MsBackend classesPackage: Spectra
Authors: RforMassSpectrometry Package Maintainer [cre],
Laurent Gatto [aut] (https://orcid.org/0000-0002-1520-2268),
Johannes Rainer [aut] (https://orcid.org/0000-0002-6977-7147),
Sebastian Gibb [aut] (https://orcid.org/0000-0001-7406-4443),
Jan Stanstrup [ctb] (https://orcid.org/0000-0003-0541-7369)
Last modified: 2023-04-28 14:07:07.293195
Compiled: Mon Sep 25 18:40:15 2023
This vignette briefly describes the MsBackend class which is used by the
Spectra package to represent and provide Mass Spectrometry (MS) data and
illustrates how a new such backend class can be created and tested for
validity.
Contributions to this vignette (content or correction of typos) or requests for additional details and information are highly welcome (ideally via pull requests or github issues).
MsBackend?The Spectra package separates the code for the analysis of MS data from the
code needed to import, represent and provide the data. The former is implemented
for the Spectra class which is the main object users will use for their
analyses. The Spectra object relies on a so-called backend to provide the MS
data. The MsBackend virtual class defines the API that new backend classes
need to implement in order to be used with the Spectra object. Each Spectra
object contains an implementation of such a MsBackend within its @backend
slot which provides the MS data to the Spectra object. All data management is
thus hidden from the user. In addition this separation allows to define new,
alternative, data representations and integrate them seamlessly into a
Spectra-based data analysis workflow.
This concept is an extension of the of in-memory and on-disk data representations from the MSnbase package (Gatto, Gibb, and Rainer 2020).
General conventions for MS data of a Spectra are:
Spectra object is supposed to contain MS (spectral) data of multiple
MS spectra.NA) for m/z values are not supported.coreSpectraVariables() function.dataStorage and dataOrigin are two special spectra variables that define
for each spectrum where the data is stored and from where the data derived (or
was loaded, such as the data origin). Both are expected to be of
typecharacter and need to be defined by the backend (i.e., they can not be
empty or missing).MsBackend implementations can also represent purely read-only data
resources. In this case only data accessor methods need to be implemented but
not data replacement methods. Whether a backend is read-only can be set with
the @readonly slot of the virtual MsBackend class (the isReadOnly
function can be used to retrieve the value for this slot). The default is
@readonly = FALSE and thus all data replacement method listed in section
Data replacement methods have to be implemented. For read-only backends
(@readonly = TRUE) only the methods in section Required methods need to be
implemented. Backends can also be partially read-only, such as the
MsBackendMzR. This backend allows for example to change spectra variables,
but not the peaks data (i.e. the m/z and intensity values). Also, backends for
purely read-only resources could extend the MsBackendCached from the
Spectra package to enable support for modifying (or adding)
spectra variables. Any changes to spectra variables will be internally cached
by the MsBackendCached without the need of them being propagating to the
underlying data resource (see for example the MsBackendMassbankSql from the
MsBackendMassbank package).The default parallel processing of Spectra splits backends by their
dataStorage and performs the processing in parallel on the subsets. Not all
backends will support such parallel processing (or any parallel processing) and
thus unexpected errors might occur (depending on the user’s settings). A new
function allowing to evaluate the parallel processing capabilities of a backend
will be added to help avoiding such issues.
The MsBackend class defines core methods that have to be implemented by a MS
backend as well as optional methods with default implementations that might
be implemented for a new backend but don’t necessarily have to. These functions
are described in sections Required methods and Optional methods,
respectively.
To create a new backend a class extending the virtual MsBackend needs to be
implemented. In the example below we create thus a simple class with a
data.frame to contain general spectral properties (spectra variables) and
two slots for m/z and intensity values. These are stored as NumericList
objects since both m/z and intensity values are expected to be of type numeric
and to allow to store data from multiple spectra into a single backend
object. We also define a simple constructor function that returns an empty
instance of our new class.
library(Spectra)
library(IRanges)
setClass("MsBackendTest",
contains = "MsBackend",
slots = c(
spectraVars = "data.frame",
mz = "NumericList",
intensity = "NumericList"
),
prototype = prototype(
spectraVars = data.frame(),
mz = NumericList(compress = FALSE),
intensity = NumericList(compress = FALSE)
))
MsBackendTest <- function() {
new("MsBackendTest")
}The 3 slots spectraVars, mz and intensity will be used to store our MS
data, each row in spectraVars being data for one spectrum with the columns
being the different spectra variables (i.e. additional properties of a
spectrum such as its retention time or MS level) and each element in mz and
intensity being a numeric with the m/z and intensity values of the
respective spectrum.
We should ideally also add some basic validity function that ensures the data to
be OK. The function below simply checks that the number of rows of the
spectraVars slot matches the length of the mz and intensity slot.
setValidity("MsBackendTest", function(object) {
if (length(object@mz) != length(object@intensity) ||
length(object@mz) != nrow(object@spectraVars))
return("length of 'mz' and 'intensity' has to match the number of ",
"rows of 'spectraVars'")
NULL
})## Class "MsBackendTest" [in ".GlobalEnv"]
##
## Slots:
##
## Name: spectraVars mz intensity readonly version
## Class: data.frame NumericList NumericList logical character
##
## Extends: "MsBackend"We can now create an instance of our new class with the MsBackendTest
function.
MsBackendTest()## An object of class "MsBackendTest"
## Slot "spectraVars":
## data frame with 0 columns and 0 rows
##
## Slot "mz":
## NumericList of length 0
##
## Slot "intensity":
## NumericList of length 0
##
## Slot "readonly":
## [1] FALSE
##
## Slot "version":
## [1] "0.1"Note that a backend class does not necessarily need to contain all the data
like the one from our example. Backends such as the MsBackendMzR for example
retrieve the data on the fly from the raw MS data files or the MsBackendSql
from the MsBackendSql a SQL database.
Methods listed in this section must be implemented for a new class extending
MsBackend. Methods should ideally also implemented in the order they are
listed here. Also, it is strongly advised to write dedicated unit tests for
each newly implemented method or function already during the development.
dataStorageThe dataStorage spectra variable of a spectrum provides some information how
or where the data is stored. The dataStorage method should therefor return a
character vector with length equal to the number of spectra of a backend
object with that information. For most backends the data storage information can
be a simple string such as "memory" or "database" to specify that the data
of a spectrum is stored within the object itself or in a database,
respectively.
Backend classes that keep only a subset of the MS data in memory and need to
load data from data files upon request will use this spectra variable to store
and keep track of the original data file for each spectrum. An example is the
MsBackendMzR backend that retrieves the MS data on-the-fly from the original
data file(s) whenever m/z or intensity values are requested from the
backend. Calling dataStorage on an MsBackendMzR returns thus the names from
the originating files.
For our example backend we define a simple dataStorage method that simply
returns the column "dataStorage" from the @svars (as a character).
setMethod("dataStorage", "MsBackendTest", function(object) {
as.character(object@spectraVars$dataStorage)
})lengthlength is expected to return a single integer with the total number of
spectra that are available through the backend class. For our example backend we
simply return the number of rows of the data.frame stored in the
@spectraVars slot.
setMethod("length", "MsBackendTest", function(x) {
nrow(x@spectraVars)
})backendInitializeThe backendInitialize method is expected to be called after creating an
instance of the backend class and should prepare (initialize) the backend which
in most cases means that MS data is loaded. This method can take any parameters
needed by the backend to get loaded/initialized with data (which can be file
names from which to load the data, a database connection or object(s) containing
the data). During backendInitialize usually also the special spectra variables
dataStorage and dataOrigin are set.
Below we define a backendInitialize method that takes as arguments a
data.frame with spectra variables and two lists with the m/z and intensity
values for each spectrum.
setMethod(
"backendInitialize", "MsBackendTest",
function(object, svars, mz, intensity) {
if (!is.data.frame(svars))
stop("'svars' needs to be a 'data.frame' with spectra variables")
if (is.null(svars$dataStorage))
svars$dataStorage <- "<memory>"
if (is.null(svars$dataOrigin))
svars$dataOrigin <- "<user provided>"
object@spectraVars <- svars
object@mz <- NumericList(mz, compress = FALSE)
object@intensity <- NumericList(intensity, compress = FALSE)
validObject(object)
object
})In addition to adding the data to object, the function also defined the
dataStorage and dataOrigin spectra variables. The purpose of these two
variables is to provide some information on where the data is stored (in
memory as in our example) and from where the data is originating. The
dataOrigin would for example allow to specify from which original data files
individual spectra derive.
