In rformassspectrometry/Chromatograms: Infrastructure for Chromatographic Mass Spectrometry Data

BiocStyle::markdown()

Package: r Biocpkg("Chromatograms")
Authors: r packageDescription("Chromatograms")[["Author"]]
Compiled: r date()

library(Chromatograms)
library(BiocStyle)

Introduction

Similar to the r Biocpkg("Spectra") package, the r Biocpkg("Chromatograms") also separates the user-faced functionality to process and analyze chromatographic mass spectrometry (MS) data from the code for storage and representation of the data. The latter functionality is provided by implementations of the ChromBackend class, further on called backends. This vignette describes the ChromBackend class and illustrates on a simple example how a backend extending this class could be implemented.

Contributions to this vignette (content or correction of typos) or requests for additional details and information are highly welcome, ideally via pull requests or issues on the package's github repository.

What is a `ChromBackend`?

The purpose of a backend class extending the virtual ChromBackend is to provide the chromatographic MS data to the Chromatograms object, which is used by the user to interact with - and analyze the data. The ChromBackend defines the API that new backends need to provide so that they can be used with Chromatograms. This API defines a set of methods to access the data. For many functions default implementations exist and a dedicated implementation for a new backend is only needed if necessary (e.g. if the data is stored in a way that a different access to it would be better). In addition, a core set of variables (data fields), the so called core chromatogram variables, is defined to describe the chromatographic data. Each backend needs to provide these, but can also define additional data fields. Before implementing a new backend it is highly suggested to carefully read the following Conventions and definitions section.

Conventions and definitions

General conventions for chromatographic MS data of a Chromatograms are:

One Chromatograms object is designed to contain multiple chromatographic data (not data from a single chromatogram).
retention time values within each chromatogram are expected to be sorted increasingly.
Missing values (NA) for retention time values are not supported.
Properties (data fields) of a chromatogram are called chromatogram variables. While backends can define their own properties, a minimum required set of chromatogram variables must be provided by each backend (even if their values are empty). These core chromatogram variables are listed (along with their expected data type) by the coreChromVariables() function.
dataOrigin defines for each chromatogram where the data is from expected to be of typecharacter. Missing values should be NA_character_
ChromBackend 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 (i.e. <- methods that would allow to add or set variables. Read-only backends should implement the isReadOnly() method, that should then return TRUE. Note that backends for purely read-only resources could also implement a caching mechanism to (temporarily) store changes to the data locally within the object (and hence in memory). See information on the MsBackendCached in the r Biocpkg("Spectra") package for more details.

Notes on parallel and chunk-wise processing

For parallel processing, Chromatograms splits the backend based on a defined factor and processes each in parallel (or in serial if a SerialParam is used). The splitting factor can be defined for Chromatograms by setting the parameter processingChunkSize. Alternatively, through the backendParallelFactor() method the backend can also suggest a factor that should/could be used for splitting and parallel processing. The default implementation for backendParallelFactor() is to return an empty factor (factor()) hence not suggesting any preferred splitting.

Besides parallel processing, for on-disk backends (i.e., backends that don't keep all of the data in memory), this chunk-wise processing can also reduce the memory demand for operations, because only the peak data of the current chunk needs to be realized in memory.

API

The ChromBackend class defines core methods that have to be implemented by a MS backend as well as optional methods for which a default implementation is already available. These functions are described in sections Required methods and Optional methods, respectively.

