DATA NORMALIZATION
Normalize data to appropriate controls for meaningful comparison. Express toxicity data as percent of vehicle control. For efficacy, normalize to baseline or positive control response. Account for plate effects using per-plate normalization. Z-score transformation enables cross-experiment comparison and quality metrics.
DOSE-RESPONSE ANALYSIS
- 4-Parameter Model: y = Bottom + (Top-Bottom)/(1+10^((LogIC50-x)*HillSlope))
- IC50/EC50: Report with 95% confidence intervals from curve fit
- Hill Slope: Indicates steepness; values near 1 suggest single-site binding
- R² Value: Goodness of fit; aim for >0.9 for regulatory submissions
- Software: GraphPad Prism, R (drc package), Python (scipy)
REPRODUCIBILITY METRICS
- Z' Factor: Screen quality metric; Z' > 0.5 indicates excellent assay
- CV%: Coefficient of variation for replicate wells; target <20%
- Signal:Background: Dynamic range between positive and negative controls
- Inter-Plate CV: Variability across plates within experiment
- Inter-Experiment CV: Variability across independent experiments
REPORTING STANDARDS
Follow MIAME/MINSEQE-like standards for complete experimental documentation. Report all data points, not just means. Include raw data in supplementary materials for regulatory submissions. Document outlier exclusion criteria prospectively. Use FAIR principles (Findable, Accessible, Interoperable, Reusable) for data sharing.