We can now create an instance of our backend class and fill it with data. We
thus first define our MS data and pass this to the backendInitialize method.
## A data.frame with spectra variables.
svars <- data.frame(msLevel = c(1L, 2L, 2L),
rtime = c(1.2, 1.3, 1.4))
## m/z values for each spectrum.
mzs <- list(c(12.3, 13.5, 16.5, 17.5),
c(45.1, 45.2),
c(64.4, 123.1, 124.1))
## intensity values for each spectrum.
ints <- list(c(123.3, 153.6, 2354.3, 243.4),
c(100, 80.1),
c(12.3, 35.2, 100))
## Create and initialize the backend
be <- backendInitialize(MsBackendTest(),
svars = svars, mz = mzs, intensity = ints)
be## An object of class "MsBackendTest"
## Slot "spectraVars":
## msLevel rtime dataStorage dataOrigin
## 1 1 1.2 <memory> <user provided>
## 2 2 1.3 <memory> <user provided>
## 3 2 1.4 <memory> <user provided>
##
## Slot "mz":
## NumericList of length 3
## [[1]] 12.3 13.5 16.5 17.5
## [[2]] 45.1 45.2
## [[3]] 64.4 123.1 124.1
##
## Slot "intensity":
## NumericList of length 3
## [[1]] 123.3 153.6 2354.3 243.4
## [[2]] 100 80.1
## [[3]] 12.3 35.2 100
##
## Slot "readonly":
## [1] FALSE
##
## Slot "version":
## [1] "0.1"While this method works and is compliant with the MsBackend API (because there
is no requirement on the input parameters for the backendInitialize method),
it would be good practice for backends that are supposed to support replacing
data, to add an optional additional parameter data that would allow passing
the complete MS data (including m/z and intensity values) to the function as a
DataFrame. This would simplify the implementation of some replacement methods
and would in addition also allow to change the backend of a Spectra using the
setBackend to our new backend. We thus re-implement the backendInitialize
method supporting also to initialize the backend with such a data frame and we
also implement a helper function that checks spectra variables for the correct
data type.
#' Helper function to check if core spectra variables have the correct
#' data type.
#'
#' @param x `data.frame` with the data for spectra variables.
#'
#' @param name `character` defining the column names (spectra variables) of `x`
#' for which the correct data type should be evaluated.
.sv_valid_data_type <- function(x, name = colnames(x)) {
sv <- coreSpectraVariables()[names(coreSpectraVariables()) %in% name]
for (i in seq_along(sv)) {
if (!is(x[, names(sv[i])], sv[i]))
stop("Spectra variabe \"", names(sv[i]), "\" is not of type ",
sv[i], call. = FALSE)
}
TRUE
}This function is then used to check the input data in our new
backendInitialize method.
setMethod(
"backendInitialize", "MsBackendTest",
function(object, svars, mz, intensity, data) {
if (!missing(data)) {
svars <- as.data.frame(
data[, !colnames(data) %in% c("mz", "intensity")])
if (any(colnames(data) == "mz"))
mz <- data$mz
if (any(colnames(data) == "intensity"))
intensity <- data$intensity
}
if (!is.data.frame(svars))
stop("'svars' needs to be a 'data.frame' with spectra variables")
if (is.null(svars$dataStorage))
svars$dataStorage <- "<memory>"
if (is.null(svars$dataOrigin))
svars$dataOrigin <- "<user provided>"
.sv_valid_data_type(svars)
object@spectraVars <- svars
object@mz <- NumericList(mz, compress = FALSE)
object@intensity <- NumericList(intensity, compress = FALSE)
validObject(object)
object
})We below create the backend again with the updated backendInitialize.
## Create and initialize the backend
be <- backendInitialize(MsBackendTest(),
svars = svars, mz = mzs, intensity = ints)
be## An object of class "MsBackendTest"
## Slot "spectraVars":
## msLevel rtime dataStorage dataOrigin
## 1 1 1.2 <memory> <user provided>
## 2 2 1.3 <memory> <user provided>
## 3 2 1.4 <memory> <user provided>
##
## Slot "mz":
## NumericList of length 3
## [[1]] 12.3 13.5 16.5 17.5
## [[2]] 45.1 45.2
## [[3]] 64.4 123.1 124.1
##
## Slot "intensity":
## NumericList of length 3
## [[1]] 123.3 153.6 2354.3 243.4
## [[2]] 100 80.1
## [[3]] 12.3 35.2 100
##
## Slot "readonly":
## [1] FALSE
##
## Slot "version":
## [1] "0.1"The backendInitialize method that we implemented for our backend class expects
the user to provide the full MS data. This does however not always have to be
the case. The backendInitialize method of the MsBackendMzR backend takes for
example the file names of the raw mzML, mzXML or CDF files as input and
initializes the backend by importing part of the data from these. Also the
backends defined by the MsBackendMgf or
MsBackendMsp packages work in the same way and thus allow to
import MS data from these specific file formats. The backendInitialize method
of the backend defined in the MsBackendSql on the other hand
takes only the connection to a database containing the data as input and
performs some sanity checks on the data but does not load the data into the
backend. Any subsequent data access is handled by the methods of the backend
class through SQL calls to the database.
The purpose of the backendInitialize method is to initialize and prepare the
data in a way that it can be accessed by a Spectra object (through the
initialized backend class). Whether the data is loaded by the
backendInitialize method into memory or simply referenced to within the
backend class does not matter as long as the backend is able to provide the data
with its accessor methods.
Note also that a backendInitialize function should ideally also perform some
data sanity checks (e.g. whether spectra variables have the correct data type
etc).
spectraVariablesThe spectraVariables method should return a character vector with the names
of all available spectra variables of the backend. While a backend class should
support defining and providing their own spectra variables, each MsBackend
class must provide also the core spectra variables (in the correct data
type). Since not all data file formats provide values for all these spectra
variables they can however also be NA (with the exception of the spectra
variable "dataStorage").
The coreSpectraVariables() function returns the full list of mandatory spectra
variables along with their expected data type.
coreSpectraVariables()## msLevel rtime acquisitionNum
## "integer" "numeric" "integer"
## scanIndex mz intensity
## "integer" "NumericList" "NumericList"
## dataStorage dataOrigin centroided
## "character" "character" "logical"
## smoothed polarity precScanNum
## "logical" "integer" "integer"
## precursorMz precursorIntensity precursorCharge
## "numeric" "numeric" "integer"
## collisionEnergy isolationWindowLowerMz isolationWindowTargetMz
## "numeric" "numeric" "numeric"
## isolationWindowUpperMz
## "numeric"A typical spectraVariables method for a MsBackend class will thus be
implemented similarly to the one for our MsBackendTest test backend: it will
return the union of the core spectra variables and the names for all available
spectra variables within the backend object.
setMethod("spectraVariables", "MsBackendTest", function(object) {
union(names(coreSpectraVariables()), colnames(object@spectraVars))
})
spectraVariables(be)## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "mz" "intensity"
## [7] "dataStorage" "dataOrigin"
## [9] "centroided" "smoothed"
## [11] "polarity" "precScanNum"
## [13] "precursorMz" "precursorIntensity"
## [15] "precursorCharge" "collisionEnergy"
## [17] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [19] "isolationWindowUpperMz"spectraDataThe spectraData method should return the full spectra data within a
backend as a DataFrame object (defined in the S4Vectors
package). The second parameter columns allows to define the names of the
spectra variables that should be returned in the DataFrame. Each row in this
data frame should represent one spectrum, each column a spectra
variable. Columns "mz" and "intensity" (if requested) have to contain each a
NumericList with the m/z and intensity values of the spectra. The DataFrame
must provide values (even if they are NA) for all requested spectra
variables of the backend (including the core spectra variables).
This is now a first problem for our toy backend class, since we keep the spectra
variable data in a simple data.frame without any constraints such as required
columns etc. A simple solution to this (which is also used by all backend
classes in the Spectra package) is to fill missing spectra variables
on-the-fly into the returned DataFrame. We thus define below a simple helper
function that adds columns with missing values (of the correct data type) for
core spectra variables that are not available within the backend to the result.
#' @description Add columns with missing core spectra variables.
#'
#' @param x `data.frame` or `DataFrame` with some spectra variables.
#'
#' @param core_vars `character` with core spectra variable names that should
#' be added to `x` if not already present.
