Dear all,

   Currently I'm working with Relion and I find myself in the following 
situation: In each iteration there is a percentage (around 10-15% in the 
later iterations) of particles that changes classes. What I don't know 
is if these particles are the same for all the iterations or change 
every round of classification.

   To address this my idea is to classify my particles based on their 
stability along the 2D or 3D classification, meaning that those 
particles that change classes few times will be considered good, and 
those changing many times will be considered bad and removed from the 
dataset. Note that this is different from the "particle sorting" option 
in Relion, which only takes into account the correlation between each 
particle and the class it belongs to for one given iteration (my idea is 
to combine these two types of classification for a more complete 
cleaning of the dataset). This is not very difficult technically, since 
the data needed is in the *data.star files outputted by Relion. In fact 
I have written a very simple bash application that reads the data and 
awards points to the particles based on events such as "staying in the 
same class", "times stayed in the same class in a row" and such, placing 
more weight in the later iterations, which are more relevant in my 
opinion.

   My question is: does anyone use or know about something similar? I'm 
looking for a solution for Relion, but any other program that does this 
would also be welcome. Since I'm not a expert in this and I'm using a 
quite simple and arbitrary approach to scoring the particles I feel like 
there might already be a more established solution for my problem that I 
have overlooked.

   Thank you in advance,

  Jesus Gomez de Segura