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. Designed for BixBench imaging questions covering colony morphometry, cell counting, fluorescence quantification, regression modeling, and statistical comparisons.

IMPORTANT: This skill handles complex multi-workflow analysis. Most implementation details have been moved to references/ for progressive disclosure. This document focuses on high-level decision-making and workflow orchestration.

When to Use This Skill

Apply when users:

• Have microscopy measurement data (area, circularity, intensity, cell counts) in CSV/TSV
• Ask about colony morphometry (bacterial swarming, biofilm, growth assays)
• Need statistical comparisons of imaging measurements (t-test, ANOVA, Dunnett's, Mann-Whitney)
• Ask about cell counting statistics (NeuN, DAPI, marker counts)
• Need effect size calculations (Cohen's d) and power analysis
• Want regression models (polynomial, spline) fitted to dose-response or ratio data
• Ask about model comparison (R-squared, F-statistic, AIC/BIC)
• Need Shapiro-Wilk normality testing on imaging data
• Want confidence intervals for peak predictions from fitted models
• Questions mention imaging software output (ImageJ, CellProfiler, QuPath)
• Need fluorescence intensity quantification or colocalization analysis
• Ask about image segmentation results (counts, areas, shapes)

BixBench Coverage: 21 questions across 4 projects (bix-18, bix-19, bix-41, bix-54)

NOT for (use other skills instead):

• Phylogenetic analysis → Use tooluniverse-phylogenetics
• RNA-seq differential expression → Use tooluniverse-rnaseq-deseq2
• Single-cell scRNA-seq → Use tooluniverse-single-cell
• Statistical regression only (no imaging context) → Use tooluniverse-statistical-modeling

Core Principles

1. Data-first approach - Load and inspect all CSV/TSV measurement data before analysis
2. Question-driven - Parse the exact statistic, comparison, or model requested
3. Statistical rigor - Proper effect sizes, multiple comparison corrections, model selection
4. Imaging-aware - Understand ImageJ/CellProfiler measurement columns (Area, Circularity, Round, Intensity)
5. Workflow flexibility - Support both pre-quantified data (CSV) and raw image processing
6. Precision - Match expected answer format (integer, range, decimal places)
7. Reproducible - Use standard Python/scipy equivalents to R functions

Required Python Packages

# Core (MUST be installed)
import pandas as pd
import 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 (for raw image processing)
import skimage
import cv2
import tifffile

Installation:

pip install pandas numpy scipy statsmodels patsy scikit-image opencv-python-headless tifffile

High-Level Workflow Decision Tree

START: User question about microscopy data
│
├─ Q1: What type of data is available?
│  │
│  ├─ PRE-QUANTIFIED DATA (CSV/TSV with measurements)
│  │  └─ Workflow: Load → Parse question → Statistical analysis
│  │     Pattern: Most common BixBench pattern (bix-18, bix-19, bix-41, bix-54)
│  │     See: Section "Quantitative Data Analysis" below
│  │
│  └─ RAW IMAGES (TIFF, PNG, multi-channel)
│     └─ Workflow: Load → Segment → Measure → Analyze
│        See: references/image_processing.md
│
├─ Q2: What type of analysis is needed?
│  │
│  ├─ STATISTICAL COMPARISON
│  │  ├─ Two groups → t-test or Mann-Whitney
│  │  ├─ Multiple groups → ANOVA or Dunnett's test
│  │  ├─ Two factors → Two-way ANOVA
│  │  └─ Effect size → Cohen's d, power analysis
│  │  See: references/statistical_analysis.md
│  │
│  ├─ REGRESSION MODELING
│  │  ├─ Dose-response → Polynomial (quadratic, cubic)
│  │  ├─ Ratio optimization → Natural spline
│  │  └─ Model comparison → R-squared, F-statistic, AIC/BIC
│  │  See: references/statistical_analysis.md
│  │
│  ├─ CELL COUNTING
│  │  ├─ Fluorescence (DAPI, NeuN) → Threshold + watershed
│  │  ├─ Brightfield → Adaptive threshold
│  │  └─ High-density → CellPose or StarDist (external)
│  │  See: references/cell_counting.md
│  │
│  ├─ COLONY SEGMENTATION
│  │  ├─ Swarming assays → Otsu threshold + morphology
│  │  ├─ Biofilms → Li threshold + fill holes
│  │  └─ Growth assays → Time-lapse tracking
│  │  See: references/segmentation.md
│  │
│  └─ FLUORESCENCE QUANTIFICATION
│     ├─ Intensity measurement → regionprops
│     ├─ Colocalization → Pearson/Manders
│     └─ Multi-channel → Channel-wise quantification
│     See: references/fluorescence_analysis.md
│
└─ Q3: When to use scikit-image vs OpenCV?
   ├─ scikit-image: Scientific analysis, measurements, regionprops
   ├─ OpenCV: Fast processing, real-time, large batches
   └─ Both: Often interchangeable for basic operations
   See: references/image_processing.md "Library Selection Guide"