#'
.fill_core_variables <- function(x, core_vars = names(coreSpectraVariables())) {
fill_vars <- setdiff(core_vars, colnames(x))
core_type <- coreSpectraVariables()
n <- nrow(x)
if (length(fill_vars)) {
fill <- lapply(fill_vars, function(z) {
rep(as(NA, core_type[z]), n)
})
names(fill) <- fill_vars
x <- cbind(x, as.data.frame(fill))
}
x
}We next implement the spectraData method that uses this helper function to
fill eventually missing core spectra variables. Note also that this function
should return a DataFrame even for a single column.
setMethod(
"spectraData", "MsBackendTest",
function(object, columns = spectraVariables(object)) {
if (!all(columns %in% spectraVariables(object)))
stop("Some of the requested spectra variables are not available")
## Add m/z and intensity values to the result
res <- DataFrame(object@spectraVars)
res$mz <- object@mz
res$intensity <- object@intensity
## Fill with eventually missing core variables
res <- .fill_core_variables(
res, intersect(columns, names(coreSpectraVariables())))
res[, columns, drop = FALSE]
})As an alternative, we could also initialize the @spectraVars data frame within
the backendInitialize method adding columns for spectra variables that are not
provided by the user and require that this data frame always contains all core
spectra variables. Extracting spectra data (single spectra variables or the full
data) might thus be more efficient then the on-the-fly initialization with
eventual missing spectra variables, but the backend class would also have a
larger memory footprint because even spectra variables with only missing values
for all spectra need to be stored within the object.
We can now use spectraData to either extract the full spectra data from the
backend, or only the data for selected spectra variables.
## Full data
spectraData(be)## DataFrame with 3 rows and 19 columns
## msLevel rtime acquisitionNum scanIndex mz
## <integer> <numeric> <integer> <integer> <NumericList>
## 1 1 1.2 NA NA 12.3,13.5,16.5,...
## 2 2 1.3 NA NA 45.1,45.2
## 3 2 1.4 NA NA 64.4,123.1,124.1
## intensity dataStorage dataOrigin centroided smoothed
## <NumericList> <character> <character> <logical> <logical>
## 1 123.3, 153.6,2354.3,... <memory> <user provided> NA NA
## 2 100.0, 80.1 <memory> <user provided> NA NA
## 3 12.3, 35.2,100.0 <memory> <user provided> NA NA
## polarity precScanNum precursorMz precursorIntensity precursorCharge
## <integer> <integer> <numeric> <numeric> <integer>
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## collisionEnergy isolationWindowLowerMz isolationWindowTargetMz
## <numeric> <numeric> <numeric>
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## isolationWindowUpperMz
## <numeric>
## 1 NA
## 2 NA
## 3 NA## Selected variables
spectraData(be, c("rtime", "mz", "centroided"))## DataFrame with 3 rows and 3 columns
## rtime mz centroided
## <numeric> <NumericList> <logical>
## 1 1.2 12.3,13.5,16.5,... NA
## 2 1.3 45.1,45.2 NA
## 3 1.4 64.4,123.1,124.1 NA## Only missing core spectra variables
spectraData(be, c("centroided", "polarity"))## DataFrame with 3 rows and 2 columns
## centroided polarity
## <logical> <integer>
## 1 NA NA
## 2 NA NA
## 3 NA NApeaksDataThe peaksData method extracts the MS peaks data from a backend, which includes
the m/z and intensity values of each MS peak of a spectrum. These are expected
to be returned as a List of numerical matrices with columns in each matrix
being the requested peaks variables (with the default being "mz" and
"intensity") of one spectrum. Backends must provide at least these two peaks
variables.
Below we implement the peaksData method for our backend. We need to loop over
the @mz and @intensity slots to merge the m/z and intensity of each spectrum
into a matrix. Also, for simplicity reasons, we accept only
c("mz", "intensity") for the columns parameter. This is the expected default
behavior for a MsBackend, but in general the columns parameter is thought to
allow the user to specify which peaks variables should be returned in each
matrix.
setMethod(
"peaksData", "MsBackendTest",
function(object, columns = c("mz", "intensity")) {
if (length(columns) != 2 && columns != c("mz", "intensity"))
stop("'columns' supports only \"mz\" and \"intensity\"")
mapply(mz = object@mz, intensity = object@intensity,
FUN = cbind, SIMPLIFY = FALSE, USE.NAMES = FALSE)
})And with this method we can now extract the peaks data from our backend.
peaksData(be)## [[1]]
## mz intensity
## [1,] 12.3 123.3
## [2,] 13.5 153.6
## [3,] 16.5 2354.3
## [4,] 17.5 243.4
##
## [[2]]
## mz intensity
## [1,] 45.1 100.0
## [2,] 45.2 80.1
##
## [[3]]
## mz intensity
## [1,] 64.4 12.3
## [2,] 123.1 35.2
## [3,] 124.1 100.0The peaksData method is used in many data analysis functions of the Spectra
object to extract the MS data, thus ideally this method should be implemented in
an efficient way. For our backend we need to loop over the lists of m/z and
intensity values which is obviously not ideal. Thus, storing the m/z and
intensity values in separate slots as done in this backend might not be
ideal. The MsBackendMemory backend for example stores the MS data already as
a list of matrices which results in a more efficient peaksData method (but
comes also with a larger overhead when adding, replacing or checking MS data).
Note also that while a backend needs to provide m/z and intensity values,
additional peak variables would also be supported. The MsBackendMemory class
for example allows to store and provide additional peak variables that can then
be added as additional columns to each returned matrix. In this case the
default peaksVariables method should also be overwritten to list the
additionally available variables and the columns parameter of the peaksData
method should allow selection of these additional peaks variables (in addition
to the required "mz" and "intensity" variables).
[The [ method allows to subset MsBackend objects. This operation is expected
to reduce a MsBackend object to the selected spectra. The method should
support to subset by indices or logical vectors and should also support
duplicating elements (i.e. when duplicated indices are used) as well as to
subset in arbitrary order. An error should be thrown if indices are out of
bounds, but the method should also support returning an empty backend with
[integer()]. Note that the MsCoreUtils::i2index function can be used to
check for correct input (and convert the input to an integer index).
Below we implement a possible [ for our test backend class. We ignore the
parameters j from the definition of the [ generic, since we treat our data
to be one-dimensional (with each spectrum being one element).
setMethod("[", "MsBackendTest", function(x, i, j, ..., drop = FALSE) {
i <- MsCoreUtils::i2index(i, length = length(x))
x@spectraVars <- x@spectraVars[i, ]
x@mz <- x@mz[i]
x@intensity <- x@intensity[i]
x
})We can now subset our backend to the last two spectra.
a <- be[2:3]
spectraData(a)## DataFrame with 2 rows and 19 columns
## msLevel rtime acquisitionNum scanIndex mz
## <integer> <numeric> <integer> <integer> <NumericList>
## 1 2 1.3 NA NA 45.1,45.2
## 2 2 1.4 NA NA 64.4,123.1,124.1
## intensity dataStorage dataOrigin centroided smoothed polarity
## <NumericList> <character> <character> <logical> <logical> <integer>
## 1 100.0, 80.1 <memory> <user provided> NA NA NA
## 2 12.3, 35.2,100.0 <memory> <user provided> NA NA NA
## precScanNum precursorMz precursorIntensity precursorCharge collisionEnergy
## <integer> <numeric> <numeric> <integer> <numeric>
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## isolationWindowLowerMz isolationWindowTargetMz isolationWindowUpperMz
## <numeric> <numeric> <numeric>
## 1 NA NA NA
## 2 NA NA NAOr extracting the second spectrum multiple times.
a <- be[c(2, 2, 2)]
spectraData(a)## DataFrame with 3 rows and 19 columns
## msLevel rtime acquisitionNum scanIndex mz intensity
## <integer> <numeric> <integer> <integer> <NumericList> <NumericList>
## 2 2 1.3 NA NA 45.1,45.2 100.0, 80.1
## 2.1 2 1.3 NA NA 45.1,45.2 100.0, 80.1
## 2.2 2 1.3 NA NA 45.1,45.2 100.0, 80.1
## dataStorage dataOrigin centroided smoothed polarity precScanNum
## <character> <character> <logical> <logical> <integer> <integer>
## 2 <memory> <user provided> NA NA NA NA
## 2.1 <memory> <user provided> NA NA NA NA
## 2.2 <memory> <user provided> NA NA NA NA
## precursorMz precursorIntensity precursorCharge collisionEnergy
## <numeric> <numeric> <integer> <numeric>
## 2 NA NA NA NA
## 2.1 NA NA NA NA
## 2.2 NA NA NA NA
## isolationWindowLowerMz isolationWindowTargetMz isolationWindowUpperMz
## <numeric> <numeric> <numeric>
## 2 NA NA NA
## 2.1 NA NA NA
## 2.2 NA NA NAbackendMergeThe backendMerge method merges (combines) MsBackend objects (of the same
type!) into a single instance. For our test backend we thus need to combine the
values in the @spectraVars, @mz and @intensity slots. To support also
merging of data.frames with different set of columns we use the
MsCoreUtils::rbindFill function instead of a simple rbind (this function
joins data frames making an union of all available columns).
