tooluniverse-image-analysis

Microscopy Image Analysis and Quantitative Imaging Data

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.

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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.

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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)

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Required Packages

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

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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

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Analysis Workflow

Phase 0: Question Parsing and Data Discovery

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.

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Common BixBench Patterns

Pattern	Example Question	Workflow
Colony Morphometry (bix-18)	"Mean circularity of genotype with largest area?"	Group by Genotype → max mean Area → report Circularity
Cell Counting (bix-19)	"Cohen's d for NeuN counts?"	Filter → split by Condition → pooled SD → Cohen's d
Multi-Group (bix-41)	"How many ratios equivalent to control?"	Dunnett's for Area AND Circularity → count non-significant in BOTH
Regression (bix-54)	"Peak frequency from natural spline?"	Ratio→frequency → spline(df=4) → grid search peak → CI

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Raw Image Processing

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.

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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"

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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.

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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