STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of GeneNMF and moderndive — release velocity, themes, recent moves, and the top alternatives to consider.
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
GeneNMF applies non-negative matrix factorization to single-cell expression data to find gene programs, then consolidates programs recurring across samples into meta-programs. Version 0.6.0 replaced the consolidation method: instead of reducing each program to a gene set and taking a consensus, it retains full gene weight vectors and compares them by cosine similarity. Later releases have built reporting and control around that core — a metaprogram composition matrix showing which samples contributed, custom signature databases for enrichment testing, and the ability to drop meta-programs from results.
The ModernDive teaching package learns to render inside the browser that runs its own textbook
moderndive supplies the datasets, regression helpers and ggplot geoms used by the ModernDive introductory statistics textbook. Its release history is mostly dataset accumulation — much of it contributed by students in batches — punctuated by occasional function work. The latest release is different: it fixes View() so it renders inside webR, the in-browser R that powers the book's live exercises, and reworks the regression helpers to survive in-formula transformations.
GeneNMF applies non-negative matrix factorization to single-cell expression data to find gene programs, then consolidates programs recurring across samples into meta-programs. Version 0.6.0 replaced the consolidation method: instead of reducing each program to a gene set and taking a consensus, it retains full gene weight vectors and compares them by cosine similarity. Later releases have built reporting and control around that core — a metaprogram composition matrix showing which samples contributed, custom signature databases for enrichment testing, and the ability to drop meta-programs from results.
The package is moving from producing meta-programs to letting users interrogate and constrain how they were formed. Composition matrices, the drop function and downsampled similarity heatmaps all serve inspection rather than derivation. The parameters added alongside the 0.6.0 rewrite — specificity weighting, cumulative weight thresholds, confidence defined as the fraction of programs containing a gene — turn what were fixed internal choices into stated, tunable ones.
Recent releases have been fixes and compatibility work rather than method changes, so the core approach appears settled. The dependency on an RcppML version not on CRAN is the loose end most likely to force the next release.
moderndive supplies the datasets, regression helpers and ggplot geoms used by the ModernDive introductory statistics textbook. Its release history is mostly dataset accumulation — much of it contributed by students in batches — punctuated by occasional function work. The latest release is different: it fixes View() so it renders inside webR, the in-browser R that powers the book's live exercises, and reworks the regression helpers to survive in-formula transformations.
The package is following the textbook's second edition into the browser. webR has no pandoc, so the DT htmlwidget path the package relied on cannot produce the self-contained HTML the notebook cell needs, and the auto-print path was gated behind interactive() being false — meaning students working through the live exercises saw an explanatory message where a table should have been. Building a static HTML table and pushing it through webR's viewer hook is a small change with a direct effect on whether the book's interactive mode works at all.
With the book's v2 datasets landed and the browser rendering path fixed, the remaining friction is most likely in other functions that assume a desktop R session.
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 GeneNMF or moderndive.
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
See all GeneNMF alternatives → · See all moderndive alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. moderndive is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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. moderndive is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top GeneNMF alternatives in Analytics are ranked by recent ship velocity. Browse the "GeneNMF alternatives" section above for the current picks, or visit /alternatives/genenmf for the full list with editorial commentary on each.
Top moderndive alternatives in Analytics are ranked by recent ship velocity. Browse the "moderndive alternatives" section above for the current picks, or visit /alternatives/moderndive for the full list with editorial commentary on each.