Merge pull request #13 from jorainer/jomain

Jomain

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.0.2
+Version: 1.0.3
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "[email protected]",

NEWS.md

@@ -1,5 +1,9 @@
 # xcmsTutorials 1.0
 
+## Changes in 1.0.3
+
+- Add reference to the `MsQuality` paper.
+
 ## Changes in 1.0.2
 
 - Add the `msdata` to *depends* to ensure it's going to be installed.

vignettes/references.bib

@@ -117,3 +117,21 @@ @article{gattoMSnbaseEfficientElegant2020a
   language = {eng},
   pmid = {32902283}
 }
+
+@article{naake_msquality_2023,
+	title = {{MsQuality} - an interoperable open-source package for the calculation of standardized quality metrics of mass spectrometry data},
+	issn = {1367-4811},
+	doi = {10.1093/bioinformatics/btad618},
+	abstract = {MOTIVATION: Multiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows.
+RESULTS: The MsQuality package calculates 43 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass spectrometry-based measurement of a sample. It helps to identify low-quality measurements and track data quality. Its use of community-standard quality metrics facilitates comparability of quality assessment and control (QA/QC) criteria across datasets.
+AVAILABILITY: The R package MsQuality is available through Bioconductor at https://bioconductor.org/packages/MsQuality.
+SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
+	language = {eng},
+	journal = {Bioinformatics (Oxford, England)},
+	author = {Naake, Thomas and Rainer, Johannes and Huber, Wolfgang},
+	month = oct,
+	year = {2023},
+	pmid = {37812234},
+	keywords = {mass spectrometry, metabolomics, proteomics, quality control, R},
+	pages = {btad618},
+}

vignettes/xcms-preprocessing.Rmd

@@ -339,7 +339,7 @@ intensity(sp) |>
 
 The same operation can also be applied to the full data set. As an example we
 calculate below the total ion signal for each spectrum in the first file and
-determine the distribution of these using the `quantiles` function.
+determine the distribution of these using the `quantile` function.
 
 ```{r}
 #' Calculate the distribution of total ion signal of the first file
@@ -363,8 +363,8 @@ The total ion signals of the two data files is (as expected) similar. Through
 the `Spectra` object we have thus the possibility to inspect and explore the
 (raw) MS data of an experiment and use its functionality to create own quality
 assessment functions. Alternatively, also the `r Biocpkg("MsQuality")` package
-could be to calculate core MS quality metrics on a full experiment
-(`MsExperiment`) or individual data files (`Spectra`).
+[@naake_msquality_2023] could be to calculate core MS quality metrics on a full
+experiment (`MsExperiment`) or individual data files (`Spectra`).
 
 
 ## Data visualization
@@ -602,7 +602,7 @@ data |>
     filterRt(rt = c(175, 189)) |>
     filterMz(mz = serine_mz + c(-0.05, 0.05)) |>
     chromatogram() |>
-plot()
+    plot()
 ```
 
 The area of such a chromatographic peak is supposed to be proportional to the
@@ -636,7 +636,7 @@ focus on the mass peak for serine.
 #' Visualize the profile-mode mass peak for [M+H]+ of serine
 sps[1] |>
     filterMzRange(c(106.02, 106.07)) |>
-    plotSpectra()
+    plotSpectra(lwd = 2)
 abline(v = serine_mz, col = "#ff000080", lty = 3)
 ```
 
@@ -1455,7 +1455,8 @@ the definition of the features and display the first 6 rows
 
 ```{r correspondence-featureDefinitions}
 #' Definition of the features
-featureDefinitions(data) |> head()
+featureDefinitions(data) |>
+    head()
 ```
 
 Each row defines one feature and provides information on it's *m/z* (column
@@ -1480,7 +1481,8 @@ abundances and show the first 6 rows.
 
 ```{r}
 #' Get abundances for the first 6 features
-featureValues(data, method = "sum") |> head()
+featureValues(data, method = "sum") |>
+    head()
 ```
 
 We could now use this feature matrix for any downstream analysis. Such feature
@@ -1704,7 +1706,8 @@ simply use the `assay` function.
 
 ```{r}
 #' Get feature values
-assay(res) |> head()
+assay(res) |>
+    head()
 ```
 
 The `SummarizedExperiment` can be subset by rows and/or columns.