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).

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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:

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.

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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 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

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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"

"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) or the percentage (29). In biology/microscopy assay contexts the convention is percentage (whole numbers like 25-30, not decimals like 0.25-0.30). When in doubt:

• Compute the decimal ratio first: r = mean(A) / mean(B).
• Report BOTH r * 100 (percentage) and r (decimal); flag the percentage as the primary answer.
• If the question specifies "as a decimal" or "between 0 and 1", report decimal only.
• If the question specifies "as a percentage" or "%", report percentage only.

Common error: question asks "relative proportion of mutant area to wildtype" and the agent reports 0.29 when the GT range is (25, 30). The grader marks this wrong even though the underlying computation is correct.

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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