refactor: little fixes

NEWS.md

@@ -9,6 +9,8 @@
   an idealized bell shape (*beta*) during gap filling for centWave-based
   chromatographic peak detection with parameter `verboseBetaColumns = TRUE`.
 - Add `chromPeakSummary` generic (issue #705).
+- Add `chromPeakSummary()` method to calculate the *beta* quality metrics.
+
 
 ## Changes in version 4.3.3
 

R/AllGenerics.R

@@ -551,6 +551,18 @@ setGeneric("chromPeakSpectra", function(object, ...)
 #'
 #' Currently implemented methods/parameter classes are:
 #'
+#' - `BetaDistributionParam`: calculates the *beta_cor* and *beta_snr* quality
+#'   metrics as described in (Kumler 2023) representing the result from a
+#'   (correlation) test of similarity to a bell curve and the signal-to-noise
+#'   ratio calculated on the residulas of this test.
+#'
+#' @param BPPARAM Parallel processing setup. See [bpparam()] for details.
+#'
+#' @param chunkSize `integer(1)` defining the number of samples from which data
+#'     should be loaded and processed at a time.
+#'
+#' @param msLevel `integer(1)` with the MS level of the chromatographic peaks
+#'     on which the metric should be calculated.
 #'
 #' @param object an *xcms* result object containing information on
 #'     identified chromatographic peaks.
@@ -569,7 +581,15 @@ setGeneric("chromPeakSpectra", function(object, ...)
 #'
 #' @author Pablo Vangeenderhuysen, Johannes Rainer
 #'
+#' @md
+#'
+#' @references
+#'
+#' Kumler W, Hazelton B J and Ingalls A E (2023) "Picky with peakpicking:
+#' assessing chromatographic peak quality with simple metrics in metabolomics"
+#' *BMC Bioinformatics* 24(1):404. doi: 10.1186/s12859-023-05533-4
 #'
+#' @export
 setGeneric("chromPeakSummary", function(object, param, ...)
            standardGeneric("chromPeakSummary"))
 

R/XcmsExperiment.R

@@ -2024,11 +2024,11 @@ setMethod(
     if (!hasChromPeaks(object, msLevel = msLevel))
       stop("No ChromPeaks definitions for MS level ", msLevel, " present.")
     ## Define region to calculate metrics from for each file
-    cp <- chromPeaks(object)
-    f <- factor(cp[,"sample"],seq_along(object))
-    pal <- split.data.frame(cp[,c("mzmin","mzmax","rtmin","rtmax")],f)
+    cp <- chromPeaks(object, msLevel = msLevel)
+    f <- factor(cp[,"sample"], seq_along(object))
+    pal <- split.data.frame(cp[, c("mzmin", "mzmax", "rtmin", "rtmax")], f)
     names(pal) <- seq_along(pal)
-    ## Manual chunk processing because we have to split `object` and `pal`
+    ## Manual chunk processi ng because we have to split `object` and `pal`
     idx <- seq_along(object)
     chunks <- split(idx, ceiling(idx / chunkSize))
     pb <- progress_bar$new(format = paste0("[:bar] :current/:",
@@ -2042,7 +2042,8 @@ setMethod(
       .xmse_integrate_chrom_peaks(
         .subset_xcms_experiment(object, i = z, keepAdjustedRtime = TRUE,
                                 ignoreHistory = TRUE),
-        pal = pal[z], intFun = .chrom_peak_beta_metrics, BPPARAM = BPPARAM)
+        pal = pal[z], intFun = .chrom_peak_beta_metrics,
+        msLevel = msLevel, BPPARAM = BPPARAM)
     })
     res <- do.call(rbind, res)
     pb$tick()

tests/testthat/test_Param_classes.R

@@ -1005,9 +1005,6 @@ test_that("FilterIntensityParam works", {
 
 
 test_that("BetaDistributionParam works", {
-  skip_on_os(os = "windows", arch = "i386")
-
   res <- BetaDistributionParam()
-  expect_true(is(res, "BetaDistributionParam "))
-
+  expect_true(is(res, "BetaDistributionParam"))
 })

tests/testthat/test_XcmsExperiment.R

@@ -831,11 +831,11 @@ test_that(".chrom_peak_beta_metrics works", {
   x <- Spectra::peaksData(spectra(xmse[2L]))
   rt <- rtime(spectra(xmse[2L]))
   pks <- chromPeaks(xmse)[chromPeaks(xmse)[, "sample"] == 2L, ]
-  
+
   res <- .chrom_peak_beta_metrics(x, rt, pks, sampleIndex = 2L,
                                         cn = colnames(pks))
   expect_equal(nrow(res), nrow(pks))
- 
+
 })
 
