Metabolomics Society Forum

Kind of. If I take the sum of all features detected from A + C and B + C, making sure to account for overlap by setting an appropriate retention time and ppm windows, I get more features than if I did A + B + C together. I am not getting the intersect between any two or all three during processing in any case. Instead, what I see is that I get more features that are exclusive to A, B, or C when I processes samples in a pairwise manner, than I do when I process them together.

Depends why it is an outlier. RT shifts or just very different intensities? If the first it could make sense. If not then no I would say. If no shift but unique features pruning those features from the peaktable might be fine.

Hi Jan,

Thank you for your comment and the link. In the past, your PowerPoint was very helpful for me to learn more about XCMS.

Let me try to clarify. In my case, I have extracted the metabolome for cells under two different types of stress conditions as well as a control condition. When I processed the data, I set the classes of each group of data using the sampclass() function in xcms to represent which group the data was generated from. I have made sure that the minsamp parameters is below the number of total samples within each treatment group. I have also adjusted the extra and missing parameters to permit features that appear in one of the three groups to be retained. Yet, I get different peak tables if I process each case against the control separately, rather than if I process both cases together with the control at the same time. I do not understand why this is.

Thank you

Hello,

Something that I have noticed when I process my own data in a case control manner is that I get aligned peak tables (assuming I processes the data once for each of my two cases) with more unique features from the two peak tables than the peak table I get when I process my data using both cases and the same control. There are equivalent number of replicates of each case and the control. I know xcms is meant to be run in a case/control fashion, but does anyone know why I might observe this difference in the number of unique features?

Just to clarify a few things, I have used peak picking parameters that have been optimized to our instrument and the raw data. For the most part, they are similar. I have also set classes with respect to each experimental case, and set the retention time correction parameters to account for the appropriate number of samples that may be missing due to the fact that they are in only one treatment and not the other.

I would be very grateful if anyone had any knowledge of how these algorithms work so I could improve my ability to use them.

To view raw files? No. To browse converted files you can use mzMine.

To do the centroiding? Yes, msconvert from Proteowizard can, but from the docs seems not well. msconvert cannot use the Waters' centroiding as it can for other vendor formats. So it has to use its own supposedly inferior implementation.