To create a new backend a class extending the virtual ChromBackend needs to be implemented. In the following example we define a simple class that uses a data.frame to store general properties (chromatogram variables) and a list of data.frame for the retention time and intensity values of each chromatograms, which represent the actual chromatographic MS data. These values are store in a list, where each element correspond to one chromatogram, as the number of values (peaks) can vary between chromatograms. We also provide a basic constructor function that returns an empty instance of the new class.

library(Chromatograms)

#' Definition of the backend class extending ChromBackend
setClass("ChromBackendTest",
         contains = "ChromBackend",
         slots = c(
             chromData = "data.frame",
             peaksData = "list"
         ),
         prototype = prototype(
             chromData = data.frame(),
             peaksData = list()
         ))

#' Simple constructor function
ChromBackendTest <- function() {
    new("ChromBackendTest")
}

The 2 slots @chromData and @peaksData will be used to store the general properties of the chromatograms and the actual chromatographic data, respectively. each row in chromData will contain data for one chromatogram with the columns being the different chromatogram variables (i.e. additional properties of a chromatogram such as its m/z value or MS level) and each element in @peaksData a data.frame with the retention time and intensity values representing thus the peaks data of the respective chromatogram. This is only one of the possibly many ways chromatographic data might be represented.

We should ideally also add some basic validity function that ensures the data to be correct (valid). The function below simply checks that the number of rows of the @chromData slot matches the length of the @peaksData slots.

#' Basic validation function
setValidity("ChromBackendTest", function(object) {
    if (length(object@peaksData) != nrow(object@chromData))
        return("length of 'peaksData' has to match the number of rows of ",
               "'chromData'")
    NULL
})

We can now create an instance of our new class with the ChromBackendTest() function.

#' Create an empty instance of ChromBackendTest
be <- ChromBackendTest()
be

A show() method would allow for a more convenient way how general information of our object is displayed. Below we add an implementation of the show() method.

#' implementation of show for ChromBackendTest
setMethod("show", "ChromBackendTest", function(object) {
    cd <- object@chromData
    cat(class(object), "with", nrow(cd), "chromatograms\n")
})
be

Required methods

Methods listed in this section must be implemented for a new class extending ChromBackend. Methods should ideally also be 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.

`dataStorage()`

The dataStorage chromatogram variable provides information how or where the data is stored. The dataStorage() method should therefore return a character vector of length equal to the number of chromatograms that are represented by the object. The values for dataStorage can be any character value, except NA. For our example backend we define a simple dataStorage() method that simply returns the column "dataStorage" from the @chromData (as a character).

#' dataStorage method to provide information *where* data is stored
setMethod("dataStorage", "ChromBackendTest", function(object) {
    as.character(object@chromData$dataStorage)
})

Calling dataStorage() on our example backend will thus return an empty character (since the object created above does not contain any data).

dataStorage(be)

`length()`

length() is expected to return an integer of length 1 with the total number of chromatograms that are represented by the backend. For our example backend we simply return the number of rows of the data.frame stored in the @chromData slot.

#' length to provide information on the number of chromatograms
setMethod("length", "ChromBackendTest", function(x) {
    nrow(x@chromData)
})
length(be)

`backendInitialize()`

The backendInitialize() method should be called after creating an instance of the backend class and is responsible for preparing (initializing) the backend with data. This method can accept any parameters required by the backend to load or initialize the data, such as file names, a database connection, or objects containing the data. It is also recommended that the the special chromatogram variables dataStorage and dataOrigin are set during backendInitialize().

It is strongly recommended to validate the input data within the initialize method. The advantage of performing these validity checks in backendInitialize() rather than using setValidity() is that computationally expensive operations/checks would only be performed once,during initialization, instead of each time values within the object are modified (e.g., through subsetting or similar operations), which would occur with setValidity().

We also use the validChromData() and validPeaksData() functions to ensure that core chromatogram variables and core peaks variables have the correct data type. These checks verify that thepeaksData contains only numeric values and that the number of retention time and intensity values matches for each chromatogram.

Below we define a backendInitialize() method that accepts a data.frame containing chromatogram variables and a list with retention time and intensity values for each chromatogram.

