# Large-scale data handling and processing with Spectra

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), Guillaume Deflandre [ctb] (ORCID: https://orcid.org/0009-0008-1257-2416), Ahlam Mentag [ctb] (ORCID: https://orcid.org/0009-0008-5438-7067)
Last modified: 2026-06-08 22:34:23.504114
Compiled: Mon Jun 8 22:36:08 2026

# Introduction

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.

# Memory requirements of different data representations

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 provided through the MsDataHub package into a `Spectra` object using an `MsBackendMzR` backend.

    library(Spectra)

    #' Define the file names from which to import the data
    fls <- c(
        MsDataHub::PestMix1_DDA.mzML(),
        MsDataHub::PestMix1_SWATH.mzML(),
        MsDataHub::X20171016_POOL_POS_1_105.134.mzML(),
        MsDataHub::X20171016_POOL_POS_3_105.134.mzML())

    ## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

    ## downloading 1 resources

    ## retrieving 1 resource

    ## loading from cache

    ## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

    ## downloading 1 resources

    ## retrieving 1 resource

    ## loading from cache

    ## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

    ## downloading 1 resources

    ## retrieving 1 resource

    ## loading from cache

    ## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

    ## downloading 1 resources

    ## retrieving 1 resource

    ## loading from cache

    #' 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
    ##  ... 34 more variables/columns.
    ##
    ## file(s):
    ## 14063e56950a_7861
    ## 14067d671a5_7862
    ## 1406348e7f56_7859
    ##  ... 1 more files

The 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")

    ## 5.3 Mb

    print(object.size(sps_mem), units = "MB")

    ## 140.2 Mb

Keeping 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())

# Chunk-wise and parallel processing

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()

    ##
    ## /github/home/.cache/R/ExperimentHub/14063e56950a_7861
    ##                                                  7602
    ##  /github/home/.cache/R/ExperimentHub/14067d671a5_7862
    ##                                                  8999
    ## /github/home/.cache/R/ExperimentHub/1406348e7f56_7859
    ##                                                   931
    ## /github/home/.cache/R/ExperimentHub/140645289943_7860
    ##                                                   931

The 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  463

After 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  463

The `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.

# Notes and suggestions for parallel or chunk-wise processing

- 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 processing

Some 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()`.

# Session information

    sessionInfo()

    ## R version 4.6.0 (2026-04-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 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/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## 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
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ## [1] MsDataHub_1.12.0    MsCoreUtils_1.24.0  IRanges_2.46.0
    ## [4] Spectra_1.22.2      BiocParallel_1.46.0 S4Vectors_0.50.1
    ## [7] BiocGenerics_0.58.1 generics_0.1.4      BiocStyle_2.40.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] KEGGREST_1.52.0        xfun_0.58              bslib_0.11.0
    ##  [4] httr2_1.2.2            Biobase_2.72.0         vctrs_0.7.3
    ##  [7] tools_4.6.0            curl_7.1.0             parallel_4.6.0
    ## [10] tibble_3.3.1           AnnotationDbi_1.74.0   RSQLite_3.53.1
    ## [13] cluster_2.1.8.2        blob_1.3.0             pkgconfig_2.0.3
    ## [16] data.table_1.18.4      dbplyr_2.5.2           lifecycle_1.0.5
    ## [19] compiler_4.6.0         Biostrings_2.80.1      Seqinfo_1.2.0
    ## [22] codetools_0.2-20       ncdf4_1.24             clue_0.3-68
    ## [25] htmltools_0.5.9        sys_3.4.3              buildtools_1.0.0
    ## [28] sass_0.4.10            yaml_2.3.12            crayon_1.5.3
    ## [31] pillar_1.11.1          jquerylib_0.1.4        MASS_7.3-65
    ## [34] cachem_1.1.0           MetaboCoreUtils_1.20.1 ExperimentHub_3.2.0
    ## [37] AnnotationHub_4.2.0    tidyselect_1.2.1       digest_0.6.39
    ## [40] purrr_1.2.2            dplyr_1.2.1            BiocVersion_3.23.1
    ## [43] maketools_1.3.2        fastmap_1.2.0          cli_3.6.6
    ## [46] magrittr_2.0.5         withr_3.0.2            filelock_1.0.3
    ## [49] rappdirs_0.3.4         bit64_4.8.2            XVector_0.52.0
    ## [52] rmarkdown_2.31         httr_1.4.8             bit_4.6.0
    ## [55] otel_0.2.0             png_0.1-9              memoise_2.0.1
    ## [58] evaluate_1.0.5         knitr_1.51             BiocFileCache_3.2.0
    ## [61] rlang_1.2.0            Rcpp_1.1.1-1.1         glue_1.8.1
    ## [64] DBI_1.3.0              mzR_2.46.0             BiocManager_1.30.27
    ## [67] jsonlite_2.0.0         R6_2.6.1               fs_2.1.0
    ## [70] ProtGenerics_1.44.0