fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of sccore and UCell — release velocity, themes, recent moves, and the top alternatives to consider.
Shared plumbing for the Kharchenko single-cell stack, updated once a year
sccore is the utility layer under the Kharchenko lab's single-cell packages — embedding plots, dot plots, parallel apply helpers and distance metrics that the downstream tools depend on rather than a tool researchers drive directly. The recent releases fix the Jensen-Shannon distance computation between matrix columns and add optional OpenMP support to the RcppArmadillo build. Cadence is roughly one CRAN release a year.
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.
sccore is the utility layer under the Kharchenko lab's single-cell packages — embedding plots, dot plots, parallel apply helpers and distance metrics that the downstream tools depend on rather than a tool researchers drive directly. The recent releases fix the Jensen-Shannon distance computation between matrix columns and add optional OpenMP support to the RcppArmadillo build. Cadence is roughly one CRAN release a year.
Work splits cleanly into two streams: keeping the compiled build acceptable to CRAN as its Makevars policy shifts, and small correctness or interoperability fixes to the plotting and distance helpers. The interoperability thread is the one with direction — embeddingPlot() learning to read Seurat objects in 1.0.6 points at meeting users in the dominant single-cell framework rather than requiring the lab's own object types.
Expect the next release to be driven by a CRAN toolchain requirement or a downstream package's needs, with any user-facing change likely another interoperability or plotting fix rather than new capability.
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 sccore 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 sccore alternatives → · See all UCell alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — single-cell, r-package, interoperability — within Analytics. sccore 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. sccore 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 sccore alternatives in Analytics are ranked by recent ship velocity. Browse the "sccore alternatives" section above for the current picks, or visit /alternatives/sccore 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.