STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
Comparison · Analytics
GeneNMF vs robma
A side-by-side editorial comparison of GeneNMF and robma — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
GeneNMF vs robma: at a glance
What is GeneNMF?
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.
What is robma?
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
GeneNMF vs robma: editorial side-by-side
GeneNMF
0.0ANALYTICS
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
robma
0.0ANALYTICS
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
◆ Current state
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
◆ Where it's heading
The 3.x series solved the modeling problem and left an interface problem behind: a caller had to know which of six constructors matched their data type, and argument names differed across them. 4.0.0 resolves that by making the model family a set of arguments rather than a function name, and by standardizing input naming on metafor-style conventions. It shipped one day after BayesTools 0.3.0, the author's own upstream infrastructure package, whose new standardization and prior-transformation machinery this rewrite depends on.
◆ Prediction
A rewrite this wide usually needs a follow-up, so expect 4.0.x patches addressing migration gaps as users hit the removed constructors and renamed arguments.
Alternatives to GeneNMF and robma
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 robma.
ggtrace
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
kde1d
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
svines
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
gghighlight
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
rtrek
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 robma alternatives →
Recent activity from GeneNMF and robma
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agorobmaUnifies six model constructors into one brma class hierarchy ⚡
- 8mo agorobmaRoBMA 3.6.1
- 11mo agoGeneNMFSingle-sample runs fixed; gene weight definition refined
- 11mo agorobmaRoBMA 3.6.0
- 1y agorobmaRoBMA 3.5.1
- 1y agorobmaRoBMA 3.5.0
- 1y agoGeneNMFMetaprogram composition exposed and custom signature DBs supported
- 1y agorobmaRoBMA 3.4.0
- 1y agoGeneNMFSimilarity heatmap downsampling and meta-program removal
- 2y agoGeneNMFMeta-programs rebuilt on gene weight vectors and cosine similarity ⚡
- 2y agoGeneNMFFirst stable release published to CRAN
Frequently asked questions
What is the difference between GeneNMF and robma?
Both compete on the same themes — r-package — within Analytics. GeneNMF and robma 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.
Is GeneNMF better than robma?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. GeneNMF and robma 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.
What are the best alternatives to GeneNMF?
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.
What are the best alternatives to robma?
Top robma alternatives in Analytics are ranked by recent ship velocity. Browse the "robma alternatives" section above for the current picks, or visit /alternatives/robma for the full list with editorial commentary on each.