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The Spectra class to manage and access MS data

Source: R/Spectra.R
Spectra.Rd

The Spectra class encapsules spectral mass spectrometry (MS) data and related metadata. The MS data is represented by a backend extending the virual MsBackend class which provides the data to the Spectra object. The Spectra class implements only data accessor, filtering and analysis methods for the MS data and relies on its backend to provide the MS data. This allows to change data representations of a Spectra object depending on the user's needs and properties of the data. Different backends and their properties are explained in the MsBackend documentation.

Documentation on other topics and functionality of Spectracan be found in:

Usage

# S4 method for class 'missing'
Spectra(
  object,
  processingQueue = list(),
  metadata = list(),
  ...,
  backend = MsBackendMemory(),
  BPPARAM = bpparam()
)

# S4 method for class 'MsBackend'
Spectra(
  object,
  processingQueue = list(),
  metadata = list(),
  ...,
  BPPARAM = bpparam()
)

# S4 method for class 'character'
Spectra(
  object,
  processingQueue = list(),
  metadata = list(),
  source = MsBackendMzR(),
  backend = source,
  ...,
  BPPARAM = bpparam()
)

# S4 method for class 'ANY'
Spectra(
  object,
  processingQueue = list(),
  metadata = list(),
  source = MsBackendMemory(),
  backend = source,
  ...,
  BPPARAM = bpparam()
)

# S4 method for class 'Spectra,MsBackend'
setBackend(
  object,
  backend,
  f = processingChunkFactor(object),
  ...,
  BPPARAM = bpparam()
)

# S4 method for class 'Spectra'
export(object, backend, ...)

# S4 method for class 'Spectra'
dataStorageBasePath(object)

# S4 method for class 'Spectra'
dataStorageBasePath(object) <- value

Arguments

object

For Spectra(): an object to instantiate the Spectra object and initialize the with data.. See section on creation of Spectra objects for details. For all other methods a Spectra object.

processingQueue

For Spectra(): optional list of ProcessingStep objects.

metadata

For Spectra(): optional list with metadata information.

...

Additional arguments.

backend

For Spectra(): MsBackend to be used as backend. See section on creation of Spectra objects for details. For setBackend(): instance of MsBackend that supports setBackend() (i.e. for which supportsSetBackend() returns TRUE). Such backends have a parameter data in their backendInitialize() function that support passing the full spectra data to the initialize method. See section on creation of Spectra objects for details. For export(): MsBackend to be used to export the data.

BPPARAM

Parallel setup configuration. See bpparam() for more information. This is passed directly to the backendInitialize() method of the MsBackend.

source

For Spectra(): instance of MsBackend that can be used to import spectrum data from the provided files. See section Creation of objects for more details.

f

For setBackend(): factor defining how to split the data for parallelized copying of the spectra data to the new backend. For some backends changing this parameter can lead to errors. Defaults to processingChunkFactor().

value

For dataStorageBasePath(): A character vector that defines the base directory where the data storage files can be found.

Details

The Spectra class uses by default a lazy data manipulation strategy, i.e. data manipulations such as performed with replaceIntensitiesBelow() are not applied immediately to the data, but applied on-the-fly to the spectrum data once it is retrieved. This enables data manipulation operations also for read only data representations. For some backends that allow to write data back to the data storage (such as the MsBackendMemory(), MsBackendDataFrame() and MsBackendHdf5Peaks()) it is possible to apply to queue with the applyProcessing() function (see the applyProcessing() function for details).

Clarifications regarding scan/acquisition numbers and indices:

  • A spectrumId (or spectrumID) is a vendor specific field in the mzML file that contains some information about the run/spectrum, e.g.: controllerType=0 controllerNumber=1 scan=5281 file=2

  • acquisitionNum is a more a less sanitize spectrum id generated from the spectrumId field by mzR (see here).

  • scanIndex is the mzR generated sequence number of the spectrum in the raw file (which doesn't have to be the same as the acquisitionNum)

See also this issue.

Data stored in a Spectra object

The Spectra object is a container for MS data that includes mass peak data (m/z and related intensity values, also referred to as peaks data in the context of Spectra) and metadata of individual spectra (so called spectra variables). While a core set of spectra variables (the coreSpectraVariables()) are guaranteed to be provided by a Spectra, it is possible to add arbitrary additional spectra variables to a Spectra object.

The Spectra object is designed to contain MS data of a (large) set of mass spectra. The data is organized linearly and can be thought of a list of mass spectra, i.e. each element in the Spectra is one spectrum.

