Hi Keren,
and many thanks for such a fast answer.
> The user does not need to infer connected regions. This is done by the program. Oops… Seems like I missed something here, I'll dig into it. And great if the program does it on his own.
>> b) Interactomics data : I have the feeling no interaction data are considered in the process (I mean informations such as "ARC2 in known to interact with ARP2"). Is that true ? > At least in the new version all interaction data is definitely being considered. The old version was before interaction data was part of MultiFit. OK, so It wasn't something I missed here.
>> c) I don't understand what run_multifit() exactly does. More precisely : >> 1. it appears a preliminary filter is performed on configurations. By configuration I mean the attribution of each subunit to one and only one region (I think it corresponds to a mapping, in the code). If this is the case, I don't think it is performed based on interactomics data, so what is it based upon ? >> 2. I have the feeling one and only one solution is output (the best score) per retained configuration. Am I right ? > run_multifit tests all possible configurations according to the sampled ones. I am not sure I understand that well… So, just for a confirmation : in the tutorial there are 7 subunits, hence 7!, that is ~5000 possible configurations. Do you mean all 5000 configurations are tested and the solution with the best score extracted ? ...Unless a first test is achieved to check for each subunit what regions are populated or deserted by the 30 pre-fitted solutions ?
>> d) Am I correct to say the cross correlation computations are only used in scoring (step 3), and not in pre fitting of subunits (step 2) ? Hence, if I am correct, the fft based cross correlation approach has been replaced by the neural/gmm approach for that particular step ? > FFT based fitting was a new addition following the point based matching used in the version we use. We allow for both options depending on the complexity and resolution of the complex you have. Just great !
--Ben