Merge pull request #328 from rformassspectrometry/jomain

Change estimatePrecursorIntensity to a method

.github/workflows/check-bioc.yml

@@ -125,32 +125,7 @@ jobs:
       - name: Install macOS system dependencies
         if: matrix.config.os == 'macOS-latest'
         run: |
-          ## Enable installing XML from source if needed
-          brew install libxml2
-          echo "XML_CONFIG=/usr/local/opt/libxml2/bin/xml2-config" >> $GITHUB_ENV
-
-          ## Required to install magick as noted at
-          ## https://github.com/r-lib/usethis/commit/f1f1e0d10c1ebc75fd4c18fa7e2de4551fd9978f#diff-9bfee71065492f63457918efcd912cf2
-          brew install imagemagick@6
-
-          ## For textshaping, required by ragg, and required by pkgdown
-          brew install harfbuzz fribidi
-
-          brew install libgit2
-          ## Helps compile RCurl from source
-          ## brew uninstall curl
-
-          ## required for ncdf4 - can not use the homebrew one because that uses GCC
-          ## Use pre-compiled libraries from https://mac.r-project.org/libs-4/
-          curl -O https://mac.r-project.org/libs-4/netcdf-4.7.4-darwin.17-x86_64.tar.gz
-          tar fvxzm netcdf-4.7.4-darwin.17-x86_64.tar.gz -C /
-          rm netcdf-4.7.4-darwin.17-x86_64.tar.gz
-          curl -O https://mac.r-project.org/libs-4/hdf5-1.12.0-darwin.17-x86_64.tar.gz
-          tar fvxzm hdf5-1.12.0-darwin.17-x86_64.tar.gz -C /
-          rm hdf5-1.12.0-darwin.17-x86_64.tar.gz
-          curl -O https://mac.r-project.org/libs-4/szip-2.1.1-darwin.17-x86_64.tar.gz
-          tar fvxzm szip-2.1.1-darwin.17-x86_64.tar.gz -C /
-          rm szip-2.1.1-darwin.17-x86_64.tar.gz
+        shell: Rscript {0}
 
       - name: Install Windows system dependencies
         if: runner.os == 'Windows'

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Spectra
 Title: Spectra Infrastructure for Mass Spectrometry Data
-Version: 1.15.6
+Version: 1.15.7
 Description: The Spectra package defines an efficient infrastructure
    for storing and handling mass spectrometry spectra and functionality to
    subset, process, visualize and compare spectra data. It provides different
@@ -41,7 +41,7 @@ Depends:
     R (>= 4.0.0),
     S4Vectors,
     BiocParallel,
-    ProtGenerics (>= 1.35.4)
+    ProtGenerics (>= 1.37.1)
 Imports:
     methods,
     IRanges,

NAMESPACE

@@ -17,7 +17,6 @@ export(concatenateSpectra)
 export(coreSpectraVariables)
 export(countIdentifications)
 export(deisotopeSpectra)
-export(estimatePrecursorIntensity)
 export(estimatePrecursorMz)
 export(filterPeaksRanges)
 export(filterPrecursorIsotopes)
@@ -84,6 +83,7 @@ exportMethods(dataStorage)
 exportMethods(dataStorageBasePath)
 exportMethods(dropNaSpectraVariables)
 exportMethods(entropy)
+exportMethods(estimatePrecursorIntensity)
 exportMethods(export)
 exportMethods(filterAcquisitionNum)
 exportMethods(filterDataOrigin)
@@ -248,6 +248,7 @@ importMethodsFrom(ProtGenerics,collisionEnergy)
 importMethodsFrom(ProtGenerics,compareSpectra)
 importMethodsFrom(ProtGenerics,dataOrigin)
 importMethodsFrom(ProtGenerics,dataStorage)
+importMethodsFrom(ProtGenerics,estimatePrecursorIntensity)
 importMethodsFrom(ProtGenerics,filterAcquisitionNum)
 importMethodsFrom(ProtGenerics,filterDataOrigin)
 importMethodsFrom(ProtGenerics,filterDataStorage)

NEWS.md

@@ -1,5 +1,10 @@
 # Spectra 1.15
 
+## Changes in 1.15.7
+
+- Change `estimatePrecursorIntensity()` to a method to avoid overrides/clashes
+  with the same-named implementation in *xcms*.
+
 ## Changes in 1.15.6
 
