Merge pull request #5 from rformassspectrometry/phili

Small updates

.github/workflows/build_deploy.yaml

@@ -1,13 +1,9 @@
 name: pkgdown
 
-on:
-  push:
-    branches:
-    - jomain
 
-# on:
-#   repository_dispatch:
-#     types: [trigger-workflow-2]
+on:
+  repository_dispatch:
+    types: [trigger-workflow-2]
 
 jobs:
   build-and-deploy:
@@ -32,10 +28,10 @@ jobs:
       - name: Verify Quarto Installation
         run: quarto --version
 
-      - name: Install rcmdcheck
-        run: |
-          install.packages('rcmdcheck')
-        shell: Rscript {0}
+      #- name: Install rcmdcheck
+      #  run: |
+      #    install.packages('rcmdcheck')
+      #  shell: Rscript {0}
 
       # - name: Check
       #   env:
@@ -54,10 +50,6 @@ jobs:
           install.packages('pkgdown')
         shell: Rscript {0}
 
-      # - name: Install package
-      #   if: github.ref == 'refs/heads/main'
-      #   run: R CMD INSTALL .
-
       - name: Build site
         env:
           QUARTO_PRINT_STACK: true

DESCRIPTION

@@ -1,11 +1,22 @@
 Package: metabonaut
 Title: Exploring and Analyzing LC-MS Data
-Version: 0.0.1
+Version: 0.0.2
 Authors@R:
     c(person(given = "Philippine", family = "Louail",
             email = "[email protected]",
             role = c("aut", "cre"),
             comment = "ORCID: 0009-0007-5429-6846"),
+     person(given = "Anna", family = "Tagliaferri",
+            email = "[email protected]",
+            role = "ctb", 
+            comment = "ORCID: 0009-0001-4044-4285"),
+     person(given = "Vinicius", family = "Verri Hernandes",
+            email = "[email protected]",
+            role = "ctb",
+            comment = "ORCID: 0000-0002-3057-6460"),
+     person(given = "Daniel", family = "Marques de Sá e Silva",
+            role = "ctb",
+            comment = "ORCID: 0000-0002-9674-042X"),
      person(given = "Johannes", family = "Rainer",
             email = "[email protected]",
             role = "aut",
@@ -18,7 +29,8 @@ Encoding: UTF-8
 LazyData: false
 Roxygen: list(markdown = TRUE)
 Depends:
-    R (>= 4.3)
+    R (>= 4.3),
+    BiocParallel (>= 1.8.0)
 Suggests:
     MsCoreUtils,
     xcms (>= 4.1.5),

Dockerfile

@@ -18,15 +18,10 @@ RUN gdebi --non-interactive quarto-linux-amd64.deb
 RUN mkdir -p /shared/data
 
 ## Install the required packages
-RUN Rscript -e "BiocManager::install(c('RCurl', 'xcms', 'MsExperiment', 'SummarizedExperiment', \
-    'Spectra', 'MetaboCoreUtils', 'limma', 'matrixStats', 'pander', 'RColorBrewer', \
-    'pheatmap', 'vioplot', 'ggfortify', 'gridExtra', 'AnnotationHub', 'CompoundDb', \
-    'MetaboAnnotation', 'RforMassSpectrometry/MsIO', 'RforMassSpectrometry/MsBackendMetaboLights', 'quarto'), \
-    ask = FALSE, dependencies = TRUE)" && \
-    Rscript -e "BiocManager::install('sneumann/xcms', ref = 'phili', ask = FALSE)"
+RUN Rscript -e "BiocManager::install(c('RforMassSpectrometry/MsIO', 'RforMassSpectrometry/MsBackendMetaboLights'), ask = FALSE, dependencies = TRUE)"
 
 ## Install the current package with vignettes
-RUN Rscript -e "devtools::install('.', dependencies = TRUE, type = 'source', build_vignettes = FALSE)"
+RUN Rscript -e "devtools::install('.', dependencies = TRUE, type = 'source', build_vignettes = FALSE, repos = BiocManager::repositories())"
 
 USER rstudio
 

README.md

@@ -1,10 +1,10 @@
 
 
-# Let's explore and learn to analyze untargeted metabolomics data
+## Let's explore and learn to analyze untargeted metabolomics data
 
 [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
 
-![metabonaut](man/figures/sitcker_draft.png)
+![metabonaut](man/figures/sitcker_small.png)
 
 Welcome to **Metabonaut**! :astronaut:
 
@@ -18,27 +18,32 @@ The primary workflow is the
 
 The full R code for all examples, along with detailed descriptions, is available 
 in the 
-[end-to-end-untargeted-metabolomics.Rmd](https://rformassspectrometry.github.io/metabonaut/vignettes/end-to-end-untargeted-metabolomics.Rmd) 
+[end-to-end-untargeted-metabolomics.qmd](https://rformassspectrometry.github.io/metabonaut/vignettes/a-end-to-end-untargeted-metabolomics.qmd) 
 file. This file can be opened in RStudio, allowing you to execute each individual 
-R command (see the section below for additional required R packages).
+R command.
 