Creation of objects

Spectra classes can be created with the Spectra() constructor function which supports the following formats:

  • parameter object is a data.frame or DataFrame containing the full spectrum data (spectra variables in columns as well as columns with the individual MS peak data, m/z and intensity). The provided backend (by default a MsBackendMemory) will be initialized with that data.

  • parameter object is a MsBackend (assumed to be already initialized).

  • parameter object is missing, in which case it is supposed that the data is provided by the MsBackend class passed along with the backend argument.

  • parameter object is of type character and is expected to be the file names(s) from which spectra should be imported. Parameter source allows to define a MsBackend that is able to import the data from the provided source files. The default value for source is MsBackendMzR() which allows to import spectra data from mzML, mzXML or CDF files.

With ... additional arguments can be passed to the backend's backendInitialize() method. Parameter backend allows to specify which MsBackend should be used for data representation and storage.

Data representation of a Spectra

The MS data which can be accessed through the Spectra object is represented by its backend, which means that this backend defines how and where the data is stored (e.g. in memory or on disk). The Specrta object relies on the backend to provide the MS data whenever it needs it for data processing. Different backends with different properties, such as minimal memory requirement or fast data access, are defined in the Spectra package or one of the MsBackend* packages. More information on backends and their properties is provided in the documentation of MsBackend.

On-disk backends keep only a limited amount of data in memory retrieving most of the data (usually the MS peak data) upon request on-the-fly from their on-disk data representations. Moving the on-disk data storage of such a backend or a serialized object to a different location in the file system will cause data corruption. The dataStorageBasePath() and dataStorageBasePath<- functions allow in such cases (and if thebackend classes support this operation), to get or change the base path to the directory of the backend's data storage. In-memory backends such as MsBackendMemory or MsBackendDataFrame keeping all MS data in memory don't support, and need, this function, but for MsBackendMzR this function can be used to update/adapt the path to the directory containing the original data files. Thus, for Spectra objects (using this backend) that were moved to another file system or computer, these functions allow to adjust/adapt the base file path.

Changing data representation of a Spectra

The data representation, i.e. the backend of a Spectra object can be changed with the setBackend() method that takes an instance of the new backend as second parameter backend. A call to setBackend(sps, backend = MsBackendDataFrame()) would for example change the backend of sps to the in-memory MsBackendDataFrame. Changing to a backend is only supported if that backend has a data parameter in its backendInitialize() method and if supportsSetBackend() returns TRUE for that backend. setBackend() will transfer the full spectra data from the originating backend as a DataFrame to the new backend.

Generally, it is not possible to change to a read-only backend such as the MsBackendMzR() backend.

The definition of the function is: setBackend(object, backend, ..., f = dataStorage(object), BPPARAM = bpparam()) and its parameters are:

  • object: the Spectra object.

  • backend: an instance of the new backend, e.g. [MsBackendMemory()].

  • f: factor allowing to parallelize the change of the backends. By default the process of copying the spectra data from the original to the new backend is performed separately (and in parallel) for each file. Users are advised to use the default setting.

  • ...: optional additional arguments passed to the backendInitialize() method of the new backend.

  • BPPARAM: setup for the parallel processing. See bpparam() for details.

Exporting data from a Spectra object

Data from a Spectra object can be exported to a file with the export() function. The actual export of the data is performed by the export method of the MsBackend class defined with the mandatory parameter backend which defines also the format in which the data is exported. Note however that not all backend classes support export of data. From the MsBackend classes in the Spectra package currently only the MsBackendMzR backend supports data export (to mzML/mzXML file(s)); see the help page of the MsBackend for information on its arguments or the examples below or the vignette for examples.

The definition of the function is export(object, backend, ...) and its parameters are:

  • object: the Spectra object to be exported.

  • backend: instance of a class extending MsBackend which supports export of the data (i.e. which has a defined export method).

  • ...: additional parameters specific for the MsBackend passed with parameter backend.

Author

Sebastian Gibb, Johannes Rainer, Laurent Gatto, Philippine Louail

Examples


##  --------  CREATION OF SPECTRA OBJECTS  --------

## Create a Spectra providing a `DataFrame` containing the spectrum data.

spd <- DataFrame(msLevel = c(1L, 2L), rtime = c(1.1, 1.2))
spd$mz <- list(c(100, 103.2, 104.3, 106.5), c(45.6, 120.4, 190.2))
spd$intensity <- list(c(200, 400, 34.2, 17), c(12.3, 15.2, 6.8))

data <- Spectra(spd)
data
#> MSn data (Spectra) with 2 spectra in a MsBackendMemory backend:
#>     msLevel     rtime scanIndex
#>   <integer> <numeric> <integer>
#> 1         1       1.1        NA
#> 2         2       1.2        NA
#>  ... 16 more variables/columns.

