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., 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.

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, radiology/DICOM/CT/MRI/PET series and cohort discovery (use tooluniverse-medical-imaging-radiology).

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)

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

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.

Common Patterns

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.

Deep-learning segmentation (Cellpose) as an alternative to classical CV

Cellpose_segment_image runs the Cellpose deep-learning model locally on a real image file (.tif/.tiff/.png/.jpg/.jpeg/.bmp) and returns per-object area/centroid plus an optional label-mask image -- reach for it when Otsu/watershed under- or over-segments touching cells or irregular shapes that classical thresholding handles poorly. model_type="cyto3" (default) segments whole cells/cytoplasm; model_type="nuclei" segments nuclei. Requires the optional cellpose package (pip install cellpose, pulls in torch) -- if missing, the tool returns a clean "cellpose package is not available" error (verified live in this environment, no crash) rather than a traceback; the first real call also downloads and caches model weights (small for cellpose 3.x, ~1 GB for the unified 4.x CPSAM model), so expect a slow first run.

Public Cell Painting screen data (Image Data Resource)

For image-based phenotypic screening questions ("what Cell Painting screens exist for compound X", "how many plates/wells in screen Y"), use the CellPainting_* tools against the Image Data Resource (IDR) rather than assuming a screen exists:

• CellPainting_search_screens(query=...) -- list/filter available screens. Verified live: an empty query returns only 26 screens, and none are named literally "JUMP" despite the tool's own description citing JUMP-CP as an example dataset -- do not assume a screen exists by name; always list first ({}) and grep the real screen-name list, or try substrings like a PI name (e.g. "wawer", "dahlin") instead of a project acronym.
• CellPainting_get_screen_plates(screen_id=...) -- plates in a screen (get screen_id from the search step, e.g. idr0016-wawer-bioactivecompoundprofiling/screenA).
• CellPainting_get_well_data(plate_id=..., limit=...) -- well-level metadata and image links for a plate (get plate_id from the plates step).

Public imaging study/dataset discovery (BioImage Archive)

For "what imaging datasets exist for X" or "find a study I can reuse/benchmark against" (distinct from CellPainting_* above, which is specifically phenotypic-screening plate data) — the BioImage Archive (EBI BioStudies) is a general repository of bioimaging study metadata across modalities (fluorescence, cryo-EM, confocal, brightfield):

• BioImageArchive_search_studies(query=..., page_size=..., page=...) — general study search across the whole archive by modality/organism/ technique/topic. Verified live: query is a broad free-text match (e.g. "fluorescence microscopy cell" returned 775,219 total hits across literature-linked S-EPMC* and directly-submitted S-BIAD* accessions) — narrow with specific technique/organism terms rather than single words.
• BioImageArchive_search_bioimages(query=..., page_size=...) — same archive, scoped to the BioImages-specific collection (returns S-BIAD*-style submissions with microscopy-specific metadata, generally more useful than the broader search above for "find a reusable imaging dataset" questions).
• BioImageArchive_get_study(accession=...) — full study metadata (title/description/organism/imaging_method) for one accession (S-BIAD#### / S-BSST#### / S-EPMC####### format) found via either search tool above.
• BioImageArchive_list_study_files(accession=..., limit=..., offset=...) — the actual per-file manifest for a study: filename, relative download path, size, and any per-image experimental annotations the submitters attached (e.g. staining, diagnosis, magnification, signal/noise class) — use this to see what's actually in a study before deciding whether it's the right reference/benchmark dataset. Page with limit/offset; metadata.records_total gives the full file count (can be in the hundreds).

Fluorescent protein reference (FPbase)

For experiment-design questions about which fluorophore to use:

• FPbase_get_protein(slug=...) -- full spectral/biophysical data (excitation/ emission max, extinction coefficient, quantum yield, brightness, maturation time, PDB/UniProt IDs) for a named protein by its lowercase slug (e.g. "egfp", "mcherry", "tdtomato").
• FPbase_search_by_spectrum(agg_exc_max__gte/__lte, agg_em_max__gte/__lte, name__icontains) -- filter FPbase's 1000+ proteins by excitation/emission wavelength range, e.g. to find green-emitting options (agg_em_max__gte=490, agg_em_max__lte=530) for a multiplexed panel or FRET pair design. All filters are optional and combine as AND.

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.

Evidence Grading

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