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Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of dynwrap and UCell — release velocity, themes, recent moves, and the top alternatives to consider.
A dormant trajectory-inference wrapper wakes up for maintenance only
dynwrap is the dynverse component that wraps single-cell trajectory inference methods behind a common interface, handling containerised method execution and the trajectory data model. The visible history is dominated by a burst of feature work in 2019 and then near-silence: the only recent release, v1.3.0, is a package modernisation with a minimum-version bump and no user-facing capability. The three entries in the feed span seven years.
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
UCell scores gene signatures in single-cell data using a rank-based metric that is robust to dataset composition. Its release history reads as a sequence of ecosystem accommodations: Bioconductor submission in 2.0, SmoothKNN() for k-nearest-neighbor smoothing of scores in 2.2, smoothing applied directly to expression slots in 2.4, Seurat v5 assay compatibility in 2.6, multi-layer Seurat v5 objects in 2.8, and a missing_genes parameter in 2.14 that lets callers impute or skip signature genes absent from the data. Version 2.16 tracks Bioconductor 3.23 and points at a new publication and a Python implementation, pyUCell.
dynwrap is the dynverse component that wraps single-cell trajectory inference methods behind a common interface, handling containerised method execution and the trajectory data model. The visible history is dominated by a burst of feature work in 2019 and then near-silence: the only recent release, v1.3.0, is a package modernisation with a minimum-version bump and no user-facing capability. The three entries in the feed span seven years.
The direction is custodial rather than developmental. The 2019 releases built out the substance — RNA velocity in the wrapper, velocity-oriented topologies, directed geodesic distances, Singularity 3.0 and sparse matrices throughout — and nothing since has extended it. The 2026 release reads as keeping the package installable against a modern R toolchain, which is what a maintained dependency of a benchmark suite needs rather than what an actively developed tool looks like.
On this evidence, expect further releases to be compatibility maintenance triggered by R or dependency changes; the entries give no indication of resumed feature work.
UCell scores gene signatures in single-cell data using a rank-based metric that is robust to dataset composition. Its release history reads as a sequence of ecosystem accommodations: Bioconductor submission in 2.0, SmoothKNN() for k-nearest-neighbor smoothing of scores in 2.2, smoothing applied directly to expression slots in 2.4, Seurat v5 assay compatibility in 2.6, multi-layer Seurat v5 objects in 2.8, and a missing_genes parameter in 2.14 that lets callers impute or skip signature genes absent from the data. Version 2.16 tracks Bioconductor 3.23 and points at a new publication and a Python implementation, pyUCell.
Two threads run through this. The scoring algorithm itself has barely changed — the rank-based core is stable, and 2.14's reformatting to gene indices rather than string matching is a speed change, not a method change. What does change constantly is object-format compatibility, which is the tax of living between Seurat and SingleCellExperiment. The pyUCell reference in 2.16 is the first sign of the method reaching beyond R, though these notes say nothing about its scope.
The cadence is locked to Bioconductor's twice-yearly release train, so the next version will most likely accompany Bioconductor 3.24 with whatever Seurat or SingleCellExperiment changes it brings.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either dynwrap or UCell.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
See all dynwrap alternatives → · See all UCell alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — single-cell — within Analytics. dynwrap and UCell are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dynwrap and UCell are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top dynwrap alternatives in Analytics are ranked by recent ship velocity. Browse the "dynwrap alternatives" section above for the current picks, or visit /alternatives/dynwrap for the full list with editorial commentary on each.
Top UCell alternatives in Analytics are ranked by recent ship velocity. Browse the "UCell alternatives" section above for the current picks, or visit /alternatives/ucell for the full list with editorial commentary on each.