#' backendInitialize method to fill the backend with data.
setMethod(
    "backendInitialize", "ChromBackendTest",
    function(object, chromData, peaksData) {
        if (!is.data.frame(chromData))
            stop("'chromData' needs to be a 'data.frame' with the general",
                 "chromatogram variables")
        ## Defining dataStorage and dataOrigin, if not available
        if (is.null(chromData$dataOrigin))
            chromData$dataOrigin <- NA_character_
        ## Validate the provided data
        validChromData(chromData)
        validPeaksData(peaksData)
        ## Fill the object with data
        object@chromData <- chromData
        object@peaksData <- peaksData
        object
    })

In addition to adding the data to object, the function also define the dataOrigin chromatographic variables. This variable is expected to provide information on where the data is originating.

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 chromatogram variables.
cdata <- data.frame(msLevel = c(1L, 1L),
                    mz = c(112.2, 123.3))

# Retention time and intensity values for each chromatogram.
pdata <- list(
  data.frame(rtime = c(12.4, 12.8, 13.2, 14.6),
       intensity = c(123.3, 153.6, 2354.3, 243.4)),
  data.frame(rtime = c(45.1, 46.2),
       intensity = c(100, 80.1))
)

#' Create and initialize the backend
be <- backendInitialize(ChromBackendTest(),
                        chromData = cdata, peaksData = pdata)
be

This backendInitialize() implementation should assure data validity and integrity. Below we use this function again to create our backend instance.

The backendInitialize() method that we implemented for our backend class expects the user to provide the full MS data. It would alternatively also be possible to implement a method that takes data file names as input from which the function can then import the data. The purpose of the backendInitialize() method is to initialize and prepare the data in a way that it can be accessed by a Chromatograms object. Whether the data is actually loaded into memory or simply referenced and loaded upon request does not matter as long as the backend is able to provide the data though its accessor methods when requested by the Chromatograms object.

`chromVariables()`

The chromVariables() method should return a character vector with the names of all available chromatogram variables of the backend. While a backend class should support defining and providing their own variables, each ChromBackend class must provide also the core chromatogram variables (in the correct data type). These can be listed by the coreChromVariables() function:

#' List core chromatogram variables along with data types.
coreChromVariables()

A typical chromVariables() method for a ChromBackend class will thus be implemented similarly to the one for our ChromBackendTest test backend: it will return the names for all available chromatogram variables that can be called by chromData() within the backend object. There is a default implementation for chromVariables() that will return the core chromatogram variables. However if a backend class defines additional chromatogram variables, the chromVariables() method should be implemented to return the names of these additional variables as well.

#' Accessor for available chromatogram variables
setMethod("chromVariables", "ChromBackendTest", function(object) {
    union(names(object@chromData), names(coreChromVariables()))
})

chromVariables(be)

`chromData()`

The chromData method should return the full chromatogram data within a backend as a data.frame object. A parameter columns should allow to define the names of the variables that should be returned. A parameter drop should also be implemented to allow for the calling of one column while still controlling the return type. Each row in this data frame should represent one chromatogram, each column a chromatogram variable. The data.frame must provide values (even if they are NA) for all requested chromatogram variables of the backend (including the core chromatogram variables). The fillCoreChromVariables() function from the Chromatograms package allows to complete (fill) a provided data.frame with eventually missing core chromatogram variables:

#' Get the data.frame with the available chrom variables
be@chromData
#' Complete this data.frame with missing core variables
fillCoreChromVariables(be@chromData)

We can thus use this function to add eventually missing core chromatogram variables in the chromData implementation for our backend:

#' function to extract the full chromData
setMethod(
    "chromData", "ChromBackendTest",
    function(object, columns = chromVariables(object),
             drop = FALSE) {
      if (!any(chromVariables(object) %in% columns))
        stop("Some of the requested Chromatogram variables are not ",
                   "available")
      res <- fillCoreChromVariables(object@chromData)
      res <- res[, columns, drop = drop]
      res
      })

We can now use chromData() to either extract the full chromatogram data from the backend, or only the data for selected variables.