setMethod("backendMerge", "MsBackendTest", function(object, ...) {
res <- object
object <- unname(c(object, ...))
res@mz <- do.call(c, lapply(object, function(z) z@mz))
res@intensity <- do.call(c, lapply(object, function(z) z@intensity))
res@spectraVars <- do.call(MsCoreUtils::rbindFill,
lapply(object, function(z) z@spectraVars))
validObject(res)
res
})Again, this implementation which requires 3 loops might not be the most
efficient - but it allows to merge backends of the type MsBackendTest.
a <- backendMerge(be, be[2], be)
a## An object of class "MsBackendTest"
## Slot "spectraVars":
## msLevel rtime dataStorage dataOrigin
## 1 1 1.2 <memory> <user provided>
## 2 2 1.3 <memory> <user provided>
## 3 2 1.4 <memory> <user provided>
## 21 2 1.3 <memory> <user provided>
## 11 1 1.2 <memory> <user provided>
## 22 2 1.3 <memory> <user provided>
## 31 2 1.4 <memory> <user provided>
##
## Slot "mz":
## NumericList of length 7
## [[1]] 12.3 13.5 16.5 17.5
## [[2]] 45.1 45.2
## [[3]] 64.4 123.1 124.1
## [[4]] 45.1 45.2
## [[5]] 12.3 13.5 16.5 17.5
## [[6]] 45.1 45.2
## [[7]] 64.4 123.1 124.1
##
## Slot "intensity":
## NumericList of length 7
## [[1]] 123.3 153.6 2354.3 243.4
## [[2]] 100 80.1
## [[3]] 12.3 35.2 100
## [[4]] 100 80.1
## [[5]] 123.3 153.6 2354.3 243.4
## [[6]] 100 80.1
## [[7]] 12.3 35.2 100
##
## Slot "readonly":
## [1] FALSE
##
## Slot "version":
## [1] "0.1"$The $ method is expected to extract a single spectra variable from a
backend. Parameter name should allow to name the spectra variable to
return. Each MsBackend must support extracting the core spectra variables
with this method (even if no data might be available for that variable). In our
example implementation below we make use of the spectraData method, but more
efficient implementations might be available as well (that would not require to
first subset/create a DataFrame with the full data and to then subset that
again). Also, the $ method should check if the requested spectra variable is
available and should throw an error otherwise.
setMethod("$", "MsBackendTest", function(x, name) {
spectraData(x, columns = name)[, 1L]
})With this we can now extract the MS levels
be$msLevel## [1] 1 2 2or a core spectra variable that is not available within the backend.
be$precursorMz## [1] NA NA NAor also the m/z values
be$mz## NumericList of length 3
## [[1]] 12.3 13.5 16.5 17.5
## [[2]] 45.1 45.2
## [[3]] 64.4 123.1 124.1lengthsThe lengths method is expected to return an integer vector (same length as
the number of spectra in the backend) with the total number of peaks per
spectrum.
For our MsBackendTest we can simply use the lengths method on the m/z or
intensity values for that.
setMethod("lengths", "MsBackendTest", function(x, use.names = FALSE) {
lengths(x@mz, use.names = use.names)
})And we can now get the peaks count per spectrum:
lengths(be)## [1] 4 2 3isEmptyThe isEmpty method is expected to return for each spectrum the information
whether it is empty, i.e. does not contain any MS peaks (and hence m/z or
intensity values). The result of the method has to be a logical of length
equal to the number of spectra represented by the backend with TRUE indicating
whether a spectrum is empty and FALSE otherwise. For our implementation of the
isEmpty method we use the lenghts method defined above that returns the
number of MS peaks per spectrum.
setMethod("isEmpty", "MsBackendTest", function(x) {
lengths(x) == 0L
})
isEmpty(be)## [1] FALSE FALSE FALSEacquisitionNumExtract the acquisitionNum core spectra variable. The method is expected to
return an integer vector with the same length as there are spectra represented
by the backend. For our backend we simply re-use the spectraData method.
setMethod("acquisitionNum", "MsBackendTest", function(object) {
spectraData(object, "acquisitionNum")[, 1L]
})
acquisitionNum(be)## [1] NA NA NAcentroidedExtract for each spectrum the information whether it contains centroided
data. The method is expected to return a logical vector with the same length
as there are spectra represented by the backend.
setMethod("centroided", "MsBackendTest", function(object) {
spectraData(object, "centroided")[, 1L]
})
centroided(be)## [1] NA NA NAcollisionEnergyExtract for each spectrum the collision energy applied to generate the fragment
spectrum. The method is expected to return a numeric vector with the same
length as there are spectra represented by the backend (with NA_real_ for
spectra for which this information is not available, such as MS1 spectra).
setMethod("collisionEnergy", "MsBackendTest", function(object) {
spectraData(object, "collisionEnergy")[, 1L]
})
collisionEnergy(be)## [1] NA NA NAdataOriginExtract the data origin spectra variable for each spectrum. This spectra
variable can be used to store the origin of each spectra. The method is expected
to return a character vector of length equal to the number of spectra
represented by the backend.
setMethod("dataOrigin", "MsBackendTest", function(object) {
spectraData(object, "dataOrigin")[, 1L]
})
dataOrigin(be)## [1] "<user provided>" "<user provided>" "<user provided>"intensityExtract the intensity values for each spectrum in the backend. The result is
expected to be a NumericList of length equal to the number of spectra
represented by the backend. For our test backend we can simply return the
@intensity slot since the data is already stored within a NumericList.
setMethod("intensity", "MsBackendTest", function(object) {
object@intensity
})
intensity(be)## NumericList of length 3
## [[1]] 123.3 153.6 2354.3 243.4
## [[2]] 100 80.1
## [[3]] 12.3 35.2 100isolationWindowLowerMzExtract the core spectra variable isolationWindowLowerMz from the
backend. This information is usually provided for each spectrum in the raw mzML
files. The method is expected to return a numeric vector of length equal to
the number of spectra represented by the backend.
setMethod("isolationWindowLowerMz", "MsBackendTest", function(object) {
spectraData(object, "isolationWindowLowerMz")[, 1L]
})
isolationWindowLowerMz(be)## [1] NA NA NAisolationWindowTargetMzExtract the core spectra variable isolationWindowTargetMz from the
backend. This information is usually provided for each spectrum in the raw mzML
files. The method is expected to return a numeric vector of length equal to
the number of spectra represented by the backend.
setMethod("isolationWindowTargetMz", "MsBackendTest", function(object) {
spectraData(object, "isolationWindowTargetMz")[, 1L]
})
isolationWindowTargetMz(be)## [1] NA NA NAisolationWindowUpperMzExtract the core spectra variable isolationWindowUpperMz from the
backend. This information is usually provided for each spectrum in the raw mzML
files. The method is expected to return a numeric vector of length equal to
the number of spectra represented by the backend.
setMethod("isolationWindowUpperMz", "MsBackendTest", function(object) {
spectraData(object, "isolationWindowUpperMz")[, 1L]
})
isolationWindowUpperMz(be)## [1] NA NA NAmsLevelExtract the MS level for each spectrum in the backend. This method is expected
to return an integer of length equal to the number of spectra represented by
the backend.
setMethod("msLevel", "MsBackendTest", function(object) {
spectraData(object, "msLevel")[, 1L]
})
msLevel(be)## [1] 1 2 2mzExtract the m/z values for each spectrum in the backend. The result is
expected to be a NumericList of length equal to the number of spectra
represented by the backend. Also, the m/z values are expected to be ordered
increasingly for each element (spectrum).
setMethod("mz", "MsBackendTest", function(object) {
object@mz
})
mz(be)## NumericList of length 3
## [[1]] 12.3 13.5 16.5 17.5
## [[2]] 45.1 45.2
## [[3]] 64.4 123.1 124.1polarityExtract the polarity core spectra variable for each spectrum in the
backend. This method is expected to return an integer of length equal to the
number of spectra represented by the backend. Negative and positive polarity are
expected to be encoded by 0L and 1L, respectively.
