tooluniverse-image-analysis

# 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