#' Extract the full data
chromData(be)

#' Selected variables
chromData(be, c("mz", "msLevel"))

#' Only missing core spectra variables
chromData(be, c("collisionEnergy", "mzMin"))

`peaksVariables()`

The peaksVariables() function is supposed to provide the names of the available peaks variables. If additional peaks variables would be available, these could also be listed by the peaksVariables() method. There is a default implementation for peaksVaraibles() that will return the core peaks variables. However if a backend class defines additional peaks variables, the peaksVariables() method should be implemented to return the names of these additional variables as well.

setMethod("peaksVariables", "ChromBackendTest", function(object) {
  union(names(corePeaksVariables()), names(object@peaksData[[1]]))
})

We can now see what peaks variables are present in our object:

peaksVariables(be)

`peaksData()`

The peaksData() method extracts the chromatographic data (peaks), i.e., the chromatograms' retention time and intensity values. This data is returned as a list of data.frame, with one array per chromatogram with columns being the peaks variables (retention time and intensity values) and rows the individual data pairs. Each backend must provide retention times and intensity values with this method, but additional peaks variables (columns) are also supported.

In a similar way as for the chromatogram variables, a backend should support defining and providing their own variables and each ChromBackend class must provide also the core peaks variables (in the correct data type). These can be listed by the corePeaksVariables() function:

corePeaksVariables()

Below we implement the peaksData() method for our backend.

#' method to extract the full chromatographic data as list of arrays
setMethod(
    "peaksData", "ChromBackendTest",
    function(object, columns = peaksVariables(object), drop = FALSE) { 
      if (!all(columns %in% peaksVariables(object)))
        stop("Some of the requested peaks variables are not available")
      res <- lapply(object@peaksData, function(x) x[, columns, drop = drop])
      res
    })

And with this method we can now extract the peaks data from our backend.

#' Extract the *peaks* data (i.e. intensity and retention times)
peaksData(be)

Since the peaksData() method is the main function used by a Chromatograms to retrieve data from the backend (and further process the values), this method should be implemented in an efficient way.

`[`

The [ method allows to subset ChromBackend objects. This operation is expected to reduce a ChromBackend object to the selected chromatograms without changing values for the subset chromatograms. 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()]. The MsCoreUtils::i2index function can be used to check and convert the provided parameter i (defining the subset) to an integer vector.

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 chromatogram being one element).

#' Main subset method.
setMethod("[", "ChromBackendTest", function(x, i, j, ..., drop = FALSE) {
  i <- MsCoreUtils::i2index(i, length = length(x))
  x@chromData <- x@chromData[i, ]
  x@peaksData <- x@peaksData[i]
  x
})

We can now subset our backend to the last two chromatograms.

a <- be[1]
chromData(a)

Or extracting the second chromatogram multiple times.

a <- be[c(1, 1, 1)]
chromData(a)

`$`

The $ method is expected to extract a single chromatogram or peaks variable from a backend. Parameter name should allow to name the variable to return. Each ChromBackend must support extracting the core chromatogram and core peaks variables with this method (even if no data might be available for that variable). In our example implementation below we make use of the chromData() method, but more efficient implementations might be possible as well. Also, the $ method should check if the requested variable is available and should throw an error otherwise.

#' Access a single chromatogram variable
setMethod("$", "ChromBackendTest", function(x, name) {
  if (name %in% union(chromVariables(x), names(coreChromVariables())))
    res <- chromData(x, columns = name, drop = TRUE)
  else if (name %in% peaksVariables(x))
    res <- peaksData(x, columns = name, drop = TRUE)
  else stop("The requested variable '", name, "' is not available")
  res
})

With this we can now extract the MS levels

be$msLevel

or a core chromatogram variable without values in our example backend.

be$precursorMz

or also the intensity values

be$intensity

`backendMerge()`

The backendMerge() method merges (combines) ChromBackend objects (of the same type!) into a single instance. For our test backend we thus need to combine the values in the @chromData, @peaksData slots. To support also merging of data.frames with different sets 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 filling eventually missing columns with NA).

