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Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, image-derived measurement statistics, and image-based assay quantification.
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).
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., 29print(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.
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
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, tifffile
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
See references/statistical_analysis.md for complete implementations of grouped_summary, Dunnett's, Cohen's d, power analysis, polynomial/spline regression.
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
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)
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.
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.
