Event detection
Event detection is part of the Wiki learning path. Event detection identifies real blockade or signal-change events in nanopore recordings and extracts comparable features from them.
Core concepts
- Detection thresholds should be tied to baseline and local noise.
- Event boundaries affect dwell time, area, and shape metrics.
- Weak, overlapping, or multi-level events need careful validation.
- False positives can arise from noise, drift, bubbles, clogging, and analysis settings.
Event detection workflow
| Step | Description |
|---|---|
| 1 | Annotate experimental phases and sample addition times. |
| 2 | Estimate local baseline and noise. |
| 3 | Set candidate thresholds and minimum duration rules. |
| 4 | Merge, split, or reject events using documented criteria. |
| 5 | Export event tables and plots for review. |
Typical applications
- Quantifying blockade depth and dwell time.
- Comparing samples or pore variants.
- Building training data for classification models.
Common pitfalls
- Using one threshold for recordings with strong drift.
- Failing to test false positives on blank controls.
- Treating automated output as correct without visual review.