| 0 | 1 function [MASKS]=get_nonparametric_masks(CFG,reads1,reads2) | 
|  | 2 | 
|  | 3 %What fracitons should be choosen for the cutoff | 
|  | 4 cen_arr=0.1:0.1:1; | 
|  | 5 | 
|  | 6 % Define the mask which should be used in order to mask high | 
|  | 7 % expresse genes | 
|  | 8 MASKS=zeros(length(cen_arr),size(reads1,2)); | 
|  | 9 | 
|  | 10 COUNTER=1; | 
|  | 11 for censor_frac= cen_arr | 
|  | 12     temp_reads1=reads1; | 
|  | 13     temp_reads2=reads2; | 
|  | 14     %cut to relvant position | 
|  | 15     read_coverage=sum(reads1,1)+sum(reads2,1); | 
|  | 16     % get positions with a positive coverage | 
|  | 17     nonzero_position=read_coverage>0; | 
|  | 18     %Determine the cutoff values | 
|  | 19     sorted_coverage=sort(read_coverage(nonzero_position)); | 
|  | 20     nr_of_nonzero_positions=sum(nonzero_position); | 
|  | 21     relevant_positions=read_coverage<=sorted_coverage(ceil(nr_of_nonzero_positions*censor_frac)); | 
|  | 22     MASKS(COUNTER,relevant_positions)=1; | 
|  | 23     COUNTER=COUNTER+1; | 
|  | 24 end |