#' Method allowing to join (concatenate) backends
setMethod("backendMerge", "ChromBackendTest", function(object, ...) {
    res <- object
    object <- unname(c(list(object), list(...)))
    res@peaksData <- do.call(c, lapply(object, function(z) z@peaksData))
    res@chromData <- do.call(MsCoreUtils::rbindFill,
                             lapply(object, function(z) z@chromData))
    validObject(res)
    res
})

Testing the function by merging the example backend instance with itself.

a <- backendMerge(be, be[2], be)
a

Data replacement methods

As stated in the general description, ChromBackend implementations can also be purely read-only resources allowing to just access, but not to replace data. For these backends isReadOnly() should return FALSE. Data replacement methods listed in this section would not need to be implemented. Our example backend stores the full data in memory, within the object, and hence we can easily change and replace values.

Since we support replacing values we also implement the isReadOnly() method for our example implementation to return FALSE (instead of the default TRUE).

#' Default for backends:
isReadOnly(be)

#' Implementation of isReadOnly for ChromBackendTest
setMethod("isReadOnly", "ChromBackendTest", function(object) FALSE)
isReadOnly(be)

All data replacement function are expected to return an instance of the same backend class that was used as input.

`chromData<-`

The main replacement method is chromData<- which should allow to replace the chormtaogram variables content of a backend with new data. This data is expected to be provided as a data.frame (similar to the one returned by chromData()). While values can be replaced, the number of chromatograms before and after a call to chromData<- has to be the same.

#' Replacement method for the full chromatogram data
setReplaceMethod("chromData", "ChromBackendTest", function(object, value) {
  if (is(value, "DataFrame"))
    value <- as(value, "data.frame")
  if (!inherits(value, "data.frame"))
    stop("'value' is expected to be a 'data.frame'")
  if (length(object) && length(object) != nrow(value))
    stop("'value' has to be a 'data.frame' with ", length(object), " rows")
  validChromData(value)
  object@chromData <- value
  object
})

To test this new method we extract the full chromatogram data from our example data set, add an additional column (chromatogram variable) and use chromData<- to replace the data of the backend.

d <- chromData(be)
d$new_col <- c("a", "b")

chromData(be) <- d

Check that we have now also the new column available.

be$new_col

`$<-`

The $<- method should allow to replace values for an existing chromatogram variable or to add an additional variable to the backend. As with all replacement methods, the length of value has to match the number of chromatograms represented by the backend. For replacement of retention time or intensity values we need also to ensure that the data would be correct after the operation, i.e., that the number of retention time and intensity values per chromatogram are the identical and that all retention time and intensity values are numeric. Finally, we use the validChromData() function to ensure that, after replacement, all core chromatogram variables have the correct data type.

#' Replace or add a single chromatogram variable.
setReplaceMethod("$", "ChromBackendTest", function(x, name, value) {
  if (length(x) && length(value) != length(x))
    stop("length of 'value' needs to match the number of chromatograms ",
         "in object.")
  if (name %in% peaksVariables(x)) {
    if (!is.list(value))
      stop("The value for peaksData should be a list")
    for (i in seq_along(value)) {
      x@peaksData[[i]][[name]] <- value[[i]]
      validPeaksData(x@peaksData)
    }
  } else {
    x@chromData[, name] <- value
    validChromData(x@chromData)
  }
  x
})

We can thus replace an existing chromatogram variable, such as msLevel:

#' Values before replacement
be$msLevel

#' Replace MS levels
be$msLevel <- c(3L, 2L)

#' Values after replacement
be$msLevel

We can also add a new chromatogram variables:

#' Add a new chromatogram variable
be$name <- c("A", "B")
be$name

Or also replace intensity values. Below we replace the intensity values by adding a value of +3 to each.