Quantitative Data Analysis Workflow

Phase 0: Question Parsing and Data Discovery

CRITICAL FIRST STEP: Before writing ANY code, identify what data files are available and what the question is asking for.

import os, glob, pandas as pd

# Discover data files
data_dir = "."
csv_files = glob.glob(os.path.join(data_dir, '**', '*.csv'), recursive=True)
tsv_files = glob.glob(os.path.join(data_dir, '**', '*.tsv'), recursive=True)
img_files = glob.glob(os.path.join(data_dir, '**', '*.tif*'), recursive=True)

# Load and inspect first measurement file
if csv_files:
    df = pd.read_csv(csv_files[0])
    print(f"Shape: {df.shape}")
    print(f"Columns: {list(df.columns)}")
    print(df.head())
    print(df.describe())

Common Column Names:

• Area: Colony or cell area in pixels or calibrated units
• Circularity: 4piarea/perimeter^2, range [0,1], 1.0 = perfect circle
• Round: Roundness = 4area/(pimajor_axis^2)
• Genotype/Strain: Biological grouping variable
• Ratio: Co-culture mixing ratio (e.g., "1:3", "5:1")
• NeuN/DAPI/GFP: Cell marker counts or intensities

Phase 1: Grouped Statistics

def grouped_summary(df, group_cols, measure_col):
    """Calculate summary statistics by group."""
    summary = df.groupby(group_cols)[measure_col].agg(
        Mean='mean',
        SD='std',
        Median='median',
        Min='min',
        Max='max',
        N='count'
    ).reset_index()
    summary['SEM'] = summary['SD'] / np.sqrt(summary['N'])
    return summary

# Example: Colony morphometry by genotype
area_summary = grouped_summary(df, 'Genotype', 'Area')
circ_summary = grouped_summary(df, 'Genotype', 'Circularity')

For detailed statistical functions, see: references/statistical_analysis.md

Phase 2: Statistical Testing

Decision guide:

• Normality test needed? → Shapiro-Wilk
• Two groups comparison? → t-test or Mann-Whitney
• Multiple groups vs control? → Dunnett's test
• Multiple groups, all comparisons? → Tukey HSD
• Two factors? → Two-way ANOVA
• Effect size? → Cohen's d
• Sample size planning? → Power analysis

See: references/statistical_analysis.md for complete implementations

Phase 3: Regression Modeling

When to use each model:

• Polynomial (quadratic/cubic): Smooth dose-response, clear peak
• Natural spline: Flexible, non-parametric, handles complex patterns
• Linear: Simple relationships, checking for trends

Model comparison metrics:

• R-squared: Overall fit (higher = better)
• Adjusted R-squared: Penalizes complexity
• F-statistic p-value: Model significance
• AIC/BIC: Compare non-nested models

See: references/statistical_analysis.md for complete implementations

Raw Image Processing Workflow

When Processing Raw Images

Workflow: Load → Preprocess → Segment → Measure → Export

# Quick start for cell counting
from scripts.segment_cells import count_cells_in_image

result = count_cells_in_image(
    image_path="cells.tif",
    channel=0,  # DAPI channel
    min_area=50
)
print(f"Found {result['count']} cells")

Segmentation Method Selection

Decision guide:

See: references/segmentation.md and references/cell_counting.md for detailed protocols

Library Selection: scikit-image vs OpenCV

Use scikit-image when:

• Scientific measurements needed (area, perimeter, intensity)
• regionprops for object properties
• Publication-quality analysis
• Easier syntax for scientists

Use OpenCV when:

• Processing large image batches
• Speed is critical
• Real-time processing
• Advanced computer vision features

Both work for:

• Thresholding, filtering, morphological operations
• Basic image transformations
• Most segmentation tasks

See: references/image_processing.md "Library Selection Guide"

Common BixBench Patterns

Pattern 1: Colony Morphometry (bix-18)

Question type: "Mean circularity of genotype with largest area?"

