47.494
## [2,] 170.16 100.000
##
## [[2]]
## mz intensity
## [1,] 109.14 16.708
## [2,] 124.08 100.000
## [3,] 170.16 40.643
##
## [[3]]
## mz intensity
## [1,] 138.0662 100
##
## [[4]]
## mz intensity
## [1,] 138.0655 32.341
## [2,] 195.9864 100.000As a reference, the original peak matrices are shown below.
peaksData(sps_rep) |> as.list()## [[1]]
## mz intensity
## [1,] 124.20 47.494
## [2,] 170.16 100.000
## [3,] 170.52 13.240
##
## [[2]]
## mz intensity
## [1,] 109.14 16.708
## [2,] 124.08 100.000
## [3,] 170.16 40.643
##
## [[3]]
## mz intensity
## [1,] 138.0662 100.000
## [2,] 195.0876 40.994
##
## [[4]]
## mz intensity
## [1,] 138.0655 32.341
## [2,] 195.9864 100.000Note that we can also perform a more relaxed matching of m/z values by passing a
different value for tolerance to the function:
sps_4 <- addProcessing(sps_rep, remove_precursor, tolerance = 0.6,
spectraVariables = "precursorMz")
peaksData(sps_4) |> as.list()## [[1]]
## mz intensity
## [1,] 124.2 47.494
##
## [[2]]
## mz intensity
## [1,] 109.14 16.708
## [2,] 124.08 100.000
##
## [[3]]
## mz intensity
## [1,] 138.0662 100
##
## [[4]]
## mz intensity
## [1,] 138.0655 32.341
## [2,] 195.9864 100.000Since 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 100intensity(sps)## 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 100Finally, for Spectra that use a writeable backend, such as the
MsBackendMemory, 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] 2sps_rep <- applyProcessing(sps_rep)
length(sps_rep@processingQueue)## [1] 0sps_rep## MSn data (Spectra) with 4 spectra in a MsBackendMemory 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 [Thu Nov 21 19:46:41 2024]
## Signal <= 10 in MS level(s) 2 set to 0 [Thu Nov 21 19:46:41 2024]
## Remove peaks with intensities outside [0.1, Inf] in spectra of MS level(s) 2. [Thu Nov 21 19:46:41 2024]
## ...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.
3.5 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.
3.6 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 sets of spectra that should be combined, and
FUN, which allows to define the function that performs the actual
aggregation. The default aggregation function is combinePeaksData() (see
?combinePeaksData 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. It
is important to mention that, by default, the function combines all mass peaks
from all spectra with a similar m/z value into a single, representative mass
peak aggregating all their intensities into one. To avoid the resulting
intensity to be affected by potential noise peaks it might be advised to first
clean the individual mass spectra using e.g. the combinePeaks() or
reduceSpectra() functions that first aggregate mass peaks within each
individual spectrum.
In this example we below we use combineSpectra() 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$name)
This results thus in a single peak for 1-methylhistidine and none for caffeine -
why? The reason for that is that the difference of the peaks’ m/z values is
larger than the default tolerance used for the peak grouping (the defaults for
combinePeaksData() is tolerance = 0 and ppm = 0). We could however already
see in the previous section that the reported peaks’ m/z values have a larger
measurement error (most likely because the fragment spectra were measured on
different instruments with different precision). Thus, we next increase the
tolerance and ppm parameters to group also peaks with a larger difference in
their m/z values.
sps_agg <- combineSpectra(sps, f = sps$name, peaks = "intersect",
minProp = 1, tolerance = 0.2)
plotSpectra(sps_agg, main = sps_agg$name)
Whether in a real analysis we would be OK with such a large tolerance is however
questionable. Note: which m/z and intensity is reported for the aggregated
spectra can be defined with the parameters intensityFun and mzFun of
combinePeaksData() (see ?combinePeaksData for more information).