 Other vignettes on this website are interlinked, and you can find a detailed 
 description of the dataset used throughout 
 [here](https://rformassspectrometry.github.io/metabonaut/articles/dataset-investigation.html).
 
 We strive for reproducibility. These workflows are designed to remain stable 
 over time, allowing you to run all the vignettes together as one comprehensive 
-"super-vignette."
+"super-vignette". Any **major** change will be document here and for smaller updates 
+check the [News](https://rformassspectrometry.github.io/metabonaut/news/index.html)
 
-## Important Notes
+## For R beginners
 
 The tutorials provided assume that users have basic knowledge of R and RMarkdown. 
 If you're unfamiliar with either, we recommend completing a short tutorial to help 
-you test the code and adapt it to your data. For RMarkdown, click 
-[here](https://bookdown.org/yihui/rmarkdown/). For R, check out 
-[this](https://learn-r.org/) or try an interactive course 
+you test the code and adapt it to your data. 
+The vignettes are written in Quarto format, to learn more about this go [here](https://quarto.org/docs/guide/), 
+this is a farily new format, and some functionallity are shared with the [RMarkdown](https://bookdown.org/yihui/rmarkdown/) format, therefore 
+learning about it can be usefull to you too. 
+
+For basic R course and documentation, I would recommand to check out 
+[this website](https://learn-r.org/) or try an interactive course 
 [here](https://swirlstats.com/students.html) for a fun introduction to basic R 
-programming.
+programming. This [cheatsheet](https://github.com/wurli/r-best-practice) could also be of help. 
 
 ## Known Issues
 

_pkgdown.yml

@@ -1,14 +1,16 @@
 url: https://rformassspectrometry.github.io/metabonaut/
 template:
   bootstrap: 5
-  bootswatch: flatly
-  light-switch: yes
+  bslib:
+    bootswatch: lux
+    pkgdown-nav-height: 100px
 navbar:
+  bg: dark
   title: Metabonaut
   left:
   - text: Installation instruction
     href: articles/install_v0.html
-  - text: Vignettes
+  - text: Workflows
     menu:
     - text: 'Dataset investigation: What to do when you get your data'
       href: articles/dataset-investigation.html
@@ -22,5 +24,4 @@ structure:
   right:
   - search
   - github
-  - lightswitch
 

vignettes/a-end-to-end-untargeted-metabolomics.qmd

@@ -14,6 +14,7 @@ vignette: >
 ```{r setup, include=FALSE}
 library(knitr)
 library(quarto)
+library(BiocStyle)
 knitr::opts_knit$set(root.dir = './')
 ```
 
@@ -42,10 +43,6 @@ and general tips on what should be done when you first get your data.
 Our workflow is therefore based on the following dependencies:
 
 ```{r packages-used, message=FALSE, warning=FALSE}
-## General bioconductor package
-library(BiocStyle)
-library(knitr)
-
 ## Data Import and handling
 library(readxl)
 library(MsExperiment)
@@ -114,18 +111,16 @@ configured through the `r Biocpkg("BiocParallel")` Bioconductor package. With
 the code below we use a *fork-based* parallel processing on unix system, and a
 *socket-based* parallel processing on the Windows operating system.
 
-```{r par-process, eval = FALSE}
-register(SerialParam())
+```{r par-process, message=FALSE, warning=FALSE}
 #' Set up parallel processing using 2 cores
-## if (.Platform$OS.type == "unix") {
-##     register(MulticoreParam(2))
-## } else {
-##     register(SnowParam(2))
-## }
+if (.Platform$OS.type == "unix") {
+    register(MulticoreParam(2))
+} else {
+    register(SnowParam(2))
+}
 
 ```
 
-
 # Data organisation
 
 The experimental data is now represented by a `MsExperiment` object from the

vignettes/alignment-to-external-dataset.qmd

@@ -38,18 +38,18 @@ library(MsBackendMetaboLights)
 library(xcms)
 library(MsExperiment)
 library(Spectra)
+library(vioplot)
 ```
 
 Setting parallel processing to improve the efficiency of the process:
 
 ```{r, message=FALSE, warning=FALSE}
-register(SerialParam())
 #' Set up parallel processing using 2 cores
-## if (.Platform$OS.type == "unix") {
-##     register(MulticoreParam(2))
-## } else {
-##     register(SnowParam(2))
-## }
+if (.Platform$OS.type == "unix") {
+    register(MulticoreParam(2))
+} else {
+    register(SnowParam(2))
+}
 
 ```
 
@@ -242,7 +242,7 @@ ref_vs_obs <- matchedRtimes(param)
 Now we can adjust the retention time of the LC-MS/MS dataset using the
 `adjustRtime()` function.
 
-```{r}
+```{r warning=FALSE}
 #' input into `adjustRtime()`
 lcms2 <- adjustRtime(lcms2, param = param)
 lcms2 <- applyAdjustedRtime(lcms2)
@@ -329,6 +329,8 @@ To quantitatively assess the quality of the alignment, we compute the distance
 between the chromatographic peaks in our LC-MS data and the anchor peaks (Lamas)
 both before and after the alignment.
 
+library(vioplot)
+
 ```{r}
 # Extract data for sample 3 directly
 ref_obs_sample_1 <- ref_vs_obs[["1"]]
@@ -347,8 +349,7 @@ distances <- data.frame(
 # Set factor levels for Alignment to ensure correct order
 distances$Alignment <- factor(distances$Alignment, levels = c("Before", "After"))
 
