Open vlagani opened 4 years ago
You might be interested in the rule set here: https://github.com/stanstrup/commonMZ/
My understanding:
Yes, I believe as you said that setting both to 1 will make all adducts have equal weight. The annotation hypothesis with the highest number of annotated peaks would thus win no matter how obscure the combination is. That is probably not a good idea...
Hi, at the end we decided to use CAMERA functions (i.e., "CAMERA::readLists()" and "CAMERA::generateRules()") for creating a custom set of rules starting from specific ions, neutral additions and neutral losses . Thanks @stanstrup for your kind reply.
I am processing a set of metabolomics profiles using xcms+CAMERA. Particularly, and I am trying to create a custom rule table for adduct identification. Unfortunately, I have not found much material on the subject, except section 6 of the "LC-MS Peak Annotation and Identification with CAMERA" vignette. Is there any additional resource where I can better learn how to create a rule table? What baffles me is the usage of the "quasi" and "ips" fields, and the corresponding concepts of annotation group and rule score. In my understanding, setting all quasi and ips values to 1 should indicate that all candidate adducts must be considered independently and have the same weight. Is this correct?