STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
Comparison · Analytics
GeneNMF vs gratia
A side-by-side editorial comparison of GeneNMF and gratia — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
GeneNMF vs gratia: 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 gratia?
The tidy front-end for GAMs, now stable enough that upstream ggplot2 sets its release calendar.
gratia wraps mgcv-fitted generalized additive models in tidy data frames and ggplot2 graphics — smooth_estimates(), fitted_values(), derivatives(), draw() and appraise() cover evaluation, prediction and diagnostics. The API reached its intended shape at 0.9.0, when every generated column was renamed to a dot-prefixed form, and 0.10.0 added conditional_values() for covariate-conditional prediction plots.
GeneNMF vs gratia: 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.
gratia
0.0ANALYTICS
The tidy front-end for GAMs, now stable enough that upstream ggplot2 sets its release calendar.
◆ Current state
gratia wraps mgcv-fitted generalized additive models in tidy data frames and ggplot2 graphics — smooth_estimates(), fitted_values(), derivatives(), draw() and appraise() cover evaluation, prediction and diagnostics. The API reached its intended shape at 0.9.0, when every generated column was renamed to a dot-prefixed form, and 0.10.0 added conditional_values() for covariate-conditional prediction plots.
◆ Where it's heading
The package has moved through a long rewrite cycle and out the other side. Successive releases replaced evaluate_smooth() with smooth_estimates(), rebuilt draw() on top of it, then renamed the entire output vocabulary to avoid colliding with user variables. That work is finished; 0.11.1 is driven almost entirely by ggplot2 4.0.0 compatibility, with new mgcv family support for quantile residuals riding along. Development now tracks upstream breakage rather than internal redesign.
◆ Prediction
Expect the next releases to continue absorbing ggplot2 4.x and mgcv changes, with incremental family coverage in quantile_residuals() as the visible new work. The entries give no signal on which mgcv families come next.
Alternatives to GeneNMF and gratia
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 gratia.
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 gratia alternatives →
Recent activity from GeneNMF and gratia
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 11mo agoGeneNMFSingle-sample runs fixed; gene weight definition refined
- 11mo agogratiaggplot2 4.0.0 compatibility, plus four more quantile-residual families
- 1y agoGeneNMFMetaprogram composition exposed and custom signature DBs supported
- 1y agogratiaconditional_values() replaces vis.gam for conditional prediction plots
- 1y agoGeneNMFSimilarity heatmap downsampling and meta-program removal
- 2y agoGeneNMFMeta-programs rebuilt on gene weight vectors and cosine similarity ⚡
- 2y agogratiaparametric_effects() joins the dot-prefix rename; LSS families begin
- 2y agogratiaEvery generated column gains a dot prefix ⚡
- 2y agoGeneNMFFirst stable release published to CRAN
- 3y agogratiaReal variable names in smooth_samples(), plus dplyr 1.1.0 fixes
- 4y agogratiaM1 example output fixes and confint tibble returns
Frequently asked questions
What is the difference between GeneNMF and gratia?
Both compete on the same themes — r-package — within Analytics. GeneNMF and gratia 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 gratia?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. GeneNMF and gratia 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 gratia?
Top gratia alternatives in Analytics are ranked by recent ship velocity. Browse the "gratia alternatives" section above for the current picks, or visit /alternatives/gratia for the full list with editorial commentary on each.