Tooluniverse Image Analysis

This handles the statistical backend for microscopy experiments when you've already got measurement tables from ImageJ or CellProfiler. It's built around a specific workflow: colony morphometry, fluorescence quantification, cell counts, then statistical comparisons using pandas, scipy, and statsmodels. The skill emphasizes checking for pre-computed results before re-running analyses, which is practical for reproducibility but tells you this was written for a lab environment where notebooks get re-executed. Includes Dunnett's test for multi-group comparisons, Cohen's d for effect sizes, and polynomial or spline regression for dose-response curves. One gotcha clearly learned from experience: it defaults "relative proportion" questions to percentages, not decimal ratios, because that's what biology assay ground truths expect.