Data: CSV with Genotype, Area, Circularity columns

Workflow:

1. Load CSV → group by Genotype
2. Calculate mean Area per genotype
3. Identify genotype with max mean Area
4. Report mean Circularity for that genotype

See: references/segmentation.md "Colony Morphometry Analysis"

Pattern 2: Cell Counting Statistics (bix-19)

Question type: "Cohen's d for NeuN counts between conditions?"

Data: CSV with Condition, NeuN_count, Sex, Hemisphere columns

Workflow:

1. Load CSV → filter by hemisphere/sex if needed
2. Split by Condition (KD vs CTRL)
3. Calculate Cohen's d with pooled SD
4. Report effect size

See: references/statistical_analysis.md "Effect Size Calculations"

Pattern 3: Multi-Group Comparison (bix-41)

Question type: "Dunnett's test: How many ratios equivalent to control?"

Data: CSV with multiple co-culture ratios, Area, Circularity

Workflow:

1. Create Strain_Ratio labels
2. Run Dunnett's test for Area (vs control)
3. Run Dunnett's test for Circularity (vs control)
4. Count groups NOT significant in BOTH tests

See: references/statistical_analysis.md "Dunnett's Test"

Pattern 4: Regression Optimization (bix-54)

Question type: "Peak frequency from natural spline model?"

Data: CSV with co-culture frequencies and Area measurements

Workflow:

1. Convert ratio strings to frequencies
2. Fit natural spline model (df=4)
3. Find peak via grid search
4. Report peak frequency + confidence interval

See: references/statistical_analysis.md "Regression Modeling"

Quick Reference Table

Example Scripts

Ready-to-use scripts in scripts/ directory:

1. segment_cells.py - Cell/nuclei counting with watershed
2. measure_fluorescence.py - Multi-channel intensity quantification
3. batch_process.py - Process folders of images
4. colony_morphometry.py - Measure colony area/circularity
5. statistical_comparison.py - Group comparison statistics

Usage:

# Count cells in image
python scripts/segment_cells.py cells.tif --channel 0 --min-area 50

# Batch process folder
python scripts/batch_process.py input_folder/ output.csv --analysis cell_count

Detailed Reference Guides

For complete implementations and protocols:

1. references/statistical_analysis.md - All statistical tests, regression models
2. references/cell_counting.md - Cell/nuclei counting protocols
3. references/segmentation.md - Colony and object segmentation
4. references/fluorescence_analysis.md - Intensity quantification, colocalization
5. references/image_processing.md - Image loading, preprocessing, library selection
6. references/troubleshooting.md - Common issues and solutions

Important Notes

Matching R Statistical Functions

Some BixBench questions use R for analysis. Python equivalents:

• R's Dunnett test (multcomp::glht) → scipy.stats.dunnett() (scipy ≥ 1.10)
• R's natural spline (ns(x, df=4)) → patsy.cr(x, knots=...) with explicit quantile knots
• R's t-test (t.test()) → scipy.stats.ttest_ind()
• R's ANOVA (aov()) → statsmodels.formula.api.ols() + sm.stats.anova_lm()

See: references/statistical_analysis.md for exact parameter matching

Answer Formatting

BixBench expects specific formats:

• "to the nearest thousand": int(round(val, -3))
• Percentages: Usually integer or 1-2 decimal places
• Cohen's d: 3 decimal places
• Sample sizes: Always integer (ceiling)
• Ratios: String format "5:1"

Completeness Checklist

Before returning your answer, verify:

• Loaded all data files and inspected column names
• Identified the specific statistic or model requested
• Used correct grouping variables and filter conditions
• Applied correct rounding or format
• For "how many" questions: counted correctly based on criteria
• For statistical tests: used appropriate multiple comparison correction
• For regression: properly prepared and transformed data
• Double-checked direction of comparisons
• Verified answer falls within expected range

Getting Help

• Start with decision tree at top of this file
• Check relevant reference guide for detailed protocol
• Use example scripts as templates
• See troubleshooting guide for common issues
• All statistical implementations in statistical_analysis.md