setMethod("polarity", "MsBackendTest", function(object) {
spectraData(object, "polarity")[, 1L]
})
polarity(be)## [1] NA NA NAprecScanNumExtract the acquisition number of the precursor for each spectrum. This method
is expected to return an integer of length equal to the number of spectra
represented by the backend. For MS1 spectra (or if the acquisition number of the
precursor is not provided) NA_integer_ has to be returned.
setMethod("precScanNum", "MsBackendTest", function(object) {
spectraData(object, "precScanNum")[, 1L]
})
precScanNum(be)## [1] NA NA NAprecursorChargeExtract the charge of the precursor for each spectrum. This method
is expected to return an integer of length equal to the number of spectra
represented by the backend. For MS1 spectra (or if the charge of the
precursor is not provided) NA_integer_ has to be returned.
setMethod("precursorCharge", "MsBackendTest", function(object) {
spectraData(object, "precursorCharge")[, 1L]
})
precursorCharge(be)## [1] NA NA NAprecursorIntensityExtract the intensity of the precursor for each spectrum. This method is
expected to return an numeric of length equal to the number of spectra
represented by the backend. For MS1 spectra (or if the precursor intensity for a
fragment spectrum is not provided) NA_real_ has to be returned.
setMethod("precursorIntensity", "MsBackendTest", function(object) {
spectraData(object, "precursorIntensity")[, 1L]
})
precursorIntensity(be)## [1] NA NA NAprecursorMzExtract the precursor m/z for each spectrum. This method is
expected to return an numeric of length equal to the number of spectra
represented by the backend. For MS1 spectra (or if the precursor m/z for a
fragment spectrum is not provided) NA_real_ has to be returned.
setMethod("precursorMz", "MsBackendTest", function(object) {
spectraData(object, "precursorMz")[, 1L]
})
precursorMz(be)## [1] NA NA NArtimeExtract the retention time of each spectrum. This method is expected to return a
numeric of length equal to the number of spectra represented by the backend.
setMethod("rtime", "MsBackendTest", function(object) {
spectraData(object, "rtime")[, 1L]
})
rtime(be)## [1] 1.2 1.3 1.4scanIndexExtract the scan index core spectra variable. The scan index represents the
relative index of the spectrum within the respective raw data file and can be
different than the acquisitionNum (which is the index of a spectrum as
recorded by the MS instrument). This method is expected to return a integer of
length equal to the number of spectra represented by the backend.
setMethod("scanIndex", "MsBackendTest", function(object) {
spectraData(object, "scanIndex")[, 1L]
})
scanIndex(be)## [1] NA NA NAsmoothedExtract the smoothed core spectra variable that indicates whether a spectrum
was smoothed. This variable is supported for backward compatibility but
seldomly used. The method is expected to return a logical with length equal to
the number of spectra represented by the backend.
setMethod("smoothed", "MsBackendTest", function(object) {
spectraData(object, "smoothed")[, 1L]
})
smoothed(be)## [1] NA NA NAspectraNamesThe spectraNames can be used to extract (optional) names (or IDs) for
individual spectra of a backend, or NULL if not set. For our test backend we
can use the rownames of the @spectraVars slot to store spectra names.
setMethod("spectraNames", "MsBackendTest", function(object) {
rownames(object@spectraVars)
})
spectraNames(be)## [1] "1" "2" "3"These are all the methods that need to be implemented for a valid read-only
MsBackend class and running a test on such an object as described in section
Testing the validity of the backend should not produce any errors. For
backends that support also data replacement also the methods listed in the next
section need to be implemented.
ticThe tic method should return the total ion count (i.e. the sum of intensities)
for each spectrum. This information is usually also provided by the raw MS data
files, but can also be calculated on the fly from the data. The parameter
initial (which is by default TRUE) allows to define whether the provided
original tic should be returned (for initial = TRUE) or whether the tic
should be calculated on the actual data (initial = FALSE). The original tic
values are usually provided by a spectra variable "totIonCurrent". Thus, for
initial = TRUE, in our implementation below we return the value of such a
spectra variable it it is avaialble or NA if it is not.
setMethod("tic", "MsBackendTest", function(object, initial = TRUE) {
if (initial) {
if (any(spectraVariables(object) == "totIonCurrent"))
spectraData(object, "totIonCurrent")[, 1L]
else rep(NA_real_, length(object))
} else vapply(intensity(object), sum, numeric(1), na.rm = TRUE)
})We can now either return the original (initial) TIC (which is not available).
tic(be)## [1] NA NA NAOr calculate the TIC based on the actual intensity values.
tic(be, initial = FALSE)## [1] 2874.6 180.1 147.5As stated in the general description, MsBackend implementations can also be
purely read-only resources allowing to just access data, but not to replace
the data. Thus, it is not strictly required to implement these methods, but for
a fully functional backend it is suggested (as much as possible). A backend for
a purely read-only MS data resource might even extend the MsBackendCached
backend defined in the Spectra package that provides a mechanism to cache
(spectra variable) data in a data.frame within the object. The
MsBackendMassbankSql implemented in the MsBackendMassbank
package extends for example this backend and thus allows modifying some spectra
variables without changing the original data in the MassBank SQL database.
spectraData<-The spectraData<- method should allow to replace the data within a
backend. The method should take a DataFrame with the full data as input value
and is expected to replace the full data within the backend, i.e. all
spectra variables as well as peak data. Also, importantly, the number of spectra
before and after calling the spectraData<- method on an object has to be the
same. For our implementation we can make use of the optional parameter data
that we added to the backendInitialize method and that allows to fill a
MsBackendTest object with the full data.
setReplaceMethod("spectraData", "MsBackendTest", function(object, value) {
if (!inherits(value, "DataFrame"))
stop("'value' is expected to be a 'DataFrame'")
if (length(object) && length(object) != nrow(value))
stop("'value' has to be a 'DataFrame' with ", length(object), " rows")
object <- backendInitialize(MsBackendTest(), data = value)
object
})To test this new method we extract the full spectra data, add an additional column (spectra variable) and replace the data again.
d <- spectraData(be)
d$new_col <- c("a", "b", "c")
spectraData(be) <- d
be$new_col## [1] "a" "b" "c"intensity<-The intensity<- method should allow to replace the intensity values of all
spectra in a backend. This method is expected to only replace the values of
the intensities, but must not change the number of intensities (and hence peaks)
of a spectrum (that could be done with the peaksData<- method that allows to
replace intensity and m/z values at the same time). The value for the
method should ideally be a NumericList to ensure that all intensity values are
indeed numeric. In addition to the method we implement also a simple helper
function that checks for the correct length of value. Each data replacement
method needs to check for that and this function thus reduces code duplication.
.match_length <- function(x, y) {
if (length(x) != length(y))
stop("Length of 'value' has to match the length of 'object'")
}
setReplaceMethod("intensity", "MsBackendTest", function(object, value) {
.match_length(object, value)
if (!(is.list(value) || inherits(value, "NumericList")))
stop("'value' has to be a list or NumericList")
if (!all(lengths(value) == lengths(mz(object))))
stop("lengths of 'value' has to match the number of peaks per spectrum")
if (!inherits(value, "NumericList"))
value <- NumericList(value, compress = FALSE)
object@intensity <- value
object
})We could now use this method to replace the intensities in our backend with modified intensities.
intensity(be)## NumericList of length 3
## [[1]] 123.3 153.6 2354.3 243.4
## [[2]] 100 80.1
## [[3]] 12.3 35.2 100intensity(be) <- intensity(be) - 10
intensity(be)## NumericList of length 3
## [[1]] 113.3 143.6 2344.3 233.4
## [[2]] 90 70.1
## [[3]] 2.3 25.2 90mz<-The mz<- method should allow to replace the m/z values of all spectra in a
backend. The implementation can be the same as for the intensity<-
method. m/z values within each spectrum need to be increasingly ordered. We thus
also check that this is the case for the provided m/z values. We take here the
advantage that a efficient is.unsorted implementation for NumericList is
already available, which is faster than e.g. calling
vapply(mz(be), is.unsorted, logical(1)).
