Table 1 The sensitivity and false discovery rate (FDR) of the different SNV detection algorithms with different simulation setups
From: Pysim-sv: a package for simulating structural variation data with GC-biases
Purity | Subclone1 | Subclone2 | GC-bias | Methods | Sensitivity | FDR |
|---|---|---|---|---|---|---|
Purity =1 | 1 | 0 | Yes | GATK | 0.97679 | 1.13×10−4 |
Varscan | 0.97892 | 2.36×10−3 | ||||
No | GATK | 0.97826 | 9.20×10−5 | |||
Varscan | 0.98134 | 2.05×10−3 | ||||
Purity =0.8 | 0.4 | 0.4 | Yes | GATK | 0.90253 | 1.93×10−3 |
Varscan | 0.92501 | 5.36×10−3 | ||||
No | GATK | 0.90853 | 8.32×10−4 | |||
Varscan | 0.93001 | 5.05×10−3 | ||||
Purity =0.5 | 0.4 | 0.1 | Yes | GATK | 0.81253 | 1.09×10−2 |
Varscan | 0.84501 | 5.34×10−2 | ||||
No | GATK | 0.81853 | 2.11×10−3 | |||
Varscan | 0.85001 | 4.51×10−2 |