ExtremaRank v0.3.0: candidate certificates and practical omics workflows

ExtremaRank v0.3.0 extends sample-deletion robustness auditing with individual candidate certificates and practical omics workflows.

• Certify each queried feature's Top-K membership and mean-effect sign, even when the whole shortlist changes.
• Bound minimum deletion changes; label a minimum exact only after excluding every smaller deletion count.
• Use safe conditional Welch pruning with exact rational comparisons and retained enumeration reference.
• Execute limma, limma-voom, edgeR and DESeq2 refits with explicit covariates, pairing, fixed filters and preserved failures.
• Aggregate dense, 10X or optional H5AD raw counts by donor, condition and cell type, with explicit raw layers and technical replicate declarations.
• Reproduce fixed real single-cell, technical protein and biological lipid examples, plus stable and difficult search cases.

Validation: all 59 local tests passed including optional H5AD and four R adapters; 6,000 native design/direction problems and 1,434,768 independent property/enclosure checks had zero failures. Real witnesses matched 183 full-feature Fraction rankings, pseudobulk matched 4,347,470 independent R sums, and external model scores matched 300 R-exported Top-K sets. GitHub CI covers core Python 3.10/3.12/3.14 and optional H5AD; R integrations were executed locally.

A useful real result: Nutrimouse upward Top-5 is preserved by every single-mouse deletion but changes under a two-mouse witness, giving exact minimum change 2. Four of five original candidates remain certified through budget 2.

Limits remain explicit. Favorable synthetic pruning is not a worst-case speed guarantee. Native certificates concern frozen rankings, not FDR or biological truth. A frozen Kang edgeR signed-sqrt-QL-F baseline contains tiny negative raw F values and remains NOT_EVALUABLE; its raw rows and failed scenarios are preserved without clipping or feature removal. Protein measurements are technical runs; lipid measurements are percentages.

The wheel contains the tool and packaged R adapter, with no base Python dependencies or large datasets. The source archive contains pinned public raw/prepared inputs, contracts, executed outputs and a per-file manifest. SHA256SUMS verifies both downloads. Third-party data retain their original terms; see DATA_LICENSE.md.

中文说明: https://github.com/heise3/ExtremaRank/blob/v0.3.0/docs/V03_CN.md
Usage and proof: https://github.com/heise3/ExtremaRank/blob/v0.3.0/docs/ROBUSTNESS.md
Model/single-cell guide: https://github.com/heise3/ExtremaRank/blob/v0.3.0/docs/REFITS.md