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),
Philippine Louail [aut] (https://orcid.org/0009-0007-5429-6846),
Jan Stanstrup [ctb] (https://orcid.org/0000-0003-0541-7369),
Nir Shahaf [ctb],
Mar Garcia-Aloy [ctb] (https://orcid.org/0000-0002-1330-6610)
Last modified: 2024-05-16 14:06:27.75861
Compiled: Thu May 16 19:35:45 2024
The Spectra package supports handling and processing of also very
large mass spectrometry (MS) data sets. Through dedicated backends, that only
load MS data when requested/needed, the memory demand can be minimized. Examples
for such backends are the MsBackendMzR and the MsBackendOfflineSql (defined
in the MsBackendSql package). In addition, Spectra supports
chunk-wise data processing, hence only parts of the data are loaded into memory
and processed at a time. In this document we provide information on how large
scale data can be best processed with the Spectra package.
The Spectra package separates functionality to process and analyze MS data
(implemented for the Spectra class) from the code that defines how and where
the MS data is stored. For the latter, different implementations of the
MsBackend class are available, that either are optimized for performance (such
as the MsBackendMemory and MsBackendDataFrame) or for low memory requirement
(such as the MsBackendMzR, or the MsBackendOfflineSql implemented in the
MsBackendSql package, that through the smallest possible memory
footprint enables also the analysis of very large data sets). Below we load MS
data from 4 test files into a Spectra using a MsBackendMzR backend.
library(Spectra)
#' Define the file names from which to import the data
fls <- c(
system.file("TripleTOF-SWATH", "PestMix1_DDA.mzML", package = "msdata"),
system.file("TripleTOF-SWATH", "PestMix1_SWATH.mzML", package = "msdata"),
system.file("sciex", "20171016_POOL_POS_1_105-134.mzML",
package = "msdata"),
system.file("sciex", "20171016_POOL_POS_3_105-134.mzML",
package = "msdata"))
#' Creating a Spectra object representing the MS data
sps_mzr <- Spectra(fls, source = MsBackendMzR())
sps_mzr## MSn data (Spectra) with 18463 spectra in a MsBackendMzR backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 0.231 1
## 2 1 0.351 2
## 3 1 0.471 3
## 4 1 0.591 4
## 5 1 0.711 5
## ... ... ... ...
## 18459 1 258.636 927
## 18460 1 258.915 928
## 18461 1 259.194 929
## 18462 1 259.473 930
## 18463 1 259.752 931
## ... 33 more variables/columns.
##
## file(s):
## PestMix1_DDA.mzML
## PestMix1_SWATH.mzML
## 20171016_POOL_POS_1_105-134.mzML
## ... 1 more filesThe resulting Spectra uses a MsBackendMzR for data representation. This
backend does only load general spectra data into memory while the full MS data
(i.e., the m/z and intensity values of the individual mass peaks) is only
loaded when requested or needed. In contrast to an in-memory backend, the
memory footprint of this backend is thus lower.
Below we create a Spectra that keeps the full data in memory by changing the
backend to a MsBackendMemory backend and compare the sizes of both objects.
sps_mem <- setBackend(sps_mzr, MsBackendMemory())
print(object.size(sps_mzr), units = "MB")## 5.2 Mbprint(object.size(sps_mem), units = "MB")## 140.1 MbKeeping the full data in memory requires thus considerably more memory.
We next disable parallel processing for Spectra to allow an unbiased estimation of memory usage.
#' Disable parallel processing globally
register(SerialParam())Operations on peaks data are the most time and memory demanding tasks. These
generally apply a function to, or modify the m/z and/or intensity
values. Among these functions are for example functions that filter, remove or
combine mass peaks (such as filterMzRange(), filterIntensity() or
combinePeaks()) or functions that perform calculations on the peaks data (such
as bin() or pickPeaks()) or also functions that provide information on, or
summarize spectra (such as lengths() or ionCount()). For all these
functions, the peaks data needs to be present in memory and on-disk backends,
such as the MsBackendMzR, need thus to first import the data from their data
storage. However, loading the full peaks data at once into memory might not be
possible for large data sets. Loading and processing the data in smaller chunks
would however reduce the memory demand and hence allow to process also such data
sets. For the MsBackendMzR and MsBackendHdf5Peaks backends the data is
automatically split and processed by the data storage files. For all other
backends chunk-wise processing can be enabled by defining the
processingChunkSize of a Spectra, i.e. the number of spectra for which peaks
data should be loaded and processed in each iteration. The
processingChunkFactor() function can be used to evaluate how the data will be
split. Below we use this function to evaluate how chunk-wise processing would be
performed with the two Spectra objects.
processingChunkFactor(sps_mem)## factor()
## Levels:For the Spectra with the in-memory backend an empty factor() is returned,
thus, no chunk-wise processing will be performed. We next evaluate whether the
Spectra with the MsBackendMzR on-disk backend would use chunk-wise
processing.
processingChunkFactor(sps_mzr) |> table()##
## /home/biocbuild/bbs-3.19-bioc/R/site-library/msdata/TripleTOF-SWATH/PestMix1_DDA.mzML
## 7602
## /home/biocbuild/bbs-3.19-bioc/R/site-library/msdata/TripleTOF-SWATH/PestMix1_SWATH.mzML
## 8999
## /home/biocbuild/bbs-3.19-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML
## 931
## /home/biocbuild/bbs-3.19-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_3_105-134.mzML
## 931The data would thus be split and processed by the original file, from which the
data is imported. We next specifically define the chunk-size for both Spectra
with the processingChunkSize() function.
