I edited the post to add additional informations
I did an experiment where I detect genomic events (mobile element integration - MEI) in different samples (10) divided in 3 groups across several time points (8). When a mobile element is integrated in a cell, it's irreversible. And each cell harbors only one integration. The probability to have the same integration point in two different samples is very (very) low. The position of integration is like a marker that can be traced in multiple time points. If the marker (i.e. the position of integration) is found in multiple time points, it show that this cell (or daughter-cells - as the integrated mobile element will be transmitted to all "clone" cells) is "recurrent" i.e. is being positively selected.
The data can be expressed as follow :
Sample 1 TP1 TP2 TP3 TP4 ... MEI 1 1 0 1 1 MEI 2 0 0 1 0 MEI 3 0 1 1 1 Sample 2 TP1 TP2 TP3 TP4 ... MEI 1 0 0 0 0 MEI 2 1 1 1 0 MEI 3 0 0 0 1 etc for the others samples... 0 = no event detected 1 = event detected
each event (MEI) represents a genomic position (chr-position). Most of the events are restricted to one samples. These data can be merged as follow :
#occurences MEI 1 4 MEI 2 3 MEI 3 3
by counting the number of times we detect each event across all time points (if an event arises in the same TP of two different samples we counted it as one).
Now my problem is to perform a test that can compare the 3 groups in term of event recurrence (more an event occurs at different time point, more it's recurrent). A big problem in my experiment is that I could not detect all events due to the sensibility of the technic used. For example for the sample 1 :
Sample 1 TP1 TP2 TP3 TP4 ... MEI 1 1 0 1 1
we should also see MEI 1 in TP2, but due to the limitation of the method we cannot...
TP1 TP2 TP3 TP4 ... MEI 2 0 0 1 0
It's complicated to tell if event 2 appears in TP3, or before ...
In summary : How to test for event recurrence across different time points in samples from different groups