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 Data-first approach - Load and inspect all CSV/TSV measurement data before analysis Question-driven - Parse the exact statistic, comparison, or model requested Statistical rigor - Proper effect sizes, multiple comparison corrections, model selection Imaging-aware - Understand ImageJ/CellProfiler measurement columns (Area, Circularity, Round, Intensity) Workflow flexibility - Support both pre-quantified data (CSV) and raw image processing Precision - Match expected answer format (integer, range, decimal places) 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: 4 pi area/perimeter^2, range [0,1], 1.0 = perfect circle Round: Roundness = 4 area/(pi major_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

:

Cell Type

Density

Best Method

Notes

Nuclei (DAPI)

Low-Medium

Otsu + watershed

Standard approach

Nuclei (DAPI)

High

CellPose/StarDist

Handles touching

Colonies

Well-separated

Otsu threshold

Fast, reliable

Colonies

Touching

Watershed

Edge detection

Cells (phase)

Any

Adaptive threshold

Handles uneven illumination

Fluorescence

Low signal

Li threshold

More sensitive

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

:

Load CSV → group by Genotype

Calculate mean Area per genotype

Identify genotype with max mean Area

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

:

Load CSV → filter by hemisphere/sex if needed

Split by Condition (KD vs CTRL)

Calculate Cohen's d with pooled SD

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

:

Create Strain_Ratio labels

Run Dunnett's test for Area (vs control)

Run Dunnett's test for Circularity (vs control)

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 : Convert ratio strings to frequencies Fit natural spline model (df=4) Find peak via grid search Report peak frequency + confidence interval See: references/statistical_analysis.md "Regression Modeling" Quick Reference Table Task Primary Tool Reference Load measurement CSV pandas.read_csv() This file Group statistics df.groupby().agg() This file T-test scipy.stats.ttest_ind() statistical_analysis.md ANOVA statsmodels.ols + anova_lm() statistical_analysis.md Dunnett's test scipy.stats.dunnett() statistical_analysis.md Cohen's d Custom function (pooled SD) statistical_analysis.md Power analysis statsmodels TTestIndPower statistical_analysis.md Polynomial regression statsmodels.OLS + poly features statistical_analysis.md Natural spline patsy.cr() + statsmodels.OLS statistical_analysis.md Cell segmentation skimage.filters + watershed cell_counting.md Colony segmentation skimage.filters.threshold_otsu segmentation.md Fluorescence quantification skimage.measure.regionprops fluorescence_analysis.md Colocalization Pearson/Manders fluorescence_analysis.md Image loading tifffile, skimage.io image_processing.md Batch processing scripts/batch_process.py scripts/ Example Scripts Ready-to-use scripts in scripts/ directory: segment_cells.py - Cell/nuclei counting with watershed measure_fluorescence.py - Multi-channel intensity quantification batch_process.py - Process folders of images colony_morphometry.py - Measure colony area/circularity 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: references/statistical_analysis.md - All statistical tests, regression models references/cell_counting.md - Cell/nuclei counting protocols references/segmentation.md - Colony and object segmentation references/fluorescence_analysis.md - Intensity quantification, colocalization references/image_processing.md - Image loading, preprocessing, library selection 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