Table 3.
Overview of NO detection methods, statistical tools, and applications.
| NO detection method |
Type | Strength | Limitations | Common statistical tools |
Software tools |
|---|---|---|---|---|---|
| DAF-FM DA fluorescence | Semi-quantitative | High-sensitivity, cell-level resolution |
Snapshot data, photobleaching | ANOVA, t-tests, correlation | ImageJ, R, SPSS |
| Griess assay | Quantitative | Simple, cheap | Measures NO2- not NO directly | Linear regression, PCA | Excel, R |
| Electrochemical probes | Real-time | Dynamic monitoring possible | Expensive, low spatial resolution | Time-series, wavelet, SEM | MATLAB, Python, R |
| Imaging (confocal) | Visual | Spatial dynamics | Semi-quantitative | PCA, cluster analysis | Fiji, Python (OpenCV) |
| Omics integration (e.g., RNA-seq) | Multidimensional | Systems-level insights | Complex modeling required | ML, regression, clustering | TensorFlow, Scikit-learn, R |