processingChunkSize(sps_mem) <- 3000
processingChunkFactor(sps_mem) |> table()##
## 1 2 3 4 5 6 7
## 3000 3000 3000 3000 3000 3000 463After setting the chunk size, also the Spectra with the in-memory backend
would use chunk-wise processing. We repeat with the other Spectra object:
processingChunkSize(sps_mzr) <- 3000
processingChunkFactor(sps_mzr) |> table()##
## 1 2 3 4 5 6 7
## 3000 3000 3000 3000 3000 3000 463The Spectra with the MsBackendMzR backend would now split the data in about
equally sized arbitrary chunks and no longer by original data file. Setting
processingChunkSize thus overrides any splitting suggested by the backend.
After having set a processingChunkSize, any operation involving peaks data
will by default be performed in a chunk-wise manner. Thus, calling ionCount()
on our Spectra will now split the data in chunks of 3000 spectra and sum the
intensities (per spectrum) chunk by chunk.
tic <- ionCount(sps_mem)While chunk-wise processing reduces the memory demand of operations, the
splitting and merging of the data and results can negatively impact
performance. Thus, small data sets or Spectra with in-memory backends
will generally not benefit from this type of processing. For computationally
intense operation on the other hand, chunk-wise processing has the advantage,
that chunks can (and will) be processed in parallel (depending on the parallel
processing setup).
Note that this chunk-wise processing only affects functions that involve actual
peak data. Subset operations that only reduce the number of spectra (such as
filterRt() or [) bypass this mechanism and are applied immediately to the
data.
For an evaluation of chunk-wise processing see also this issue on the Spectra github repository.
Estimating memory usage in R tends to be difficult, but for MS data sets with
more than about 100 samples or whenever processing tends to take longer than
expected it is suggested to enable chunk-wise processing (if not already used,
as with MsBackendMzR).
Spectra uses the BiocParallel package for parallel
processing. The parallel processing setup can be configured globally by
registering the preferred setup using the register() function (e.g.
register(SnowParam(4)) to use socket-based parallel processing on Windows
using 4 different R processes). Parallel processing can be disabled by setting
register(SerialParam()).
Chunk-wise processing will by default run in parallel, depending on the configured parallel processing setup.
Parallel processing (and also chunk-wise processing) have a computational overhead, because the data needs to be split and merged. Thus, for some operations or data sets avoiding this mechanism can be more efficient (e.g. for in-memory backends or small data sets).
Spectra functions supporting or using parallel processingSome functions allow to configure parallel processing using a dedicated parameter that allows to define how to split the data for parallel (or chunk-wise) processing. These functions are:
applyProcessing(): parameter f (defaults to
processingChunkFactor(object)) can be used to define how to split and
process the data in parallel.combineSpectra(): parameter p (defaults to x$dataStorage) defines how
the data should be split and processed in parallel.estimatePrecursorIntensity(): parameter f (defaults to dataOrigin(x))
defines the splitting and processing. This should represent the original data
files the spectra data derives from.intensity(): parameter f (defaults to processingChunkFactor(object))
defines if and how the data should be split for parallel processing.mz(): parameter f (defaults to processingChunkFactor(object))
defines if and how the data should be split for parallel processing.peaksData(): parameter f (defaults to processingChunkFactor(object))
defines if and how the data should be split for parallel processing.setBackend(): parameter f (defaults to processingChunkFactor(object))
defines if and how the data should be split for parallel processing.Functions that perform chunk-wise (parallel) processing natively, i.e., based
on the processingChunkFactor:
containsMz().containsNeutralLoss().ionCount().isCentroided().isEmpty().sessionInfo()## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
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## time zone: America/New_York
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##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] MsCoreUtils_1.16.0 IRanges_2.38.0 Spectra_1.14.1
## [4] ProtGenerics_1.36.0 BiocParallel_1.38.0 S4Vectors_0.42.0
## [7] BiocGenerics_0.50.0 BiocStyle_2.32.0
##
## loaded via a namespace (and not attached):
## [1] cli_3.6.2 knitr_1.46 rlang_1.1.3
## [4] xfun_0.44 ncdf4_1.22 clue_0.3-65
## [7] jsonlite_1.8.8 mzR_2.38.0 htmltools_0.5.8.1
## [10] sass_0.4.9 Biobase_2.64.0 rmarkdown_2.26
## [13] evaluate_0.23 jquerylib_0.1.4 MASS_7.3-60.2
## [16] fastmap_1.2.0 yaml_2.3.8 lifecycle_1.0.4
## [19] bookdown_0.39 BiocManager_1.30.23 cluster_2.1.6
## [22] compiler_4.4.0 codetools_0.2-20 fs_1.6.4
## [25] Rcpp_1.0.12 MetaboCoreUtils_1.12.0 digest_0.6.35
## [28] R6_2.5.1 parallel_4.4.0 bslib_0.7.0
## [31] tools_4.4.0 cachem_1.1.0