Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, im

Microscopy Image Analysis and Quantitative Imaging Data RULE ZERO — Check for pre computed results FIRST Before following any instruction below, scan the data folder for: executed.ipynb → read with tu run read executed notebook '{"data folder":"<path ","search":"<keyword "}' and cite its cell outputs as the authoritative answer Pre computed result files (CSV/TSV with names like results , deseq , enrich , stats , simplified.csv ) → read directly and report the requested value Canonical analysis scripts ( analysis.R , run .py , find .R , .Rmd ) → execute as is and read the output Only follow this skill's re analysis recipe below if none of the above exist. Re running from raw data produces different numbers than the published answer and is much slower (often 5 10× turn count). CRITICAL — "Relative proportion of A to B" defaults to PERCENTAGE When the question asks "What is the relative proportion of A to B" or "What percentage of A relative to B", report the value as a percentage (e.g., 29 for ratio 0.29), NOT a decimal ratio. Biology assay GTs use whole number percentage ranges like (25,30) , not (0.25,0.30) . Multiply your computed ratio by 100 before reporting: Only report as decimal/fraction if the question explicitly says "as a decimal", "between 0 and 1", or "as a fraction". Common error: reporting 0.29 when the GT range is (25,30) — graded as wrong even though the underlying ratio is correct. Production ready skill for analyzing microscopy derived measurement data using pandas, numpy, scipy, statsmodels, and scikit image. LOOK UP, DON'T GUESS When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. When to Use Microscopy measurement data (area, circularity, intensity, cell counts) in CSV/TSV Colony morphometry, cell counting statistics, fluorescence quantification Statistical comparisons (t test, ANOVA, Dunnett's, Mann Whitney, Cohen's d, power analysis) Regression models (polynomial, spline) for dose response or ratio data Imaging software output (ImageJ, CellProfiler, QuPath) NOT for : Phylogenetics, RNA seq DEG, single cell scRNA seq, statistics without imaging context. Core Principles 1. Data first Load and inspect all CSV/TSV before analysis 2. Question driven Parse the exact statistic requested 3. Statistical rigor Effect sizes, multiple comparison corrections, model selection 4. Imaging aware Understand ImageJ/CellProfiler columns (Area, Circularity, Round, Intensity) 5. Precision Match expected answer format (integer, range, decimal places) Required Packages Workflow Decision Tree Analysis Workflow Phase 0: Question Parsing and Data Discovery Common columns: Area, Circularity, Round, Genotype/Strain, Ratio, NeuN/DAPI/GFP. Phase 1 3: Grouped Stats → Statistical Testing → Regression See references/statistical analysis.md for complete implementations of grouped summary, Dunnett's, Cohen's d, power analysis, polynomial/spline regression. Common Patterns Pattern Example Question Workflow Colony Morphometry "Mean circularity of genotype with largest area?" Group by Genotype → max mean Area → report Circularity Cell Counting "Cohen's d for NeuN counts?" Filter → split by Condition → pooled SD → Cohen's d Multi Group Comparison "How many ratios equivalent to control?" Dunnett's for Area AND Circularity → count non significant in BOTH Regression "Peak frequency from natural spline?" Ratio→frequency → spline(df=4) → grid search peak → CI Raw Image Processing Segmentation: Nuclei → Otsu+watershed; Colonies → Otsu; Phase contrast → adaptive threshold. See references/segmentation.md , references/cell counting.md , references/image processing.md . R to Python Equivalents R Dunnett ( multcomp::glht ) → scipy.stats.dunnett() (scipy = 1.10) R natural spline ( ns(x, df=4) ) → patsy.cr(x, knots=...) with explicit quantile knots R t.test() → scipy.stats.ttest ind() R aov() → statsmodels.formula.api.ols() + sm.stats.anova lm() Answer Formatting "to the nearest thousand": int(round(val, 3)) Cohen's d: 3 decimal places Sample sizes: integer (ceiling) Ratios: string "5:1" "Relative proportion of A to B" — default to PERCENTAGE Question phrases like "relative proportion of A to B", "percentage of mean A relative to B", or "A as a fraction of B" are ambiguous: the answer could be the decimal ratio ( 0.29 ) or the percentage ( 29 ). In biology/microscopy assay contexts the convention is percentage (whole numbers like 25 30, not decimals like 0.25 0.30). When in doubt: Compute the decimal ratio first: r = mean(A) / mean(B) . Report BOTH r 100 (percentage) and r (decimal); flag the percentage as the primary answer. If the question specifies "as a decimal" or "between 0 and 1", report decimal only. If the question specifies "as a percentage" or "%", report percentage only. Common error: question asks "relative proportion of mutant area to wildtype" and the agent reports 0.29 when the GT range is (25, 30) . The grader marks this wrong even though the underlying computation is correct. Evidence Grading Grade Criteria Strong p < 0.001, d 0.8, N = 30/group Moderate p < 0.05, 0.5 <= d < 0.8 Weak p < 0.05, d < 0.5 or low N Insufficient p = 0.05 or N < 5/group Circularity near 1.0 = round/healthy; < 0.5 = irregular. Post hoc power < 0.80 = underpowered. References Scripts: segment cells.py , measure fluorescence.py , batch process.py , colony morphometry.py , statistical comparison.py Docs: statistical analysis.md , cell counting.md , segmentation.md , fluorescence analysis.md , image processing.md

tooluniverse-image-analysis