---
name: tooluniverse-image-analysis
description: 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, image-derived measurement statistics, and image-based assay quantification.
disable-model-invocation: true
---
# 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:
```python
ratio = mean_A / mean_B # e.g., 0.29
percentage = ratio * 100 # e.g., 29
print(f"{percentage:.1f}%") # "29.0%" ← THIS is the answer
```
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
```python
import pandas as pd, numpy as np
from scipy import stats
from scipy.interpolate import BSpline, make_interp_spline
import statsmodels.api as sm
from statsmodels.formula.api import ols
from statsmodels.stats.power import TTestIndPower
from patsy import dmatrix, bs, cr
# Optional: skimage, cv2, tifffile
```
---
## Workflow Decision Tree
```
PRE-QUANTIFIED DATA (CSV/TSV) → Load → Parse question → Statistical analysis
RAW IMAGES (TIFF, PNG) → Load → Segment → Measure → Analyze (see references/)
Statistical comparison:
Two groups → t-test or Mann-Whitney
Multiple groups vs control → Dunnett's test
Two factors → Two-way ANOVA
Effect size → Cohen's d + power analysis
Regression:
Dose-response → Polynomial (quadratic/cubic)
Ratio optimization → Natural spline
Model comparison → R-squared, F-stat, AIC/BIC
```
---
## Analysis Workflow
### Phase 0: Question Parsing and Data Discovery
```python
import os, glob, pandas as pd
csv_files = glob.glob(os.path.join(".", '**', '*.csv'), recursive=True)
df = pd.read_csv(csv_files[0])
print(f"Shape: {df.shape}, Columns: {list(df.columns)}")
```
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
```python
from scripts.segment_cells import count_cells_in_image
result = count_cells_in_image(image_path="cells.tif", channel=0, min_area=50)
```
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`