setReplaceMethod("mz", "MsBackendTest", function(object, value) {
.match_length(object, value)
if (!(is.list(value) || inherits(value, "NumericList")))
stop("'value' has to be a list or NumericList")
if (!all(lengths(value) == lengths(mz(object))))
stop("lengths of 'value' has to match the number of peaks per spectrum")
if (!inherits(value, "NumericList"))
value <- NumericList(value, compress = FALSE)
if (any(is.unsorted(value)))
stop("m/z values need to be increasingly sorted within each spectrum")
object@mz <- value
object
})peaksData<-The peaksData<- should allow to replace the peaks data (m/z and intensity
values) of all spectra in a backend. In contrast to the mz<- and intensity<-
methods this method should also support changing the number of peaks per
spectrum (e.g. due to filtering). Parameter value has to be a list of
matrix objects with columns "mz" and "intensity". The length of this list
has to match the number of spectra in the backend. In the implementation for our
backend class we need to loop over this list to extract the m/z and intensity
values and assign them to the @mz and @intensity slots.
setReplaceMethod("peaksData", "MsBackendTest", function(object, value) {
if (!(is.list(value) || inherits(value, "SimpleList")))
stop("'value' has to be a list-like object")
.match_length(object, value)
object@mz <- NumericList(lapply(value, "[", , "mz"), compress = FALSE)
object@intensity <- NumericList(lapply(value, "[", , "intensity"),
compress = FALSE)
validObject(object)
object
})Using the peaksData<- method we can now also for example remove peaks.
pd <- peaksData(be)
## Remove the first peak from the first spectrum
pd[[1L]] <- pd[[1L]][-1L, ]
lengths(be)## [1] 4 2 3peaksData(be) <- pd
lengths(be)## [1] 3 2 3$<-The $<- method should allow to replace values for spectra variables or also to
add additional spectra variables to the backend. As with all replacement
methods, the length of value has to match the number of spectra represented
by the backend.
setReplaceMethod("$", "MsBackendTest", function(x, name, value) {
.match_length(x, value)
if (name == "mz") {
mz(x) <- value
} else if (name == "intensity") {
intensity(x) <- value
} else {
x@spectraVars[[name]] <- value
}
.sv_valid_data_type(x@spectraVars, name)
x
})We can now replace for example existing spectra variables:
msLevel(be)## [1] 1 2 2be$msLevel <- c(2L, 1L, 2L)
msLevel(be)## [1] 2 1 2Or even add new spectra variables.
be$new_var <- c("a", "b", "c")
be$new_var## [1] "a" "b" "c"Replacement methods for all core spectra variables can be implemented similarly.
selectSpectraVariablesThe selectSpectraVariables should allow to reduce the information within the
backend (parameter object) to the selected spectra variables (parameter
spectraVariables). This is equivalent to a subset by columns/variables. For
core spectra variables, if not specified by parameter spectraVariables, only
their values are expected to be removed (since core spectra variables are
expected to be available even if they are not defined within a backend). The
implementation for our backend will remove any columns in the @spectraVars
data frame not defined in the spectraVariables parameter. Special care is
given to the "mz" and "intensity" spectra variables: if they are not
selected, the @mz and @intensity slots are initialized with empty
NumericList (of length matching the number of spectra). Note also that some
backends might throw an error if a spectra variable required for the backend is
removed (such as "dataStorage" for a MsBackendMzR backend, which is
required by the backend to allow retrieval of m/z and intensity values).
setMethod(
"selectSpectraVariables", "MsBackendTest",
function(object, spectraVariables = spectraVariables(object)) {
keep <- colnames(object@spectraVars) %in% spectraVariables
object@spectraVars <- object@spectraVars[, keep, drop = FALSE]
if (!any(spectraVariables == "mz"))
object@mz <- NumericList(vector("list", length(object)),
compress = FALSE)
if (!any(spectraVariables == "intensity"))
object@intensity <- NumericList(vector("list", length(object)),
compress = FALSE)
validObject(object)
object
})We can now use selectSpectraVariables to remove for example the spectra
variable "new_var" added above.
be2 <- be
be2 <- selectSpectraVariables(be2, c("msLevel", "rtime", "mz",
"intensity", "dataStorage"))
spectraVariables(be2)## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "mz" "intensity"
## [7] "dataStorage" "dataOrigin"
## [9] "centroided" "smoothed"
## [11] "polarity" "precScanNum"
## [13] "precursorMz" "precursorIntensity"
## [15] "precursorCharge" "collisionEnergy"
## [17] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [19] "isolationWindowUpperMz"The spectra variable "new_var" is now no longer be available. Note however
that still all core spectra variables are listed, even if they were not
selected with the spectraVariables parameter. While these variables (such as
"dataOrigin") are still listed by spectraVariables(be2), their actual values
have been removed:
dataOrigin(be)## [1] "<user provided>" "<user provided>" "<user provided>"dataOrigin(be2)## [1] NA NA NAIf "mz" and "intensitity" are not selected, the m/z and intensity values get
removed.
be2 <- selectSpectraVariables(be2, c("msLevel", "rtime", "dataStorage"))
mz(be2)## NumericList of length 3
## [[1]] numeric(0)
## [[2]] numeric(0)
## [[3]] numeric(0)intensity(be2)## NumericList of length 3
## [[1]] numeric(0)
## [[2]] numeric(0)
## [[3]] numeric(0)centroided<-Replace the value for the centroided core spectra variable. The provided data
type must be a logical. We re-use the function .sv_valid_data_type which
was defined above for backendInitialize to check for the correct data type of
core spectra variables.
setReplaceMethod("centroided", "MsBackendTest", function(object, value) {
object@spectraVars[["centroided"]] <- value
.sv_valid_data_type(object@spectraVars, "centroided")
object
})Alternatively, we could also simply re-use the $<- replacement method
above. This would make the whole code base for our backend cleaner as replacing
or adding spectra variables would be handled in a single central function.
setReplaceMethod("centroided", "MsBackendTest", function(object, value) {
object$centroided <- value
object
})centroided(be) <- c(TRUE, FALSE, TRUE)
centroided(be)## [1] TRUE FALSE TRUEcollisionEnergy<-Replace the values for the collision energy. Parameter value has to be of type
numeric.
setReplaceMethod("collisionEnergy", "MsBackendTest", function(object, value) {
object$collisionEnergy <- value
object
})collisionEnergy(be) <- c(NA_real_, 20.0, 20.0)
collisionEnergy(be)## [1] NA 20 20dataOrigin<-Replace the values for the data origin spectra variable. Parameter value has
to be of type character.
setReplaceMethod("dataOrigin", "MsBackendTest", function(object, value) {
object$dataOrigin <- value
object
})dataOrigin(be)## [1] "<user provided>" "<user provided>" "<user provided>"dataOrigin(be) <- c("unknown", "file a", "file b")
dataOrigin(be)## [1] "unknown" "file a" "file b"dataStorage<-Replace the values for the data storage spectra variable. Parameter value has
to be of type character. Since our backend does not really make any use of
this spectra variable, we can accept any character value. For other backends,
that for example need to load data on-the-fly from data files, this spectra
variable could be used to store the name of the data files and hence we would
need to perform some additional checks within this replacement function.
setReplaceMethod("dataStorage", "MsBackendTest", function(object, value) {
object$dataStorage <- value
object
})dataStorage(be)## [1] "<memory>" "<memory>" "<memory>"dataStorage(be) <- c("", "", "")
dataStorage(be)## [1] "" "" ""isolationWindowLowerMz<-Replace the values for the isolation window lower m/z spectra
variable. Parameter value has to be of type numeric (NA_real_ missing
values are supported, e.g. for MS1 spectra).
setReplaceMethod(
"isolationWindowLowerMz", "MsBackendTest", function(object, value) {
object$isolationWindowLowerMz <- value
object
})isolationWindowLowerMz(be) <- c(NA_real_, 245.3, NA_real_)
isolationWindowLowerMz(be)## [1] NA 245.3 NAisolationWindowTargetMz<-Replace the values for the isolation window target m/z spectra
variable. Parameter value has to be of type numeric (NA_real_ missing
values are supported, e.g. for MS1 spectra).
setReplaceMethod(
"isolationWindowTargetMz", "MsBackendTest", function(object, value) {
object$isolationWindowTargetMz <- value
object
})isolationWindowTargetMz(be) <- c(NA_real_, 245.4, NA_real_)
isolationWindowTargetMz(be)## [1] NA 245.4 NAisolationWindowUpperMz<-Replace the values for the isolation window upper m/z spectra
variable. Parameter value has to be of type numeric (NA_real_ missing
values are supported, e.g. for MS1 spectra).
