Table 1 Workflow of Clustered Gene Filtering (CGF)

Input: Gene data matrix A ∈ {0, 1}^m × n, distance threshold d _CGF , group element threshold n _CGF .

1: Sum A by row and sort the result in descent order, and then obtain the sorted index A ^* _sum ;

2: Initialize each element as ungrouped;

3: For each ungrouped element i in A ^* _sum :

 (a) For each ungrouped element j in A ^* _sum other than i:

  i. Calculate the similarity d(A(i, :), A(j, :));

  ii. If d(A(i, :), A(j, :)) > d _CGF , assign j into the group of i;

4: Set the output gene index set g _out  = ∅;

5: For each group c of A after step 3:

 (a) If group element number n _c  ≥ n _CGF , select the top n _CGF genes with the highest mutation occurrence frequency as g _c ;

 (b) g _out  = g _out  ∪ g _c ;

6: Apply the index set g _out on A and get the filtered gene data A _CGF  = A(g _out , :);

Output: A _CGF , i.e. the gene data after CGF