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


##  --------  CHANGING DATA REPRESENTATIONS  --------

## The MS data is on disk and will be read into memory on-demand. We can
## however change the backend to a MsBackendMemory backend which will
## keep all of the data in memory.
sciex_im <- setBackend(sciex, MsBackendMemory())
sciex_im
#> 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 07:26:27 2024] 

## The `MsBackendMemory()` supports the `setBackend()` method:
supportsSetBackend(MsBackendMemory())
#> [1] TRUE

## Thus, it is possible to change to that backend with `setBackend()`. Most
## read-only backends however don't support that, such as the
## `MsBackendMzR` and `setBackend()` would fail to change to that backend.
supportsSetBackend(MsBackendMzR())
#> [1] FALSE

## The on-disk object `sciex` is light-weight, because it does not keep the
## MS peak data in memory. The `sciex_im` object in contrast keeps all the
## data in memory and its size is thus much larger.
object.size(sciex)
#> 395976 bytes
object.size(sciex_im)
#> 55802328 bytes

## The spectra variable `dataStorage` returns for each spectrum the location
## where the data is stored. For in-memory objects:
head(dataStorage(sciex_im))
#> [1] "<memory>" "<memory>" "<memory>" "<memory>" "<memory>" "<memory>"

## While objects that use an on-disk backend will list the files where the
## data is stored.
head(dataStorage(sciex))
#> [1] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [2] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [3] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [4] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [5] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [6] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"

## The spectra variable `dataOrigin` returns for each spectrum the *origin*
## of the data. If the data is read from e.g. mzML files, this will be the
## original mzML file name:
head(dataOrigin(sciex))
#> [1] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [2] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [3] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [4] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [5] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [6] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
head(dataOrigin(sciex_im))
#> [1] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [2] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [3] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [4] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [5] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
#> [6] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"


##  -------- DATA EXPORT  --------

## Some `MsBackend` classes provide an `export()` method to export the data
## to the file format supported by the backend.
## The `MsBackendMzR` for example allows to export MS data to mzML or
## mzXML file(s), the `MsBackendMgf` (defined in the MsBackendMgf R package)
## would allow to export the data in mgf file format.
## Below we export the MS data in `data`. We call the `export()` method on
## this object, specify the backend that should be used to export the data
## (and which also defines the output format) and provide a file name.
fl <- tempfile()
export(data, MsBackendMzR(), file = fl)

## This exported our data in mzML format. Below we read the first 6 lines
## from that file.
readLines(fl, n = 6)
#> [1] "<?xml version=\"1.0\" encoding=\"utf-8\"?>"                                                                                                                                                                                            
#> [2] "<indexedmzML xmlns=\"http://psi.hupo.org/ms/mzml\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://psi.hupo.org/ms/mzml http://psidev.info/files/ms/mzML/xsd/mzML1.1.2_idx.xsd\">"                 
#> [3] "  <mzML xmlns=\"http://psi.hupo.org/ms/mzml\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://psi.hupo.org/ms/mzml http://psidev.info/files/ms/mzML/xsd/mzML1.1.0.xsd\" id=\"\" version=\"1.1.0\">"
#> [4] "    <cvList count=\"2\">"                                                                                                                                                                                                              
#> [5] "      <cv id=\"MS\" fullName=\"Proteomics Standards Initiative Mass Spectrometry Ontology\" version=\"4.1.163\" URI=\"https://raw.githubusercontent.com/HUPO-PSI/psi-ms-CV/master/psi-ms.obo\"/>"                                      
#> [6] "      <cv id=\"UO\" fullName=\"Unit Ontology\" version=\"09:04:2014\" URI=\"https://raw.githubusercontent.com/bio-ontology-research-group/unit-ontology/master/unit.obo\"/>"                                                           

## If only a single file name is provided, all spectra are exported to that
## file. To export data with the `MsBackendMzR` backend to different files, a
## file name for each individual spectrum has to be provided.
## Below we export each spectrum to its own file.
fls <- c(tempfile(), tempfile())
export(data, MsBackendMzR(), file = fls)

## Reading the data from the first file
res <- Spectra(backendInitialize(MsBackendMzR(), fls[1]))

mz(res)
#> NumericList of length 1
#> [[1]] 100 103.2 104.3 106.5
mz(data)
#> NumericList of length 2
#> [[1]] 100 103.2 104.3 106.5
#> [[2]] 45.6 120.4 190.2