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 simmer.plot — 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 plotting companion to simmer, shipping only when the simulator or a graphics dependency moves.
simmer.plot renders discrete-event simulation output — S3 plot() methods over get_mon_arrivals(), get_mon_attributes() and get_mon_resources(), plus trajectory diagrams drawn through DiagrammeR. Since 0.1.12 the methods attach to the monitoring data itself rather than the simulation environment, and 0.1.18 finished that migration by deleting the deprecated environment-level methods.
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.
simmer.plot renders discrete-event simulation output — S3 plot() methods over get_mon_arrivals(), get_mon_attributes() and get_mon_resources(), plus trajectory diagrams drawn through DiagrammeR. Since 0.1.12 the methods attach to the monitoring data itself rather than the simulation environment, and 0.1.18 finished that migration by deleting the deprecated environment-level methods.
This package moves when something it depends on moves. Its history is a sequence of parser fixes for new simmer trajectory formats, DiagrammeR and tidyr and dplyr version bumps, and ggplot2 workarounds. The one clear internal decision — plotting monitor output instead of the environment — was made in 2017 and completed six years later. The 2025 release fixes documentation cross-references and nothing else.
The next release most likely follows a simmer trajectory-format change or a CRAN documentation policy, matching every recent entry. There is no visible feature work in the pipeline.
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 simmer.plot.
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 simmer.plot alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. GeneNMF and simmer.plot 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. GeneNMF and simmer.plot 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 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 simmer.plot alternatives in Analytics are ranked by recent ship velocity. Browse the "simmer.plot alternatives" section above for the current picks, or visit /alternatives/simmer-plot for the full list with editorial commentary on each.