-# Plot the violin plot
-library(vioplot)
+# Plot distances between anchor peaks between the two runs before and after alignment.
 vioplot(Distance ~ Alignment, data = distances, xlab = "",
         rectCol = "#c4114580",
         lineCol = "white",
@@ -367,15 +368,15 @@ peaks in the file. Additionally, it is feasible to directly `plot()` the `param`
 object for the file of interest, showcasing the distribution of these
 chromatographic peaks along with the fitted model line.
 
-```{r}
-#' access summary of matches and model information
+```{r warning=FALSE}
+#' Access summary of matches and model information
 summary <- summarizeLamaMatch(param)
 summary
 
-#' coverage for each file
+#' Coverage for each file
 summary$Matched_peaks / summary$Total_peaks * 100
 
-#' access the information on the model of for the first file
+#' Access the information on the model of for the first file
 summary$Model_summary[[1]]
 
 #' Plot obs vs. lcms1 with fitting line

vignettes/dataset-investigation.qmd

@@ -71,9 +71,3 @@ separation was achieved using hydrophilic interaction liquid chromatography
     quality.
 -   Compare pool lc-ms and pool lc-ms/ms and show that we have better separation
     on the second run.
-
-```{r}
-getwd()
-list.files()
-list.dirs()
-```

vignettes/install_v0.qmd

@@ -13,23 +13,29 @@ vignette: >
 For manual installation, an [R](https://www.r-project.org/) version \>= 4.4.0 is
 required.
 
-# Install packages required for metabonaut tutorials
+[Notes]{.underline}: only a development version of `metabonaut` is available as
+of now but a stable version will come soon. Therefore, if you are having
+difficulty to run some part of the code we strongly recommend to use the Docker
+image, as it will have all the package necessary to run all the analysis
+presented in these workflows.
 
-Notes: only a development version of metabonaut is available as of now but a
-stable version will come soon.
+# Running workflows locally
 
-The following packages need to be installed for proper functioning of the
-tutorials:
+To install on your computer all the packages necessary for the workflows run the
+code as follow:
 
-Run the code as follow:
+BiocManager::install("rformassspectrometry/metabonaut",
+
+dependencies = TRUE, ask = FALSE, update = TRUE)
 
 ```{r eval=FALSE}
 install.packages("BiocManager")
-BiocManager::install(c('RCurl', 'xcms', 'MsExperiment', 'SummarizedExperiment',
-    'Spectra', 'MetaboCoreUtils', 'limma', 'matrixStats', 'pander', 
-    'RColorBrewer', 'pheatmap', 'vioplot', 'ggfortify', 'gridExtra', 
-    'AnnotationHub', 'CompoundDb', 'MetaboAnnotation', 
-    'RforMassSpectrometry/MsIO', 'RforMassSpectrometry/MsBackendMetaboLights'))
+
+## Packa
+BiocManager::install(c('RforMassSpectrometry/MsIO', 'RforMassSpectrometry/MsBackendMetaboLights'), ask = FALSE, dependencies = TRUE)
+
+BiocManager::install("rformassspectrometry/metabonaut",
+                     dependencies = TRUE, ask = FALSE, update = TRUE)
 ```
 
 # Docker image
@@ -45,16 +51,20 @@ code and examples from the vignettes can be evaluated within this environment
     installation information [here](https://docs.docker.com/desktop/).
 -   Get the [docker
     image](https://hub.docker.com/r/rformassspectrometry/metabonaut) of this
-    tutorial e.g. from the command line with
-    `docker pull rformassspectrometry/metabonaut:latest`.
+    tutorial e.g. from the command line with:
+
+```         
+    docker pull rformassspectrometry/metabonaut:latest
+```
+
 -   Start the docker container, either through the Docker Desktop, or on the
     command line with
 
 ```         
-docker run -e PASSWORD=bioc -p 8787:8787 rformassspectrometry metabonaut:latest
+docker run -e PASSWORD=bioc -p 8787:8787 rformassspectrometry/metabonaut:latest
 ```
 
 -   Enter [`http://localhost:8787`](http://localhost:8787) in a web browser and
     log in with username `rstudio` and password `bioc`.
--   In the RStudio server version: open any of the R-markdown (*.Rmd*) files in
-    the *vignettes* folder and evaluate the R code blocks in that document.
+-   In the RStudio server version: open any of the Quarto files in the
+    *vignettes* folder and evaluate the R code blocks in that document.