Runtime and assay verification

CellProfiler 4.2.8

The pipeline targets the full CellProfiler 4.2.8, not merely cellprofiler-core: IdentifyPrimaryObjects and measurement modules live in the full package. The official image cellprofiler/cellprofiler:4.2.8 is a Linux amd64 environment. Its digest, rechecked against the official registry on 2026-09-30, is sha256:fec440caa2b44edf80f9bd440a3d8c6b214d72437332ad2586a32d79e2eae2a4. Apple Silicon runs that image with --platform linux/amd64 under emulation. The image is about 1.54 GB compressed; allow space for its expanded layers before pulling.

The application download and manual still advertise 4.2.8, while PyPI offers 4.2.8.1 (2026-03-09). Released wheels were inspected without installing their native stack. All seven pipeline modules, plus Threshold, are byte-identical to the tagged 4.2.8 source. The CLI source diff adds Wayland display detection; -c -r -p, --data-file, -o, and --done-file are unchanged. This source comparison is not a 4.2.8.1 engine execution test.

For containers, run the helper inside a mounted working directory or mount input images, manifest, pipeline, and outputs at the same absolute paths as the host. LoadData CSV file URLs must resolve inside the container. A relative path inside a manifest is resolved before export; a host file URL does not automatically become /work/... when mounted there.

Example container pattern, from a working directory containing images.csv, images/, and a copy of this skill directory called cellprofiler-skill/:

docker run --rm --platform linux/amd64 --entrypoint python \
  -v "$PWD:/work" -w /work cellprofiler/cellprofiler:4.2.8 \
  cellprofiler-skill/scripts/nuclei_assay.py run images.csv results

The image supplies its own numpy and tifffile versions. Do not upgrade those in place to the helper test versions. Native helper-only verification uses Python 3.12, numpy==2.5.3, and tifffile==2026.9.20 in a separate environment. Full native CellProfiler has additional Java, GUI/build, and database client dependencies; follow the official installation instructions. The current 4.2.8.1 PyPI metadata still pins mysqlclient==1.4.6, scipy==1.9.0, scikit-image==0.18.3, scikit-learn<1, and tifffile<2022.4.22. A previous development installation stopped on missing mysql_config; a fresh native installation was not attempted in this review. The container command above is an execution pattern requiring an available Docker engine and image; it was not rerun during this review.

Input and output contracts

The released LoadData implementation accepts URL_DNA with file: URLs and Metadata_Sample/Plate/Well/Site. --data-file overrides its configured CSV. It infers metadata types across each column; quote marks in CSV do not force strings. Prefix sample and plate identifiers with text when leading zeros matter. Use canonical site numbers and well names such as A01 to keep acquisition identities unambiguous.

The starter sets LoadData Rescale intensities? to No, which ignores camera-range metadata and divides unsigned image pixels by their integer storage maximum. It does not preserve raw integer intensity units in measurements. The TIFF helper rejects additional pages/series and pyramids so a reader cannot silently choose the first image or level.

ExportToSpreadsheet's single-object CSV uses ImageNumber and ObjectNumber as keys; object numbers run from 1 to Count_Nuclei within each image. The helper rejects duplicate keys, orphan rows, missing object indices, fractional counts, nonfinite mean/integrated intensities, and nonpositive/nonfinite areas. An image with zero nuclei remains present, with null means and an explicit warning. Shape area is in square pixels, not square micrometres.

In the released CLI, --done-file records the pipeline's Exit_Status. Without that switch, some non-complete statuses can still produce exit code zero. The helper therefore requests the marker and requires Complete, then separately checks ModuleError_* and table contents. status: complete means these execution/consistency checks passed; it does not assert that segmentation is biologically accurate. The log and assay_qc.json remain available after a process or output-validation failure. If the helper itself is killed, a running report is incomplete evidence and must not be treated as success.

Scientific checks

• Establish a manually counted set of fields across the acquisition range. Assess precision, missed objects, splits, and merges, not only mean count correlation.
• Compare nuclear area distributions with plausible biology. Inflated area may indicate merges or illumination background; many tiny objects may be debris or threshold noise.
• Check boundary policy. This starter excludes objects touching an image edge; overlapping acquisition tiles need a deliberate deduplication/edge policy.
• Flat-field correction uses a separately estimated illumination function. Do not independently normalize each image to its own minimum/maximum before intensity comparisons.
• Review out-of-focus and saturated fields. A >1% saturation warning is a triage heuristic, not a universal acceptance limit; set acceptance criteria for the assay.
• Preserve field identities and experimental replicates when aggregating cells to wells.

Verification scope

The original development record reports that the asset was serialized by CellProfiler 4.2.8 module APIs and executed headlessly on a synthetic uint16 TIFF with three separated disks (radius 10 pixels; intensities 20,000, 30,000, 40,000). The regression asserts three nuclei, mean normalized intensity near 30,000/65,535, and a saved outline PNG. That engine run was not repeated in the 2026-09-30 review. Current tests execute manifest/TIFF handling and CSV QC with synthetic data, and simulate the subprocess contract to test completion and failure reporting. These tests do not execute CellProfiler segmentation.

To repeat the real integration test in a repository checkout, set CELLPROFILER_TEST_EXECUTABLE to an installed cellprofiler executable or a launcher that forwards all arguments and mounts the paths unchanged. Run only tests/cellprofiler/ in that pytest process. The test is explicitly skipped without that executable; helper tests still run.

Review sources

• Official application release, 4.2.8.1 PyPI metadata, and official container tags.
• Tagged CLI source: command flags and completion semantics; compared with the current PyPI wheel.
• Tagged LoadData source: URL input, type inference, and storage-range rescaling.
• File-processing manual: CSV and single-object exports, metadata, and 2D LoadData restriction.
• Measurement manual and object-processing manual: mean versus integrated intensity, pixel area, and nuclear identification assumptions.
• tifffile documentation: TIFF pages, series, levels, TiffFile, and array reading.