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https://github.com/jorainer/metabolomics2018

Workshop illustrating mass spectrometry data analysis in R and use of the updated xcms functionality for the preprocessing of LC-MS data.
https://github.com/jorainer/metabolomics2018

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Workshop illustrating mass spectrometry data analysis in R and use of the updated xcms functionality for the preprocessing of LC-MS data.

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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3909299.svg)](https://doi.org/10.5281/zenodo.3909299)



- *2021-03-25*: provide a description on the *good practice* to define a

  phenodata/data files table containing also the names of the raw data files

  along with all sample information.

- *2020-06-15*: use new more data-driven gap-filling approach: `fillChromPeaks`

  with `ChromPeakAreaParam`.

- *2020-02-04*: add `refineChromPeaks` to allow *refinement* of peak detection

  results. Also, add the `quantify` method to extract the preprocessing results

  as `SummarizedExperiment`. Both required `xcms` version >= 2.9.2.

- *2019-09-29*: More updates and expansion of descriptions.

- *2019-06-20*: Updated to match `xcms` functionality available with

Bioconductor version 3.9.



# LC-MS data pre-processing with `xcms`



This workshop provides an overview of recent developments in Bioconductor to

work with mass spectrometry ([MSnbase](https://github.com/lgatto/MSnbase)) and

specifically LC-MS data ([xcms](https://github.com/sneumann/xcms)) and walks

through the preprocessing of a toy data set emphasizing on selection of

data-dependent settings for the individual pre-processing steps. The present

workshop represents an updated version of the workshop given at the Metabolomics

Society conference 2018 in Seattle (http://metabolomics2018.org).



Covered topics are:

- Data import and representation.

- Accessing, subsetting and visualizing data.

- Centroiding of profile MS data.

- Chromatographic peak detection.

- Empirically determine appropriate settings for the analyzed data set.

- Evaluation of identified peaks.

- Alignment (retention time correction).

- Correspondence (grouping of chromatographic peaks across samples).



The full R code of all examples along with comprehensive descriptions is

provided in the [xcms-preprocessing.Rmd](./xcms-preprocessing.Rmd) file. This

file can be opened with e.g. RStudio which allows execution of the individual R

commands (see section below for additionally required R packages). The R command

`rmarkdown::render("xcms-preprocessing.Rmd")` would generate the html file

[xcms-preprocessing.html](https://jorainer.github.io/metabolomics2018/xcms-preprocessing.html).



For those that can not attend the workshop: you can have a look at the

presentation online [xcms-preprocessing-ioslides.html](https://jorainer.github.io/metabolomics2018/xcms-preprocessing-ioslides.html).



## Prerequisites



The analysis in this document requires an R version >= 3.6.0 and recent versions

of the `MSnbase` and `xcms` (version >= 3.3.1 is needed) packages. The code

below installs all packages for the analysis.



```r

install("BiocManager")

BiocManager::install(c("xcms",

                       "MSnbase",

                       "msdata",

                       "magrittr",

                       "devtools",

                       "BiocParallel"))

```



## Files



- [xcms-preprocessing.Rmd](./xcms-preprocessing.Rmd): file containing the

  complete R code and expanded description. Can be converted to a html file with

  `rmarkdown::render("xcms-preprocessing.Rmd")`.



- [xcms-preprocessing-ioslides.Rmd](./xcms-preprocessing-ioslides.Rmd): R

  markdown file that is rendered (with

  `rmarkdown::render("xcms-preprocessing-ioslides.Rmd")` into the html

  (ioslides-based) presentation for the conference. This file contains most of

  the R commands from `xcms-preprocessing.Rmd` but only few descriptions. (outdated!)



- [xcms-preprocessing-bullets.Rmd](./xcms-preprocessing-bullets.Rmd): file with

  complete R code but strongly reduced descriptive content (in form of bullet

  points). This file is thought to be used for an interactive presentation with

  RStudio (i.e. live execution of commands). (outdated!)