Johannes Rainer

Hi @cbroeckl , yes, with the `XCMSnExp` we might indeed have a larger memory footprint than before - mostly because there have been added more and more information (spectra header...

I'm not at all familiar with MSe data, so I would need some more info on that to be able to answer. So you have an MSe mzML file, does...

Update: have a look at this issue https://github.com/sneumann/xcms/issues/451 It should be possible to analyze also MSe data, you can do first *classical* detection in MS1 and then run a second...

Yes, Obiwarp uses one sample as reference so in theory this should be possible. I am however a little reluctant to implement here something as this sounds simple at first,...

I did the same - saving each `XCMSnExp` object (from a single mzML file) as a RData object and then, when everything was done, combining all of them into a...

For the `boxplot` I would suggest to plot the log2 transformed intensities instead of the natural scale values (something like `boxplot(lapply(tc, log2), ...`). For the base peak chromatogram, what puzzles...

Thanks for the suggestion - but I would rather keep it with file names, as *sample* names are not mandatory and we can not expect that users provide this information...

You can customize the size of the *plot margins* of an R plot (i.e. the area outside of the actual plot where the axes labels are) with the `"mar"` parameter...

The resulting `Spectra` could then eventually be filtered restricting to peaks of interest (based on their expected m/z) using the [`Spectra::filterMzValues`](https://rformassspectrometry.github.io/Spectra/reference/Spectra.html#data-subsetting-filtering-and-merging) function.

I'm also running our preprocessings using slurm on our cluster - but I'm defining one Rmd file that defines the whole analysis. So I submit the `rmarkdown::render(".Rmd")` as a job...