Table 2 A step-by-step example of differentiating “unknown” from “negative” status using the MSN method
A. Define “positive” samples | |||
|---|---|---|---|
Sample | Read counts | Status | |
Mutant | Total (coverage) | ||
A | 10 | 20 | Positive |
B | 0 | 20 | TBD* |
C | 4 | 9 | Positive |
D | 0 | 8 | TBD* |
E | 0 | 5 | TBD* |
B. Separate “unknown” from “negative” statuses | |||
|---|---|---|---|
“Non-positive” sample | Rejected against positive samples (p value**) | Final status | |
vs A | vs C | ||
B | Yes (< 0.01) | Yes (< 0.01) | Negative |
D | Yes (< 0.05) | No | Unknown |
E | No | No | Unknown |