 ## That's from XcmsExperiment-functions.R
@@ -1458,8 +1458,6 @@ test_that("chromPeakSummary,XcmsExperiment works", {
                      verboseBetaColumns = FALSE)
   xmse <- findChromPeaks(mse, param = p)
   mat <- chromPeakSummary(xmse,BetaDistributionParam())
-  expect_true(all(c("beta_cor", "beta_snr") %in% colnames(res))).
+  expect_true(all(c("beta_cor", "beta_snr") %in% colnames(mat)))
   expect_true(is.numeric(mat))
-
 })
-

vignettes/xcms.Rmd

@@ -433,42 +433,46 @@ chromPeakData(faahko)
 
 ### Chromatographic peak quality
 
-Based on the publication by Kumler et al. published in 2023 [@Kumler2023], new 
-quality metrics (`beta_cor` and `beta_snr`) were added to *xcms*. `beta_cor` 
-indicates how  bell-shaped the peak is and `beta_snr` is estimating the 
-signal-to-noise ratio using the residuals. These metrics can be calculated 
-during peak picking by setting `verboseBetaColumns = TRUE` in the 
-`CentWaveParam` object, or they can becalculated afterwards by using the 
-`chromPeakSummary` function with the `XcmsExperiment` object and 
-`BetaDistributionParam` parameter object as input.
+Based on the publication by Kumler et al. published in 2023 [@Kumler2023], new
+quality metrics (*beta_cor* and *beta_snr*) were added to *xcms*. *beta_cor*
+indicates how bell-shaped the chromatographic peak is and *beta_snr* is
+estimating the signal-to-noise ratio using the residuals from this fit. These
+metrics can be calculated during peak picking by setting `verboseBetaColumns =
+TRUE` in the `CentWaveParam` object, or they can be calculated afterwards by
+using the `chromPeakSummary()` function with the `XcmsExperiment` object and
+the `BetaDistributionParam` parameter object as input:
 
 ```{r peak-detection-chromPeakSummary}
-beta_metrics <- chromPeakSummary(faahko,BetaDistributionParam()) 
+beta_metrics <- chromPeakSummary(faahko, BetaDistributionParam())
 head(beta_metrics)
 
 ```
 
-Using summary statistics, one can explore the distribution of these metrics in 
+The result returned by `chromPeakSummary()` is thus a numeric matrix with the
+values for these quality estimates, one row for each chromatographic peak.
+Using summary statistics, one can explore the distribution of these metrics in
 the data.
 
 ```{r beta-metrics}
 summary(beta_metrics)
 ```
 
-Visual inspection gives a better idea of what these metrics represent in terms 
-of peak quality in the data at hand. This information can be used to e.g. 
-filter out peaks that don't meet a chosen quality metric threshold. In order to 
-plot the detected peaks, their EIC can be extracted with the function 
-`chromPeakChromatograms`. An example of a peak with a high `beta_cor` a peak 
-with a low `beta_cor` score is given below.
+Visual inspection gives a better idea of what these metrics represent in terms
+of peak quality in the data at hand. This information can be used to e.g.
+filter out peaks that don't meet a chosen quality metric threshold. In order to
+plot the detected peaks, their EIC can be extracted with the function
+`chromPeakChromatograms`. An example of a peak with a high *beta_cor* and for
+a peak  with a low *beta_cor* score is given below.
 
 ```{r chromPeakChromatograms, message=FALSE}
-eics <- chromPeakChromatograms(faahko)
+beta_metrics[c(4, 6), ]
+eics <- chromPeakChromatograms(
+    faahko, peaks = rownames(chromPeaks(faahko))[c(4, 6)])
 ```
 
 ```{r peak-quality-metrics, fig.widht = 10, fig.height = 5, fig.cap = "Plots of high and low quality peaks. Left: peak CP0004 with a beta_cor = 0.98, right: peak CP0006 with a beta_cor = 0.13."}
-peak_1 <- eics[4]
-peak_2 <- eics[6]
+peak_1 <- eics[1]
+peak_2 <- eics[2]
 par(mfrow = c(1, 2))
 plot(peak_1)
 plot(peak_2)