 - Fix in `selectSpectraVariables()` for `MsBackendMzR`: ensure peaks variables

R/Spectra-functions.R

@@ -690,82 +690,6 @@ processingLog <- function(x) {
     x@processing
 }
 
-#' @title Estimate Precursor Intensities
-#'
-#' @description
-#'
-#' Some MS instrument manufacturers don't provide precursor intensities for
-#' fragment spectra. These can however be estimated, given that also MS1
-#' spectra are available. The `estimatePrecursorIntensity()` funtion defines the
-#' precursor intensities for MS2 spectra using the intensity of the matching
-#' MS1 peak from the closest MS1 spectrum (i.e. the last MS1 spectrum measured
-#' before the respective MS2 spectrum). With `method = "interpolation"` it is
-#' also possible to calculate the precursor intensity based on an interpolation
-#' of intensity values (and retention times) of the matching MS1 peaks from the
-#' previous and next MS1 spectrum. See below for an example.
-#'
-#' @param x `Spectra` with MS1 and MS2 spectra.
-#'
-#' @param ppm `numeric(1)` with the maximal allowed relative difference of m/z
-#'     values between the precursor m/z of a spectrum and the m/z of the
-#'     respective ion on the MS1 scan.
-#'
-#' @param tolerance `numeric(1)` with the maximal allowed difference of m/z
-#'     values between the precursor m/z of a spectrum and the m/z of the
-#'     respective ion on the MS1 scan.
-#'
-#' @param method `character(1)` defining whether the precursor intensity
-#'     should be estimated on the previous MS1 spectrum (`method = "previous"`,
-#'     the default) or based on an interpolation on the previous and next
-#'     MS1 spectrum (`method = "interpolation"`).
-#'
-#' @param msLevel. `integer(1)` the MS level for which precursor intensities
-#'     should be estimated. Defaults to `2L`.
-#'
-#' @param f `factor` (or vector to be coerced to `factor`) defining which
-#'     spectra belong to the same original data file (sample).
-#'     Defaults to `f = dataOrigin(x)`.
-#'
-#' @param BPPARAM Parallel setup configuration. See [bpparam()] for more
-#'     information. This is passed directly to the [backendInitialize()] method
-#'     of the [MsBackend-class].
-#'
-#' @author Johannes Rainer with feedback and suggestions from Corey Broeckling
-#'
-#' @export
-#'
-#' @rdname estimatePrecursorIntensity
-#'
-#' @examples
-#'
-#' #' ## Calculating the precursor intensity for MS2 spectra:
-#' ##
-#' ## Some MS instrument manufacturer don't report the precursor intensities
-#' ## for MS2 spectra. The `estimatePrecursorIntensity` function can be used
-#' ## in these cases to calculate the precursor intensity on MS1 data. Below
-#' ## we load an mzML file from a vendor providing precursor intensities and
-#' ## compare the estimated and reported precursor intensities.
-#' tmt <- Spectra(msdata::proteomics(full.names = TRUE)[5],
-#'     backend = MsBackendMzR())
-#' pmi <- estimatePrecursorIntensity(tmt)
-#' plot(pmi, precursorIntensity(tmt))
-#'
-#' ## We can also replace the original precursor intensity values with the
-#' ## newly calculated ones
-#' tmt$precursorIntensity <- pmi
-estimatePrecursorIntensity <- function(x, ppm = 20, tolerance = 0,
-                                       method = c("previous", "interpolation"),
-                                       msLevel. = 2L, f = dataOrigin(x),
-                                       BPPARAM = bpparam()) {
-    if (is.factor(f))
-        f <- as.character(f)
-    f <- factor(f, levels = unique(f))
-    BPPARAM <- backendBpparam(x@backend, BPPARAM)
-    unlist(bplapply(split(x, f), FUN = .estimate_precursor_intensity, ppm = ppm,
-                    tolerance = tolerance, method = method, msLevel = msLevel.,
-                    BPPARAM = BPPARAM), use.names = FALSE)
-}
-
 #' estimate precursor intensities based on MS1 peak intensity. This function
 #' assumes that `x` is a `Spectra` with data **from a single file/sample**.
 #'

