Resonalis

J-EXPRESS / OPEN SCIENTIFIC METHODS

Scientific skills.
Open to inspection.

Modern AI brings broad reasoning capabilities. Our research-informed skills add explicit methodological procedures, quality checks, limitations and references. J-Express tools turn those procedures into reproducible computation, while the researcher contributes context, judgment and domain expertise.

The most valuable insight comes from the combination: the AI's reasoning, evidence-based workflow skills, reliable analysis tools, and — crucially — the scientist's own experience.

Matrix analysisPCA, clustering, correspondence analysis and numerical matrix explorationNormalizationModality-aware transformations, scaling and preprocessing safeguardsEmbeddingsOriginal-space neighbors, UMAP, semantic search and representation analysisBulk RNA-seqCount-aware QC, experimental design and exploratory expression analysisSingle-cell RNA-seqCell QC, feature selection, neighborhoods and clustering with donor-aware caveats

Five source-controlled skill snapshots, prepared from the J-Express repository. They are ready within a documented technical acceptance profile; publication does not constitute independent scientific peer review. These pages are for inspection and do not install skills or verify client distribution. Scientific references and implementation limits appear inside each skill.

Browse the current public documentation and source skills ↗