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Add NULISA (Alamar NULISAseq) support - #59
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Readers - NPQ long format CSV and NPQ workbook (xlsx), detected automatically; platform "nulisa", values NPQ (log2) - UniProt IDs, protein names and plate QC (with thresholds) come from the instrument XML named in PlateID when it sits next to the export; the CLI downloads XMLs from PRIDE as auxiliary files; an XML on its own gives a clear error (raw read counts, not the NPQ export) - internal control (mCherry, no NPQ) is not a feature; NC / SC / IPC are control sample types; Disease, Lab, sample matrix kept as metadata QC - LOD: NULISA target LOD only (no Olink NCLOD / FixedLOD fallback, no LOD comparison); unit NPQ in labels and the QC/LOD legend - technical CV from sample controls (SC), on 2^NPQ - new "NULISA Sample & Run QC" plot: reads, IC reads, IC median and detectability per sample by type, vendor thresholds, WARN in red; run QC rows (IC CV, IPC CV, IPC target CV, reads) in the summary - Olink-only IQR/median QC not run - summary QC Status counts study samples only Example run (Inflammation Panel 250, 96 samples): 249 targets with UniProt, 242 above LOD, technical CV 5.9%, run QC all pass, groups from Disease.
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📝 Walkthrough
Merge Risk: 🟡 Moderate · up to
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage |  |
Docstring coverage is 77.50% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 40 functions across 11 files. (1 skipped:… | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main change: adding support for the NULISA (Alamar NULISAseq) platform. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request.
Full details: Docstring Coverage
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Explanation
Docstring coverage is 77.50% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 40 functions across 11 files. (1 skipped: 1 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
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🟡 Changes recommended
Reader defects can crash reports, discard measurements, misalign matrices and attach unrelated run QC.
5 open issues
What changed in this PR
Adds NULISAseq as a third platform in pyprideap’s data-reading and QC reporting workflows.
Changes:
- Adds NPQ CSV/workbook readers with optional XML annotation.
- Integrates NULISA units, controls, LOD handling and vendor QC reporting.
- Extends CLI support, documentation and synthetic tests.
| File | Description |
|---|---|
| tests/test_nulisa.py | Tests readers, annotation, QC and reports. |
| src/pyprideap/viz/qc/report.py | Integrates NULISA plots and run summaries. |
| src/pyprideap/viz/qc/render.py | Renders vendor QC metrics and NPQ legends. |
| src/pyprideap/viz/qc/compute.py | Computes NULISA QC and platform-aware metrics. |
| src/pyprideap/processing/lod.py | Disables unsupported NULISA LOD fallbacks. |
| src/pyprideap/processing/filtering.py | Recognizes NULISA control types. |
| src/pyprideap/io/readers/registry.py | Detects and dispatches NULISA formats. |
| src/pyprideap/io/readers/olink_csv.py | Generalizes the shared matrix conversion helper. |
| src/pyprideap/io/readers/nulisa.py | Implements NPQ readers and XML enrichment. |
| src/pyprideap/core.py | Adds the platform and value-unit helper. |
| src/pyprideap/cli.py | Adds platform selection and auxiliary XML downloads. |
| README.md | Documents supported formats and QC behavior. |
Review effort: Balanced
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| if named: | ||
| return named | ||
| return [p for p in sorted(path.parent.glob("*.xml")) if is_nulisa_xml(p)] |
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Fixed in 0f56bf9: when PlateID names the XML files, only those are used; another run's XML is never substituted. Test: test_named_plate_never_takes_another_runs_xml.
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| sample_key = "SampleName" | ||
| sample_cols = [sample_key, *[c for c in (*_SAMPLE_META_COLUMNS, *_SAMPLE_QC_COLUMNS) if c in df.columns]] | ||
| if "AUTO_WELLPOSITION" in df.columns: | ||
| sample_cols.append("AUTO_WELLPOSITION") | ||
| samples = _plain(df[sample_cols].drop_duplicates(subset=[sample_key]).reset_index(drop=True)) | ||
| samples = samples.rename( | ||
| columns={**_SAMPLE_QC_COLUMNS, "AUTO_WELLPOSITION": "WellID", "SAMPLE_MATRIX": "SampleMatrix"} | ||
| ) | ||
| samples.insert(0, "SampleID", samples[sample_key].astype(str)) |
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Fixed in 0f56bf9: the reader uses _sample_run_key, so a sample on several plates keeps one row per run (SampleRun), with SampleID = SampleName. Test: test_sample_on_two_plates_keeps_both_runs.
| metadata["lod_matrix"] = lod.reindex(columns=expression.columns) | ||
| if "UnnormalizedCount" in df.columns: | ||
| counts = _long_to_wide(df, sample_key, sample_order, "UnnormalizedCount", assay_col="AlamarTargetID") | ||
| metadata["read_count_matrix"] = counts.reindex(columns=expression.columns) |
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Fixed in 0f56bf9 by registering read_count_matrix in _PER_SAMPLE_METADATA. I kept the separate key instead of count_matrix, because Olink's count-based LOD and normalisation code reads count_matrix, and NULISA counts shouldn't enter that logic. Test: test_filtering_keeps_read_counts_aligned.
| features = features.set_index("AlamarTargetID").reindex(expression.columns).reset_index() | ||
| features = features.rename(columns={"index": "AlamarTargetID"}) | ||
| plate_ids = sorted(df["PlateID"].dropna().astype(str).unique()) if "PlateID" in df.columns else [] | ||
| features = _annotate(features, metadata, _find_xml(path, plate_ids)) |
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Fixed in 0f56bf9: a shared _rekey helper keys the expression, LOD and count matrices by the annotated AlamarTargetID, for both the CSV and workbook readers. Test: test_csv_without_target_ids_is_rekeyed_after_annotation.
