sccore
Shared plumbing for the Kharchenko single-cell stack, updated once a year
A side-by-side editorial comparison of dissmapr and UCell — release velocity, themes, recent moves, and the top alternatives to consider.
dissmapr spent its first releases becoming citable rather than adding methods.
dissmapr provides an R workflow for compositional dissimilarity and turnover — occurrence data through spatial gridding and environmental linkage to order-wise dissimilarity and bioregional mapping. All three releases to date are infrastructure: a first citable archive in June 2026, then a maturity release aligning the package with the B-Cubed software development guide. The ten-function pipeline described in the notes has not changed across them.
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.
dissmapr provides an R workflow for compositional dissimilarity and turnover — occurrence data through spatial gridding and environmental linkage to order-wise dissimilarity and bioregional mapping. All three releases to date are infrastructure: a first citable archive in June 2026, then a maturity release aligning the package with the B-Cubed software development guide. The ten-function pipeline described in the notes has not changed across them.
The work is compliance-shaped rather than method-shaped: explicit @importFrom in place of whole-namespace imports, library() calls removed from package code, roughly 11 MB of development caches dropped, a runnable README quick-start, and Zenodo archival with CITATION.cff and codemeta.json. dissmapr moves in lockstep with its B-Cubed sibling invasimapr — both tagged 0.1.0 within three minutes of each other and 0.2.0 on the same day — so releases here reflect project-wide standards deadlines more than package-specific work. The stated roadmap of additional ecological distance metrics has not yet landed.
With standards work now signed off and R CMD check clean, the next release is the first real chance for the roadmap items — additional ecological distance metrics — to arrive.
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 dissmapr or UCell.
Shared plumbing for the Kharchenko single-cell stack, updated once a year
The R client for DataONE ships slow, correctness-focused maintenance
A Shiny text-mining GUI grows into a full NLP workbench at 1.0.0
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
A diagnostic package that generalized past its own name, then learned to say which kind of separation it found
See all dissmapr alternatives → · See all UCell alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. dissmapr 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. dissmapr 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 dissmapr alternatives in Analytics are ranked by recent ship velocity. Browse the "dissmapr alternatives" section above for the current picks, or visit /alternatives/dissmapr 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.