A very fundamental question. I'm running a large scale SWATH experiment with 3 experimental gps (control vs 1.5hr treatment vs 3 hrs treatment), for each experiment set, I have 4 biological replicates (ie 3 experimental gps x 4). I also run 3 technical replicates for each sample. what will be the best way to do the analysis in either Oneomics or Markerview?
Do you suggest:
1. look for differentially expressed proteins (eg cutoff 1.5 fold, p<0.05) in each biological replicate one by one first ie, (control vs 1.5hr treatment vs 3 hrs treatment), then manually check for those signifcantly expressed proteins which can be commonly detected cross all 4 biological replicates?
2. import all biological and technical samples together in one go without any prefiltering first, such that normalization can be done on all samples together. We then report those with significant changes based on P<0.05? this workflow will be much faster
In addition, regarding the library construction, is it true that the more IDA I inject, the better ID and quantification accuracy I'll get? so far it seems to be true for differnt samples in our trials.
Thanks for taking my questions