#' Replace intensity values
be$msLevel3 <- be$msLevel + 3
be$msLevel3

`peaksData<-`

The peaksData<- method should allow to replace the full peaks data (retention time and intensity value pairs) of all chromatograms in a backend. As value, a list of data.frame should be provided with columns names "rtime" and "intensity". Because the full peaks data is provided at once, this method can (and should) support changing also the number of peaks per chromatogram (while the methods like rtime<- or $rtime would not allow).

#' replacement method for peaks data
setReplaceMethod("peaksData", "ChromBackendTest", function(object, value) {
  if (!is.list(value))
    stop("'value' is expected to be a list")
  if (length(object) && length(object) != length(value))
    stop("'value' has to be a list with ", length(object), " elements")
  validPeaksData(value)
  object@peaksData <- value
  object
})

With this method we can now replace the peaks data of a backend:

#' Create a list with peaks matrices; our backend has 3 chromatograms
#' thus our `list` has to be of length 3
tmp <- list(
    data.frame(rtime = c(12.3, 14.4, 15.4, 16.4),
          intensity = c(200, 312, 354.1, 232)),
    data.frame(rtime = c(14.4),
          intensity = c(13.4))
)

be_2 <- be
#' Assign this peaks data to one of our test backends
peaksData(be_2) <- tmp

#' Evaluate that we properly added the peaks data
peaksData(be_2)

Methods with available default implementations

Default implementations for the ChromBackend class are available for a large number of methods. Thus, any backend extending this class will automatically inherit these default implementations. Alternative, class-specific, versions can, but don't need to be developed. The default versions are defined in the R/ChromBackend.R file, and also listed in this section. If alternative versions are implemented it should be ensured that the expected data type is always used for core chromatogram variables. Use coreChromVariables() and corePeaksVariables() to list these mandatory data types.

`backendParallelFactor()`

The backendParallelFactor() function allows a backend to suggest a preferred way it could be split for parallel processing. The default implementation returns factor() (i.e. a factor of length 0) hence not suggesting any specific splitting setup.

#' Is there a specific way how the object could be best split for
#' parallel processing?
setMethod("backendParallelFactor", "ChromBackend", function(object, ...) {
    factor()
})

backendParallelFactor(be)

`chromIndex()`

The chromIndex() function should return the value for the "chromIndex" chromatogram variable. As a result, an integer of length equal to the number of chromatograms in object needs to be returned. The default implementation is:

#' get the values for the chromIndex chromatogram variable
setMethod("chromIndex", "ChromBackend",
          function(object, columns = chromVariables(object)) {
              chromData(object, columns = "chromIndex", drop = TRUE)
          })

The result of calling this method on our test backend:

chromIndex(be)

`collisionEnergy()`

The collisionEnergy() function should return the value for the "collisionEnergy" chromatogram variable. As a result, a numeric of length equal to the number of chromatograms has to be returned. The default implementation is:

#' get the values for the collisionEnergy chromatogram variable
setMethod("collisionEnergy", "ChromBackend", function(object) {
    chromData(object, columns = "collisionEnergy", drop = TRUE)
})

The result of calling this method on our test backend:

collisionEnergy(be)

The default replacement method for the collisionEnergy chromatogram variable is:

#' Default replacement method for collisionEnergy
setReplaceMethod(
    "collisionEnergy", "ChromBackend", function(object, value) {
    object$collisionEnergy <- value
    object
    })

This method thus makes use of the $<- replacement method we implemented above. To test this function we replace the collision energy below.

#' Replace the collision energy
collisionEnergy(be) <- c(20, 30)
collisionEnergy(be)

`dataOrigin()`, `dataOrigin<-`

The dataOrigin() and dataOrigin<- methods return or set the value(s) for the "dataOrigin" chromatogram variable. The values for this chromatogram variable need to be of type character (the length equal to the number of chromatograms). The default implementation for dataOrigin() is:

#' Default implementation to access dataOrigin
setMethod("dataOrigin", "ChromBackend", function(object) {
    chromData(object, columns = "dataOrigin", drop = TRUE)
})

Below we use this method to access the values of the dataOrigin chromatogram variable.