While the combinePeaksData() function is indeed helpful to combine peaks from
different spectra, the combineSpectra() function would in addition also allow
us to provide our own, custom, peak aggregation function. As a simple example,
instead of combining the spectra, we would like to select one of the input
spectra as representative spectrum for grouped input
spectra. combineSpectra() supports any function that takes a list of peak
matrices as input and returns a single peak matrix as output. We thus define
below a function that calculates the total signal (TIC) for each input peak
matrix, and returns the one peak matrix with the largest TIC.
#' function to select and return the peak matrix with the largest tic from
#' the provided list of peak matrices.
maxTic <- function(x, ...) {
tic <- vapply(x, function(z) sum(z[, "intensity"], na.rm = TRUE),
numeric(1))
x[[which.max(tic)]]
}We can now use this function with combineSpectra() to select for each compound
the spectrum with the largest TIC.
sps_agg <- combineSpectra(sps, f = sps$name, FUN = maxTic)
plotSpectra(sps_agg, main = sps_agg$name)
3.7 Comparing spectra
Spectra can be compared with the compareSpectra() function, that allows to
calculate similarities between spectra using a variety of
methods. compareSpectra() implements similarity scoring as a two-step
approach: first the peaks from the pair of spectra that should be compared are
matched (mapped) against each other and then a similarity score is calculated on
these. The MAPFUN parameter of compareSpectra() defines the function to
match (or map) the peaks between the spectra and parameter FUN specifies the
function to calculate the similarity. By default, compareSpectra() uses
MAPFUN = joinPeaks (see ?joinPeaks for a description and alternative
options) and FUN = ndotproduct (the normalized dot-product spectra similarity
score). Parameters to configure these functions can be passed to
compareSpectra() as additional parameter (such as e.g. ppm to define the
m/z-relative tolerance for peak matching in joinPeaks()).
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 1.0000000The resulting matrix provides the similarity scores 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. Similarities between Spectra objects can
be calculated with calls in the form of compareSpectra(a, b) with a and b
being the two Spectra objects to compare. As a result a n x m matrix will be
returned with n (rows) being the spectra in a and m (columns) being the
spectra in b.
The above similarity was calculated with the default (normalized) dot-product,
but also other similarity scores can be used instead. Either one of the other
metrics provided by the MsCoreUtils could be used (see
?MsCoreUtils::distance for a list of available options) or any other external
or user-provided similarity scoring function. As an example, we use below the
spectral entropy similarity score introduced in (Y et al. 2021) and provided
with the
msentropy
package. Since this msentropy_similarity() function performs also the mapping
of the peaks between the compared spectra internally (along with some spectra
cleaning), we have to disable that in the compareSpectra() function using
MAPFUN = joinPeaksNone. To configure the similarity scoring we can pass all
additional parameters of the msentropy_similarity() (see
?msentropy_similarity) to the compareSpectra() call. We use
ms2_tolerance_in_ppm = 50 to set the tolerance for m/z-relative peak matching
(equivalent to ppm = 50 used above) and ms2_tolerance_in_da = -1 to disable
absolute tolerance matching.
library(msentropy)## Loading required package: RcppcompareSpectra(sps, MAPFUN = joinPeaksNone, FUN = msentropy_similarity,
ms2_tolerance_in_ppm = 50, ms2_tolerance_in_da = -1)## 1 2 3 4
## 1 1.0000000 0.3002225 0.0000000 0.0000000
## 2 0.3002225 1.0000000 0.0000000 0.0000000
## 3 0.0000000 0.0000000 1.0000000 0.5144764
## 4 0.0000000 0.0000000 0.5144764 1.0000000Note also that GNPS-like scores can be calculated with MAPFUN = joinPeaksGnps
and FUN = MsCoreUtils::gnps. For additional information and examples see also
(Rainer et al. 2022) or the
SpectraTutorials tutorial.
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. Below, we explicitly set zero.rm = FALSE to retain all bins
generated by the function, including those with an intensity of zero.
sps_bin <- Spectra::bin(sps, binSize = 0.1, zero.rm = FALSE)All spectra will now have the same number of m/z values.
lengths(sps_bin)## [1] 1400 1400 1400 1400Most 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 100We’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 100The 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.
