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.
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
- Data-first - Load and inspect all CSV/TSV before analysis
- Question-driven - Parse the exact statistic requested
- Statistical rigor - Effect sizes, multiple comparison corrections, model selection
- Imaging-aware - Understand ImageJ/CellProfiler columns (Area, Circularity, Round, Intensity)
- Precision - Match expected answer format (integer, range, decimal places)
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, tifffileWorkflow 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/BICAnalysis 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.
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 |
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.
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"
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