LFQ MBR FDR algorithm needed.

[ypriverol]

Description of the Feature

During the benchmark of quantms using LFQ and MBR (issues #300 #301 #287) we developed a new probabilistic algorithm based on SVM that control the number of false positives in a better way than previous proteomicsLFQ algorithm (based on number of samples where the feature is found).

However, the current algorithm produces better reliable results issues #301 #287 we should aim in ProteomicsLFQ a better FDR control algorithm that only use one parameter. In addition, would be great to improve the algorithm and feature detection. From my point of view, these are the priorities for that algorithm:

• [ ] Implement an FDR-based approach for MBR reducing the number of parameters.
• [ ] Improve the feature detection, including the possibility to do feature transfer across any msrun in the experiment. I think OpenMS only transfer features across samples in the same condition, however MQ uses all msruns in the experiment, which may be the source of the differences between tools.
• [ ] Implement the MBRs for TMT datasets similar to the following manuscript https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00209

We can discuss the details @timosachsenberg @jpfeuffer @daichengxin.

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