#' Access the dataOrigin values
dataOrigin(be)

The default implementation for dataOrigin<- uses, like all defaults for replacement methods, the $<- method:

#' Default implementation of the `dataOrigin<-` replacement method
setReplaceMethod("dataOrigin", "ChromBackend", function(object, value) {
  object$dataOrigin <- value
  object
})

For our backend we can change the values of the dataOrigin variable:

#' Replace the backend's dataOrigin values
dataOrigin(be) <- rep("from somewhere", 2)
dataOrigin(be)

`intensity()`, `intensity<-`

The intensity() and intensity<- methods allow to extract or set the intensity values of the individual chromatograms represented by the backend. The default for the intensity() function, which is expected to return a list of numeric values with the intensity values of each chromatogram, uses the peaksData() method:

#' Default method to extract intensity values
setMethod("intensity", "ChromBackend", function(object) {
    if (length(object)) {
        peaksData(object, column = "intensity", drop = TRUE)
    } else list()
})

The default replacement method for intensity values uses the $<- method:

#' Default implementation of the replacement method for intensity values
setReplaceMethod("intensity", "ChromBackend", function(object, value) {
    pd <- peaksData(object)
    if (!is.list(value) || length(pd) != length(value))
        stop("'value' should be a list of the same length as 'object'")
    for (i in seq_along(pd)) {
        if (length(value[[i]]) != nrow(pd[[i]])) {
            stop(paste0("Length of 'value[[", i, "]]' does not match ",
                       "the number of rows in the intensity of chromatogram: ",
                       i, "'"))
        }
    }
    peaksData(object) <- lapply(seq_along(pd), function(i) {
        pd[[i]]$intensity <- value[[i]]
        return(pd[[i]])
    })
    object
})

#' Replace intensity values
intensity(be)[[1]] <- intensity(be)[[1]] + 10
intensity(be)

`isEmpty()`

The isEmpty() is a simple helper function to evaluate whether chromatograms are empty, i.e. have no peaks (retention time and intensity values). It should return a logical vector of length equal to the number of chromatograms in the backend with TRUE if a chromatogram is empty and FALSE otherwise. The default implementation uses the lengths() method (defined further below) that returns for each chromatogram the number of available data points (peaks).

#' Default implementation for `isEmpty()`
setMethod("isEmpty", "ChromBackend", function(x) {
    lengths(x) == 0L
})

isEmpty(be)

`isReadOnly()`

As discussed above, backends can also be read-only, hence only allowing to access, but not to change any values (e.g. if the data is stored in a data base and the connection to this data base does not support updating or replacing data). In such cases, the default isReadOnly() method can be used, which returns always TRUE:

#' Default implementation of `isReadOnly()`
setMethod("isReadOnly", "ChromBackend", function(object) {
    TRUE
})

Backends that support changing data values should implement their own version (like we did above) to return FALSE instead:

isReadOnly(be)

`length()`

The length() method should return a single integer with the total number of chromatograms available through the backend. The default implementation for this function is:

#' Default implementation for `length()`
setMethod("length", "ChromBackend", function(x) {
    nrow(chromData(x, columns = "dataStorage"))
})

length(be)

`lengths()`

The lengths() function should return the number of data pairs (peaks; retention time or intensity values) per chromatogram. The result should be an integer vector (of length equal to the number of chromatograms in the backend) with these counts. The default implementation uses the intensity() function.

#' Default implementation for `lengths()`
setMethod("lengths", "ChromBackend", function(x) {
    lengths(intensity(x))
})

The number of peaks for our test backend:

lengths(be)

`msLevel()`, `msLevel<-`

The msLevel() and msLevel<- methods should allow extracting and setting the MS level for the individual chromatograms. MS levels are encoded as integer, thus, msLevel() must return an integer vector of length equal to the number of chromatograms of the backend and msLevel<- should take/accept such a vector as input. The default implementations for both methods are shown below.

