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Messages - CoreyG
As another follow up, you could look at "El MAVEN" (https://elucidatainc.github.io/ElMaven/). The website says the following:
Maven and El-MAVEN share following features:It is being updated fairly recently, with the latest release coming out just 5 days ago.
I haven't used MAVEN and by the lack of responses, it doesn't seem like many (any?) people here use it.
I would suggest trying a few things and letting us know if any worked/didn't:
I assume you are using MAVEN for the analysis part of the program?
For those who haven't heard, SCIEX are hosting an online symposium: Novel approaches to quantitative metabolomics.
There are no costs associated with the event and all talks are available on-demand.
You can find access to the event at https://sciex.com/events/metabolomics-online-symposium/
I'm not a user of HILIC columns, but I've heard a few times that retention time reproducibility can be quite sensitive to pH changes. So adding a buffer to the apolar solvent could help.
There are a few discussions going on in the forum about correcting for retention time drift. So if you can't sort it out, it is possible to correct some of the issues with software (such as XCMS).
Keep us informed of your trials - there are a lot of others that will benefit from your experiences!
The "onDisk" mode of xcms has allowed us to process ~1,000 samples comfortably on a desktop machine - although it does take some time. Retention time alignment and correspondence happens quite fast and hasn't given us any trouble at all.
The only problem we've had is with fillChromPeaks, where we need to run it single threaded due to memory constraints.
Thanks Johannes, I'll definitely be checking this out.
We use data from blank+ISTD samples in our targeted metabolomics workflow extensively.
Thanks for posting links to those articles, Guillermo.
The use of train-test (cross-validation) and a validate split for evaluation of batch correction is fantastic. Something that is often underappreciated.
I would like to try out qc-svrc, so I'll email you soon.
Jo, thanks for chiming in. That's an interesting point you make about RUVIII and something I'll keep an eye on.
It seems that linear models are being used by quite a few people, possibly with slight variations.
I ran some simulations using varying RSD% and number of QC samples. Overfitting is a possibility if QC numbers are low. Interestingly, including data from randomized samples reduces this chance. Perhaps including a weighting parameter in favor of QC samples might be prudent.
It might interesting to hear what has/hasn't worked for others in the forum.
I'll throw in my 2 cents.
Our lab has been experimenting with RUVIII (a variant of the 'PCA' method used by Wehrens). It can be found in the r package 'ruv' on cran.
RUVIII estimates the factors of unwanted variation using replicate (QC) samples and internal standards (or control metabolites).
So far it has been pretty effective at reducing the coefficient of variation of QC samples (not the sample QC samples used above, but rather a validation set).
I'm getting used to XCMS3, but I hope I can help you anyway.
You might be getting unstuck due to differences in XCMSnExp and xcmsSet classes. Using the 'as' function, you can convert an XCMSnExp object to an xcmsSet object.
I think all of your code will work if you replace the first line with:
xs.fill <- as(xgF,"xcmsSet")
Please let me (and everyone else) know if this works for you