3.8 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 file1a910b50f6ad03...OKTo 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).
sps_im <- Spectra(backendInitialize(MsBackendMzR(), fl))
spectraVariables(sps)[!spectraVariables(sps) %in% spectraVariables(sps_im)]## [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 file1a910b36418d54...OK
## Writing file file1a910b1aae289b...OKA 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.
3.9 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 MsBackendMemory 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 MsBackendMemory backend.
print(object.size(sps_sciex), units = "Mb")## 0.4 Mbsps_sciex <- setBackend(sps_sciex, MsBackendMemory())
sps_sciex## MSn data (Spectra) with 1862 spectra in a MsBackendMemory 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 MsBackendMemory [Thu Nov 21 19:46:47 2024]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")## 53.2 MbThe 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"4 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:
MsBackendMemory: the default backend to store data in memory. Due to its design theMsBackendMemoryprovides fast access to the peaks matrices (using thepeaksData()function) and is also optimized for fast access to spectra variables and subsetting. Since all data is kept in memory, this backend has a relatively large memory footprint (depending on the data) and is thus not suggested for very large MS experiments.MsBackendDataFrame: the mass spectrometry data is stored (in-memory) in aDataFrame. 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 toMsBackendMzRthis 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:
MsBackendCompDb(package CompoundDb): provides access to spectra data (spectra and peaks variables) from a CompDb database. Has a small memory footprint because all data (except precursor m/z values) are retrieved on-the-fly from the database.MsBackendHmdbXml(packageMsbackendHmdb): allows import of MS data from xml files of the Human Metabolome Database (HMDB). Extends theMsBackendDataFrameand keeps thus all data, after import, in memory.MsBackendMassbank(package MsBackendMassbank): allows to import/export data in MassBank text file format. Extends theMsBackendDataFrameand keeps thus all data, after import, in memory.MsBackendMassbankSql(package MsBackendMassbank): allows to directly connect to a MassBank SQL database to retrieve all MS data and variables. Has a minimal memory footprint because all data is retrieved on-the-fly from the SQL database.MsBackendMetaboLights(packager BiocStyle::Biocpkg("MsBackendMetaboLights")): retrieves and caches MS data files from the MetaboLights repository.MsBackendMgf: (package MsBackendMgf): support for import/export of mass spectrometry files in mascot generic format (MGF).MsBackendMsp: (package MsBackendMsp): allows to import/export data in NIST MSP format. Extends theMsBackendDataFrameand keeps thus all data, after import, in memory.MsBackendRawFileReader(package MsBackendRawFileReader): implements a backend for reading MS data from Thermo Fisher Scientific’s raw data files using the manufacturer’s NewRawFileReader .Net libraries. The package generalizes the functionality introduced by the rawrr package, see also (Kockmann and Panse 2021).MsBackendSql(package MsBackendSql): stores all MS data in a SQL database and has thus a minimal memory footprint.MsBackendTimsTof(packageMsBackendTimsTof: allows import of data from Bruker TimsTOF raw data files (using theopentimsrR package).MsBackendWeizMass(packageMsBackendWeizMass: allows to access MS data from WeizMass MS/MS spectral databases.
5 Handling very large data sets
The Spectra package was designed to support also efficient processing of very
large data sets. Most of the functionality do not require to keep the full MS
data in memory (specifically, the peaks data, i.e., m/z and intensity values,
which represent the largest chunk of data for MS experiments). For some
functions however the peaks data needs to be loaded into memory. One such
example is the lengths() function to determine the number of peaks per spectra
that is calculated (on the fly) by evaluating the number of rows of the peaks
matrix. Backends such as the MsBackendMzR perform by default any data
processing separately (and eventually in parallel) by data file and it should
thus be safe to call any such functions on a Spectra object with that
backend. For other backends (such as the
MsBackendSql or the
MsBackendMassbankSql)
it is however advised to process the data in a chunk-wise manner using the
spectrapply() function with parameter chunkSize. This will split the
original Spectra object into chunks of size chunkSize and applies the
function separately to each chunk. That way only data from one chunk will
eventually be loaded into memory in each iteration enabling to process also very
large Spectra objects on computers with limited hardware resources. Instead of
a lengths(sps) call, the number of peaks per spectra could also be determined
(in a less memory demanding way) with spectrapply(sps, lengths, chunkSize = 5000L). In that way only peak data of 5000 spectra at a time will be loaded
into memory.