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Actionable comments posted: 6
- 🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @src/pyprideap/cli.py:
- Around line 114-118: Scope auxiliary XML downloads to the intended NULISA
annotations rather than every non-checksum XML file, and update the auxiliary
download loop to catch urlretrieve failures, log a warning, and continue
processing the remaining files without aborting the report run. Use the existing
auxiliary URL symbols and download function around the referenced code.
Review comments at @src/pyprideap/io/readers/nulisa.py:
- Around line 238-241: The workbook reader should validate that the required
targetName column exists before calling set_index on the NPQ and
Annotation-Targets sheets. Update the relevant parsing flow to raise a
ValueError naming targetName when it is missing, matching the behavior of
read_nulisa_csv, while preserving normal parsing for valid workbooks.
- Around line 159-162: Update the XML parser’s empty-result return to construct
the DataFrame with the expected AlamarTargetID, Target, UniProt, and ProteinName
columns, so _annotate can safely process files without usable target IDs while
preserving the existing plate_qc_rows return.
Review comments at @src/pyprideap/io/readers/registry.py:
- Around line 64-66: Update the NULISA detection in the registry’s
format-detection function to use the Olink reader’s _sniff_delimiter result
instead of sep=None with the Python engine, importing _sniff_delimiter from
pyprideap.io.readers.olink_csv if needed. Wrap the header read in handling for
ParserError, UnicodeDecodeError, and EmptyDataError, treating failures as no
detected columns, then preserve the existing NULISA_LONG_MARKER_COLS check and
return behavior.
- Around line 115-118: Update the forced NULISA branch in read to check .xml
paths with is_nulisa_xml before dispatching; raise the established clear
NPQ-export ValueError for identified NULISA instrument XML files, while
preserving the existing XLSX and CSV dispatch behavior for all other paths.
Review comments at @src/pyprideap/viz/qc/compute.py:
- Around line 2515-2519: Update threshold parsing in the spec-handling block to
validate the parsed values before assigning thresholds: require the count to
match len(default) and require every value to be finite via the existing NumPy
utilities. Preserve the default thresholds when conversion fails, the count
differs, or values are NaN or infinite.
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📒 Files selected for processing: 12
README.mdsrc/pyprideap/cli.pysrc/pyprideap/core.pysrc/pyprideap/io/readers/nulisa.pysrc/pyprideap/io/readers/olink_csv.pysrc/pyprideap/io/readers/registry.pysrc/pyprideap/processing/filtering.pysrc/pyprideap/processing/lod.pysrc/pyprideap/viz/qc/compute.pysrc/pyprideap/viz/qc/render.pysrc/pyprideap/viz/qc/report.pytests/test_nulisa.py
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- XML lookup: when the export names its plates, only those XMLs are used; another run's XML is never substituted - missing UniProt stored as NaN, and get_proteins_above_lod checks for missing values before truthiness (pd.NA raised TypeError) - a sample run on several plates keeps one row per run (SampleRun) - read_count_matrix is subset with the samples by filter_controls/filter_qc - CSV without AlamarTargetID: expression, LOD and count matrices are rekeyed after the XML annotation, as for the workbook - XML without target IDs returns an empty annotation instead of KeyError - workbook without targetName raises a clear ValueError - format detection sniffs the delimiter and tolerates unparsable CSVs - read(platform="nulisa") on an instrument XML gives the clear message - XML thresholds are used only when complete and finite - auxiliary XML downloads that fail log a warning instead of aborting
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Adds QC for a third platform, NULISA (Alamar Biosciences NULISAseq). It was developed on an example run: the Inflammation Panel 250, 96 wells, lupus / RA / normal plasma, measured in two labs.
Readers
.csvnulisa_csv.xlsx(NPQ,Annotation-Targets,Annotation-Samples)nulisa_xlsx.xml(<NULISAseq>)PlateIDcolumn, holdsUniProtID, protein names and the plate-QC metrics with thresholds, so the reader takes them from it when it sits next to the data.report -adownloads XMLs as auxiliary files, and protein counts on PRIDE work (242 of 249 above LOD in the example).QC
Diseasecolumn (MPI within groups, colour-by on PCA).Example run
Tests
tests/test_nulisa.py(6 tests on a synthetic export with CSV + XML + workbook) covers:AlamarTargetIDand via gene symbol;Not included
Summary by CodeRabbit
New Features
Bug Fixes
Documentation