R/Spectra.R

@@ -2859,4 +2859,85 @@ asDataFrame <- function(object, i = seq_along(object),
     v <- spectraData(object)[rep(seq_along(object), n), spectraVars]
     p <- do.call(rbind, as.list(peaksData(object)))
     cbind(p, v)
-}
+}
+
+#' @title Estimate Precursor Intensities
+#'
+#' @description
+#'
+#' Some MS instrument manufacturers don't provide precursor intensities for
+#' fragment spectra. These can however be estimated, given that also MS1
+#' spectra are available. The `estimatePrecursorIntensity()` funtion defines the
+#' precursor intensities for MS2 spectra using the intensity of the matching
+#' MS1 peak from the closest MS1 spectrum (i.e. the last MS1 spectrum measured
+#' before the respective MS2 spectrum). With `method = "interpolation"` it is
+#' also possible to calculate the precursor intensity based on an interpolation
+#' of intensity values (and retention times) of the matching MS1 peaks from the
+#' previous and next MS1 spectrum. See below for an example.
+#'
+#' @param object `Spectra` with MS1 and MS2 spectra.
+#'
+#' @param ppm `numeric(1)` with the maximal allowed relative difference of m/z
+#'     values between the precursor m/z of a spectrum and the m/z of the
+#'     respective ion on the MS1 scan.
+#'
+#' @param tolerance `numeric(1)` with the maximal allowed difference of m/z
+#'     values between the precursor m/z of a spectrum and the m/z of the
+#'     respective ion on the MS1 scan.
+#'
+#' @param method `character(1)` defining whether the precursor intensity
+#'     should be estimated on the previous MS1 spectrum (`method = "previous"`,
+#'     the default) or based on an interpolation on the previous and next
+#'     MS1 spectrum (`method = "interpolation"`).
+#'
+#' @param msLevel. `integer(1)` the MS level for which precursor intensities
+#'     should be estimated. Defaults to `2L`.
+#'
+#' @param f `factor` (or vector to be coerced to `factor`) defining which
+#'     spectra belong to the same original data file (sample).
+#'     Defaults to `f = dataOrigin(x)`.
+#'
+#' @param BPPARAM Parallel setup configuration. See [bpparam()] for more
+#'     information. This is passed directly to the [backendInitialize()] method
+#'     of the [MsBackend-class].
+#'
+#' @author Johannes Rainer with feedback and suggestions from Corey Broeckling
+#'
+#' @importMethodsFrom ProtGenerics estimatePrecursorIntensity
+#'
+#' @exportMethod estimatePrecursorIntensity
+#'
+#' @rdname estimatePrecursorIntensity
+#'
+#' @examples
+#'
+#' #' ## Calculating the precursor intensity for MS2 spectra:
+#' ##
+#' ## Some MS instrument manufacturer don't report the precursor intensities
+#' ## for MS2 spectra. The `estimatePrecursorIntensity` function can be used
+#' ## in these cases to calculate the precursor intensity on MS1 data. Below
+#' ## we load an mzML file from a vendor providing precursor intensities and
+#' ## compare the estimated and reported precursor intensities.
+#' tmt <- Spectra(msdata::proteomics(full.names = TRUE)[5],
+#'     backend = MsBackendMzR())
+#' pmi <- estimatePrecursorIntensity(tmt)
+#' plot(pmi, precursorIntensity(tmt))
+#'
+#' ## We can also replace the original precursor intensity values with the
+#' ## newly calculated ones
+#' tmt$precursorIntensity <- pmi
+setMethod(
+    "estimatePrecursorIntensity", "Spectra",
+    function(object, ppm = 20, tolerance = 0,
+             method = c("previous", "interpolation"),
+             msLevel. = 2L, f = dataOrigin(object), BPPARAM = bpparam()) {
+        if (is.factor(f))
+            f <- as.character(f)
+        f <- factor(f, levels = unique(f))
+        BPPARAM <- backendBpparam(object@backend, BPPARAM)
+        unlist(bplapply(split(object, f),
+                        FUN = .estimate_precursor_intensity, ppm = ppm,
+                        tolerance = tolerance, method = method,
+                        msLevel = msLevel., BPPARAM = BPPARAM),
+               use.names = FALSE)
+    })

tests/testthat/test_Spectra-functions.R

@@ -690,16 +690,6 @@ test_that(".estimate_precursor_intensity works", {
     expect_true(all(is.na(res)))
 })
 
-test_that("estimatePrecursorIntensity works", {
-    fls <- msdata::proteomics(full.names = TRUE)[c(5, 3)]
-    second <- Spectra(fls[2], backend = MsBackendMzR())
-    both <- Spectra(fls, backend = MsBackendMzR())
-
-    res_second <- estimatePrecursorIntensity(second)
-    res_both <- estimatePrecursorIntensity(both)
-    expect_equal(res_second, res_both[510:length(res_both)])
-})
-
 test_that(".chunk_factor works", {
     res <- .chunk_factor(10, chunkSize = 3)
     expect_equal(res, as.factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4)))

tests/testthat/test_Spectra.R

@@ -1928,4 +1928,14 @@ test_that("asDataFrame works", {
     expect_identical(nrow(df), sum(sapply(peaksData(sp[1:3]), nrow)))
     expect_identical(ncol(df), 2L)
     expect_identical(names(df), c("mz", "intensity"))
-})
+})
+
+test_that("estimatePrecursorIntensity works", {
+    fls <- msdata::proteomics(full.names = TRUE)[c(5, 3)]
+    second <- Spectra(fls[2], backend = MsBackendMzR())
+    both <- Spectra(fls, backend = MsBackendMzR())
+
+    res_second <- estimatePrecursorIntensity(second)
+    res_both <- estimatePrecursorIntensity(both)
+    expect_equal(res_second, res_both[510:length(res_both)])
+})