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Hurdle Model on DIA-NN Data #65
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I'll only answer the part I'm more familiar with. The hurdle model tests specifically for differential detection, and in the absence thereof, differential abundance. proDA uses a dropout model that models the probability to missing a feature based on its abundance, and then uses this to test for differential abundance (even when no abundances have been measured in one condition). I don't remember what DEqMS does, and haven't used it. |
Thanks, @lgatto! This is helpful. I'll await your colleagues guidance regarding application of msqrob2 to DIA-NN outputs. Looking forward to trying it out. |
@ococrook I just wanted to circle back and see if you had some insight into this query with regards to DIA-NN outputs as I'd love to be able to use your fantastic tool? |
@ococrook Hope you are doing well. I wanted to check in and see if you had a chance to review my query so that I may utilize your wonderful tool? |
Hi! Sorry for delayed response, yes I would think that's a sensible model input. I would ask @lievenclement to clarify though as I didn't develop the tool |
Thanks, @ococrook! @lievenclement any feedback/thoughts on the use of DIA-NN data as inputs for your wonderful tool? |
@lievenclement @lgatto @ococrook Thank you for a great tool! I am looking to fit a hurdle model on data I have run through DIANN. In order to do this, should I be utilizing the protein intensity values and the number of precursors mapped to that protein as inputs to the model?
In addition, for my learning, what is the difference between the hurdle model approach and the approaches used by DEqMS and proDA that also seem to model peptide counts/missingness?
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