Table 1. Algorithm for barcode enrichment using a k-mer frequency selection model.

a is the seed barcode. b represents all of the other barcodes. T is the target barcode set. F is a featured unique k-mer set with high frequency in each iteration. Threshold is the relation threshold between barcodes, which is controlled by the g parameter. k₁ is the number of iterations.

Algorithm 1 IterCluster using k-mer frequency select model
1. Generate seed barcode a
2. For each barcode(b):
3. if common unique k-mer (a,b) >Threshold:
4. add b to T
5. End for
6. Statistic unique k-mer frequency of reads in T, select the unique k-mer with high frequency to F
7. Fork1 iteration:
8. Get optional barcode set base on adjacency matrix
9. For each barcode(b) in optional barcode set
10. if common unique k-mer (b,F) >Threshold:
11. add b to T
12. Statistic unique k-mer frequency of reads in T
13. Select unique k-mer with high frequency to F
14. End for
15. End for