setReplaceMethod(
"isolationWindowUpperMz", "MsBackendTest", function(object, value) {
object$isolationWindowUpperMz <- value
object
})isolationWindowUpperMz(be) <- c(NA_real_, 245.5, NA_real_)
isolationWindowUpperMz(be)## [1] NA 245.5 NAmsLevel<-Replace the MS level of spectra in a backend. Parameter value has to be of
type integer. Missing values (NA_integer_) are supported.
setReplaceMethod("msLevel", "MsBackendTest", function(object, value) {
object$msLevel <- value
object
})msLevel(be)## [1] 2 1 2msLevel(be) <- c(1L, 1L, 2L)
msLevel(be)## [1] 1 1 2polarity<-Replace the values for the polarity spectra variables. Parameter value has to
be of type integer and should ideally also use the standard encoding 0L
and 1L for negative and positive polarity (and NA_integer for
missing). Thus, in our implementation we also make sure the input parameter
contains the expected values (although this is not a strictly required).
setReplaceMethod("polarity", "MsBackendTest", function(object, value) {
if (!all(value %in% c(0, 1, NA)))
stop("'polarity' should be encoded as 0L (negative), 1L (positive) ",
"with missing values being NA_integer_")
object$polarity <- value
object
})polarity(be) <- c(0L, 0L, 0L)
polarity(be)## [1] 0 0 0rtime<-Replace the retention times for the spectra represented by the
backend. Parameter value must be of type numeric. Also, although it is not a
strict requirement, retention times should ideally be ordered increasingly per
sample and their unit should be seconds.
setReplaceMethod("rtime", "MsBackendTest", function(object, value) {
object$rtime <- value
object
})rtime(be)## [1] 1.2 1.3 1.4rtime(be) <- rtime(be) + 2
rtime(be)## [1] 3.2 3.3 3.4smoothed<-Replace the spectra variable smoothed that indicates whether some data
smoothing operation was performed on the spectra. Parameter value must be of
type logical.
setReplaceMethod("smoothed", "MsBackendTest", function(object, value) {
object$smoothed <- value
object
})smoothed(be) <- rep(TRUE, 3)
smoothed(be)## [1] TRUE TRUE TRUEspectraNames<-Replace the names of individual spectras within the backend. Same as for
names, colnames or rownames, spectraNames are expected to be of type
character. In our backend implementation we store the spectra names into the
rownames of the @spectraVars data frame.
setReplaceMethod("spectraNames", "MsBackendTest", function(object, value) {
rownames(object@spectraVars) <- value
object
})spectraNames(be) <- c("a", "b", "c")
spectraNames(be)## [1] "a" "b" "c"Default implementations for these methods are available for MsBackend
classes, thus these methods don’t have to be implemented for each new
backend. For some backends, depending on how the data is represented or accessed
within it, different implementations might however be more efficient.
backendBpparamThe backendBpparam method is supposed to evaluate whether a provided (or the
default) parallel processing setup is supported by the backend. Backends that
do not support parallel processing should return SerialParam() instead.
The default implementation is shown below.
setMethod("backendBpparam", signature = "MsBackend",
function(object, BPPARAM = bpparam()) {
## Return SerialParam() instead to disable parallel processing
BPPARAM
})dropNaSpectraVariablesThe dropNaSpectraVariables is supposed to allow removing all spectra variables
from a data set (storage) that contain only missing values (i.e. where the value
of a spectra variable for each spectrum is NA). This function is intended to
reduce memory requirements of backends such as the MsBackendMzR that load
values from all core spectra variables from the original data files, even if
their values are only NA. Removing these missing values from the backend can
hence reduce the size in memory of a backend without data loss (because methods
extracting core spectra variables are supposed to always return NA values even
if no data is available for them - in such cases the NA values are supposed to
be created on-the-fly.
The default implementation is shown below.
setMethod("dropNaSpectraVariables", "MsBackend", function(object) {
svs <- spectraVariables(object)
svs <- svs[!(svs %in% c("mz", "intensity"))]
spd <- spectraData(object, columns = svs)
keep <- !vapply1l(spd, function(z) all(is.na(z)))
selectSpectraVariables(object, c(svs[keep], "mz", "intensity"))
})isReadOnlyisReadOnly is expected to return a logical(1) with either TRUE or FALSE
indicating whether the backend supports replacing data or not. The default
implementation is shown below.
setMethod("isReadOnly", "MsBackend", function(object) {
object@readonly
})peaksVariablesThe peaksVariables is expected to return a character vector with the names
of the peaks variables (i.e. information and properties of individual mass
peaks) available in the backend. The default implementation for MsBackend
returns by default c("mz", "intensity"). This method should only be
implemented for backends that (eventually) also provide additional peaks
variables. The default implementation is shown below.
setMethod("peaksVariables", "MsBackend", function(object) {
c("mz", "intensity")
})uniqueMsLevelsThis method should return the unique MS level(s) of all spectra within the backend. The default implementation is shown below.
setMethod("uniqueMsLevels", "MsBackend", function(object, ...) {
unique(msLevel(object))
})This method thus retrieves first the MS levels of all spectra and then calls
unique on them. Database-based backends might avoid such an eventually heavy
operation by selecting the unique MS levels directly using an SQL call.
ionCountThe ionCount method should return a numeric (length equal to the number of
spectra represented by the backend) with the sum of all intensities within each
spectrum. For empty spectra NA_real_ should be returned. The method below is
the default implementation of the method.
setMethod("ionCount", "MsBackend", function(object) {
vapply1d(intensity(object), sum, na.rm = TRUE)
})isCentroidedThis method should return the information for each spectrum whether it is
centroided. In contrast to the centroided method a heuristic approach is
used. The default implementation is shown below.
setMethod("isCentroided", "MsBackend", function(object, ...) {
vapply1l(peaksData(object), .peaks_is_centroided)
})resetThis is a special method that backends may implement or support, but don’t
necessary have to. This method will be called by the reset,Spectra method and
is supposed to restore the data to its original state. The default
implementation for MsBackend shown below simply returns the backend as-is. The
MsBackendSql backend from the MsBackendSql package in contrast
re-initializes the data using the data from the database.
setMethod("reset", "MsBackend", function(object) {
object
})exportThis method should export the data from a MsBackend. The method is called by
the export,Spectra method that passes itself as a second argument to the
function. The export,MsBackend implementation is thus expected to take a
Spectra object as second argument from which all data should be taken and
exported. Implementation of this method is optional. The implementation of the
method for the MsBackendMzR backend is shown below.
setMethod("export", "MsBackendMzR", function(object, x, file = tempfile(),
format = c("mzML", "mzXML"),
copy = FALSE,
BPPARAM = bpparam()) {
l <- length(x)
file <- sanitize_file_name(file)
if (length(file) == 1)
file <- rep_len(file, l)
if (length(file) != l)
stop("Parameter 'file' has to be either of length 1 or ",
length(x), ", i.e. 'length(x)'.", call. = FALSE)
f <- factor(file, levels = unique(file))
tmp <- bpmapply(.write_ms_data_mzR, split(x, f), levels(f),
MoreArgs = list(format = format, copy = copy),
BPPARAM = BPPARAM)
})See alternatively also the MsBackendMgf package for an
implementation for the MsBackendMgf backend.
splitThe split method should allow to split a MsBackend into a list of
MsBackend objects. The default implementation is shown below.
setMethod("split", "MsBackend", function(x, f, drop = FALSE, ...) {
split.default(x, f, drop = drop, ...)
})supportsSetBackendWhether a MsBackend supports the setBackend method that allows to change the
backend of a Spectra object from one to another backend. To support
setBackend the backend needs to have a parameter data in its
backendInitialize method that allows it to be initialized with a DataFrame
containing the full spectra data. The default implementation is shown below.
setMethod("supportsSetBackend", "MsBackend", function(object, ...) {
!isReadOnly(object)
})filterDataOriginThe filter* methods are expected to take a MsBackend and to subset it based
on some criteria. While also the [ method could be used to perform such subset
operation, these methods might allow more efficient ways to subset the data
e.g. by performing the operation within a database with a dedicated SQL call. A
default implementation is available for every filter function and thus a method
needs only to be implemented if the data storage/representation within a backend
would allow a more efficient operation.
All filter methods are expected to return the subset backend (i.e. an instance of the same backend class with the same, or less spectra).