6 Serializing (saving), moving and loading serialized Spectra objects
Serializing and re-loading variables/objects during an analysis using e.g. the
save() and load() functions are common in many workflows, especially if some
of the tasks are computationally intensive and take long time. Sometimes such
serialized objects might even be moved from one computer (or file system) to
another. These operations are unproblematic for Spectra objects with
in-memory backends such as the MsBackendMemory or MsBackendDataFrame, that
keep all data in memory, would however break for on-disk backends such as the
MsBackendMzR if the file path to the original data files is not identical. It
is thus suggested (if the size of the MS data respectively the available system
memory allows it) to change the backend for such Spectra objects to a
MsBackendMemory before serializing the object with save(). For Spectra
objects with a MsBackendMzR an alternative option would be to eventually
update/adapt the path to the directory containing the raw (e.g. mzML) data
files: assuming these data files are available on both computers, the path to
the directory containing these can be updated with the dataStorageBasePath<-
function allowing thus to move/copy serialized Spectra objects between
computers or file systems.
An example workflow could be:
files a.mzML, b.mzML are stored in a directory /data/mzML/ on one
computer. These get loaded as a Spectra object with MsBackendMzR and then
serialized to a file A.RData.
A <- Spectra(c("/data/mzML/a.mzML", "/data/mzML/b.mzML"))
save(A, file = "A.RData")Assuming this file gets now copied to another computer (where the data is not
available in a folder /data/mzML/) and loaded with load().
load("A.RData")This Spectra object would not be valid because its MsBackendMzR can no
longer access the MS data in the original data files. Assuming the user also
copied the data files a.mzML and b.mzML, but to a folder
/some_other_folder/, the base storage path of the object would need to be
adapted to match the directory where the data files are available on the second
computer:
dataStorageBasePath(A) <- "/some_other_folder"By pointing now the storage path to the new storage location of the data files,
the Spectra object A would also be usable on the second computer.
7 Session information
sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [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] msdata_0.47.0 msentropy_0.1.4 Rcpp_1.0.13-1
## [4] MsCoreUtils_1.19.0 IRanges_2.41.1 Spectra_1.17.1
## [7] BiocParallel_1.41.0 S4Vectors_0.45.2 BiocGenerics_0.53.3
## [10] generics_0.1.3 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.9 compiler_4.5.0 BiocManager_1.30.25
## [4] tinytex_0.54 Biobase_2.67.0 rhdf5filters_1.19.0
## [7] magick_2.8.5 parallel_4.5.0 cluster_2.1.6
## [10] jquerylib_0.1.4 yaml_2.3.10 fastmap_1.2.0
## [13] R6_2.5.1 ProtGenerics_1.39.0 knitr_1.49
## [16] MASS_7.3-61 bookdown_0.41 bslib_0.8.0
## [19] rlang_1.1.4 cachem_1.1.0 xfun_0.49
## [22] fs_1.6.5 sass_0.4.9 cli_3.6.3
## [25] Rhdf5lib_1.29.0 magrittr_2.0.3 digest_0.6.37
## [28] ncdf4_1.23 mzR_2.41.1 MetaboCoreUtils_1.15.0
## [31] rhdf5_2.51.0 lifecycle_1.0.4 clue_0.3-66
## [34] evaluate_1.0.1 codetools_0.2-20 rmarkdown_2.29
## [37] tools_4.5.0 htmltools_0.5.8.1