#' Default methods to get or set MS levels
setMethod("msLevel", "ChromBackend", function(object) {
    chromData(object, columns = "msLevel", drop = TRUE)
})
setReplaceMethod("msLevel", "ChromBackend", function(object, value) {
    object$msLevel <- value
    object
})

To test these we below replace the MS levels for our test data set and extract these values again.

msLevel(be) <- c(1L, 2L)
msLevel(be)

`mz()`, `mz<-`

The mz() and mz<- methods should allow to extract or set the m/z value for each chromatogram. The m/z value of a chromatogram is encoded as numeric, thus, the methods are expected to return or accept a numeric vector of length equal to the number of chromatograms. The default implementations are shown below.

#' Default implementations to get or set m/z value(s)
setMethod("mz", "ChromBackend", function(object) {
    chromData(object, columns = "mz", drop = TRUE)
})
setReplaceMethod("mz", "ChromBackend", function(object, value) {
    object$mz <- value
    object
})

We below set and extract these target m/z values.

mz(be) <- c(314.3, 312.5)
mz(be)

`mzMax()`, `mzMax<-`

The mzMax() and mzMax<- methods should allow to extract or set the upper m/z boundary for each chromatogram. m/z values are encoded as numeric, thus, the methods are expected to return or accept a numeric vector of length equal to the number of chromatograms. The default implementations are shown below.

#' Default implementations to get or set upper m/z limits
setMethod("mzMax", "ChromBackend", function(object) {
    chromData(object, columns = "mzMax", drop = TRUE)
})
setReplaceMethod("mzMax", "ChromBackend", function(object, value) {
    object$mzMax <- value
    object
})

Testing these functions by replacing the upper m/z boundary with new values.

mzMax(be) <- mz(be) + 0.01
mzMax(be)

`mzMin(),`mzMin<-`

The mzMin() and mzMin<- methods should allow to extract or set the lower m/z boundary for each chromatogram. m/z values are encoded as numeric, thus, the methods are expected to return or accept a numeric vector of length equal to the number of chromatograms. The default implementations are shown below.

#' Default methods to get or set the lower m/z boundary
setMethod("mzMin", "ChromBackend", function(object) {
  chromData(object, columns = "mzMin", drop = TRUE)
})

setReplaceMethod("mzMin", "ChromBackend", function(object, value) {
  object$mzMin <- value
  object
})

Testing these functions by replacing the lower m/z boundary with new values.

mzMin(be) <- mz(be) - 0.01
mzMin(be)

`precursorMz()`, `precursorMz<-`

The precursorMz() and precursorMz<- methods are expected to get or set the values for the precursor m/z of each chromatogram (if available). These are encoded as numeric (one value per chromatogram) - and if a value is not available NA_real_ should be returned. The default implementations are:

#' Default implementations to get or set the precursorMz chrom variable
setMethod("precursorMz", "ChromBackend", function(object) {
    chromData(object, columns = "precursorMz", drop = TRUE)
})
setReplaceMethod("precursorMz", "ChromBackend", function(object, value) {
    object$precursorMz <- value
    object
})

Below we set and get the precursorMz chromatogram variable for our backend.

precursorMz(be) <- c(NA_real_, 123.3)
precursorMz(be)

`precursorMzMax()`, `precursorMzMax<-`

These methods are supposed to allow to get and set the precursorMzMax chromatogram variable. The default implementations are:

#' Default implementations for `precursorMzMax`
setMethod("precursorMzMax", "ChromBackend", function(object) {
    chromData(object, columns = "precursorMzMax", drop = FALSE)
})
setReplaceMethod("precursorMzMax", "ChromBackend", function(object, value) {
    object$precursorMzMax <- value
    object
})