The filterDataOrigin should subset the backend to spectra with their
dataOrigin spectra variable matching the values provided with the dataOrigin
parameter. The default implementation for MsBackend is shown below.
setMethod("filterDataOrigin", "MsBackend",
function(object, dataOrigin = character()) {
if (length(dataOrigin)) {
object <- object[dataOrigin(object) %in% dataOrigin]
if (is.unsorted(dataOrigin))
object[order(match(dataOrigin(object), dataOrigin))]
else object
} else object
})filterDataStorageSimilar to the filterDataOrigin, the filterDataStorage should subset a
backend to spectra with their dataStorage spectra variable matching the values
provided with the dataStorage parameter.
setMethod("filterDataStorage", "MsBackend",
function(object, dataStorage = character()) {
if (length(dataStorage)) {
object <- object[dataStorage(object) %in% dataStorage]
if (is.unsorted(dataStorage))
object[order(match(dataStorage(object), dataStorage))]
else object
} else object
})filterEmptySpectraThe filterEmptySpectra should remove all empty spectra (i.e. spectra without
any mass peaks) from the backend. The method is expected to return the subset
backend. The default implementation for MsBackend is shown below.
setMethod("filterEmptySpectra", "MsBackend", function(object, ...) {
if (!length(object)) return(object)
object[as.logical(lengths(object))]
})filterIsolationWindowThe filterIsolationWindow filters the backend to spectra with the provided
mz value being within their isolationWindowLowerMz and
isolationWindowUpperMz. The parameter mz defining this target m/z is
expected to be a numeric of length 1. The default implementation for
MsBackend is shown below.
setMethod("filterIsolationWindow", "MsBackend",
function(object, mz = numeric(), ...) {
if (length(mz)) {
if (length(mz) > 1)
stop("'mz' is expected to be a single m/z value")
keep <- which(isolationWindowLowerMz(object) <= mz &
isolationWindowUpperMz(object) >= mz)
object[keep]
} else object
})filterMsLevelThe filterMsLevel method is expected to reduce the backend to spectra with the
provided MS level(s). Parameter msLevel has to be an integer (any length).
The default implementation for MsBackend is shown below.
setMethod("filterMsLevel", "MsBackend",
function(object, msLevel = integer()) {
if (length(msLevel)) {
object[msLevel(object) %in% msLevel]
} else object
})filterPolarityThe filterPolarity method is expected to subset the backend to spectra
matching the provided polarity (or polarities). Parameter polarity has to be
an integer (of any length). The default implementation for MsBackend is
shown below.
setMethod("filterPolarity", "MsBackend",
function(object, polarity = integer()) {
if (length(polarity))
object[polarity(object) %in% polarity]
else object
})filterPrecursorMzRangeThe filterPrecursorMzRange method filters the backend to spectra with their
precursorMz being between the provided m/z range (parameter mz). This method
was previously named filterPrecursorMz. Parameter mz is expected to be a
numeric of length 2 defining the lower and upper limit of this precursor m/z
range. The default implementation for MsBackend is shown below.
library(MsCoreUtils)##
## Attaching package: 'MsCoreUtils'## The following objects are masked from 'package:Spectra':
##
## bin, smooth## The following objects are masked from 'package:ProtGenerics':
##
## bin, smooth## The following object is masked from 'package:stats':
##
## smoothsetMethod("filterPrecursorMzRange", "MsBackend",
function(object, mz = numeric()) {
if (length(mz)) {
mz <- range(mz)
keep <- which(between(precursorMz(object), mz))
object[keep]
} else object
})filterPrecursorMzValuesThe filterPrecursorMzValues method filters the backend to spectra with their
m/z values matching to the provided m/z value(s). Parameters ppm and
tolerance (both expected to be numeric of length 1) allow to define the
conditions for the relaxed matching. Parameter mz has to be a numeric (of
any length). The default implementation for MsBackend is shown below.
setMethod("filterPrecursorMzValues", "MsBackend",
function(object, mz = numeric(), ppm = 20, tolerance = 0) {
if (length(mz)) {
object[.values_match_mz(precursorMz(object), mz = mz,
ppm = ppm, tolerance = tolerance)]
} else object
})The .values_match_mz function used by this function is defined as:
.values_match_mz <- function(x, mz, ppm = 20, tolerance = 0) {
o <- order(x, na.last = NA)
cmn <- common(x[o], sort(mz), tolerance = tolerance, ppm = ppm,
duplicates = "keep", .check = FALSE)
sort(o[cmn])
}filterPrecursorChargeThe filterPrecursorCharge method filters the backend to spectra with matching
precursor charge. Parameter z defining the requested precursor charge has to
be an integer (of any length). The default implementation for MsBackend is
shown below.
setMethod("filterPrecursorCharge", "MsBackend",
function(object, z = integer()) {
if (length(z)) {
keep <- which(precursorCharge(object) %in% z)
object[keep]
} else object
})filterPrecursorScanThe filterPrecursorScan method filters the backend to parent (e.g. MS1) and
children scans (e.g. MS2) of acquisition number acquisitionNum. Parameter f
defines how the backend should be split (by default by original data file) to
avoid selecting spectra from different samples/files. The default implementation
for MsBackend is shown below.
setMethod("filterPrecursorScan", "MsBackend",
function(object, acquisitionNum = integer(), f = dataOrigin(object)) {
if (length(acquisitionNum) && length(f)) {
if (!is.factor(f))
f <- factor(f, exclude = character())
keep <- unsplit(lapply(split(object, f = f), function(z, an) {
.filterSpectraHierarchy(acquisitionNum(z),
precScanNum(z),
an)
}, an = acquisitionNum), f = f)
object[keep]
} else object
})filterRtThe filterRt method filters the backend to spectra with their retention time
being between the provided rt range. Parameter rt is expected
to be a numeric of length 2 defining the lower and upper bound of this
range. Parameter msLevel. (note the . in the name of the parameter!) can be
optionally used to restrict the filtering to the selected MS levels (i.e. the RT
filter is only applied to spectra of the selected MS levels and all spectra with
a different MS level are returned as well). The default implementation for
MsBackend is shown below.
setMethod("filterRt", "MsBackend",
function(object, rt = numeric(), msLevel. = uniqueMsLevels(object)) {
if (length(rt)) {
rt <- range(rt)
sel_ms <- msLevel(object) %in% msLevel.
sel_rt <- between(rtime(object), rt) & sel_ms
object[sel_rt | !sel_ms]
} else object
})In this tutorial we implemented a simple in-memory MsBackend from
scratch. For many real-life situation it might however be better to extend some
of the pre-defined backend classes from the Spectra package to avoid
duplicating functionality. A good starting point might be the MsBackendMemory
backend for any in-memory data representation, or the MsBackendCached for
backends that retrieve data from inherently read-only resources (such as
database connection or raw data files) but still would need to support adding
spectra variables or changing values of spectra variables. Similarly, if the
only purpose of a backend is to import or export data in a specific format, the
MsBackendMemory might be extended and a single method (backendInitialize)
would need to be implemented for the new class: this new backendInitialize
would then call the code to import the data from the new file format and store
it within the available slots of the MsBackendMemory object. Examples would be
the backends provided by the MsBackendMgf
and MsBackendMsp classes.
The Spectra package provides a set of unit tests that allow to check a backend
for compliance with MsBackend. Below we load this test suite and call the
tests. The tests will be performed on a variable be in the current workspace
(which in our case is an instance of our MsBackendTest class).
library(testthat)
test_suite <- system.file("test_backends", "test_MsBackend",
package = "Spectra")
test_dir(test_suite, stop_on_failure = TRUE)sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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] MsCoreUtils_1.12.0 IRanges_2.34.1 Spectra_1.10.3
## [4] ProtGenerics_1.32.0 BiocParallel_1.34.2 S4Vectors_0.38.2
## [7] BiocGenerics_0.46.0 BiocStyle_2.28.1
##
## loaded via a namespace (and not attached):
## [1] cli_3.6.1 knitr_1.44 rlang_1.1.1
## [4] xfun_0.40 clue_0.3-65 jsonlite_1.8.7
## [7] htmltools_0.5.6 sass_0.4.7 rmarkdown_2.25
## [10] evaluate_0.21 jquerylib_0.1.4 MASS_7.3-60
## [13] fastmap_1.1.1 yaml_2.3.7 bookdown_0.35
## [16] BiocManager_1.30.22 cluster_2.1.4 compiler_4.3.1
## [19] codetools_0.2-19 fs_1.6.3 digest_0.6.33
## [22] R6_2.5.1 parallel_4.3.1 bslib_0.5.1
## [25] tools_4.3.1 cachem_1.0.8Gatto, Laurent, Sebastian Gibb, and Johannes Rainer. 2020. “MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data.” Journal of Proteome Research, September. https://doi.org/10.1021/acs.jproteome.0c00313.