NEWS

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scProkaR 0.99.2

Maintenance

• Add the maintainer's verified ORCID to the package author metadata.
• Replace blanket warning suppression with explicit handling of numeric labels, undefined correlations and unreachable graph paths. Preserve meaningful warnings from model fitting and Harmony backends.
• Extract workflow and clustering stages into internal helpers, retaining the public interfaces and processing metadata.
• Wrap vignette prose and align continuation lines consistently while preserving the rendered Markdown structure.

scProkaR 0.99.1

Fixes

• Resolve the legacy Harmony matrix function only when exported, avoiding missing-export warnings with current Harmony versions.
• Use scrapper::modelGeneVariances() for optional feature selection, ranking genes by residual variance. The updated trend fitter can change the selected features relative to the previous scran implementation.
• Apply the existing MNN cosine normalization explicitly before fastMNN(), avoiding its deprecated normalization dependency while preserving the normalization, feature subset and caller-supplied backend settings.
• Compute per-cell QC directly with the existing sparse-aware sums, avoiding the newly deprecated scuttle::perCellQCMetrics() call while preserving the metrics for single-cell and multi-cell objects.
• Use exact PCA for tiny matrices or large requested fractions of the singular values, avoiding the current irlba small-matrix error and high-rank warnings. Retain sparse iterative PCA for low-rank large data.
• Use R's default package citation until an associated publication is available, removing the custom citation without a publication DOI.

scProkaR 0.99.0

New

• RegisterExistingIntegrationEmbeddings() registers latent spaces already stored in an object, with optional copying and automatic name detection.
• RegisterIntegrationEmbedding() protects existing reductions from changes unless overwrite = TRUE is supplied.
• BenchmarkIntegration() discovers unregistered integrated_* reductions and accepts method and reduction names without regard to case.
• First submission to Bioconductor.
• CreateBacObject() builds a SingleCellExperiment from a count matrix, a 10x Genomics directory, or a Seurat object, and MergeBacObjects() combines several such objects while keeping cell barcodes unique.
• RunBacQC() and FilterBacCells() compute and apply prokaryote-specific quality-control metrics, including ribosomal RNA fraction.
• IntegrateBacData(), RegisterIntegrationEmbedding(), RunIntegratedUMAP() and RunIntegratedClustering() provide batch integration and downstream embedding, with BenchmarkIntegration() scoring the corrected embeddings using scIB-inspired metrics.
• aggregate_pseudobulk(), normalize_pseudobulk() and the run_edger_*() functions provide pseudobulk differential expression built on edgeR, covering both pairwise contrasts and spline-based time courses.
• run_tata() implements Time Aware Trajectory Analysis (TATA), a time-informed trajectory abstraction for perturbation time courses, together with branch probability, gene trend, plotting and benchmarking helpers.
• The packaged sce1 dataset provides a small bacterial single-cell example used throughout the vignettes.