STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
Comparison · Analytics
GeneNMF vs labelled
A side-by-side editorial comparison of GeneNMF and labelled — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
GeneNMF vs labelled: 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 labelled?
The bridge between Stata/SPSS labelled data and tidy R keeps widening, one integration at a time.
labelled manages variable labels, value labels and user-defined missing values on data imported from Stata, SPSS and SAS, filling the gap between those formats' metadata and R's native types. Recent releases have pushed outward from the core label accessors: survey design objects from the survey package are now supported throughout, look_for() results can be rendered as formatted gt tables, and dictionary data frames convert in both directions. Error messaging moved wholesale to cli in 2.14.0.
GeneNMF vs labelled: 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.
labelled
0.0ANALYTICS
The bridge between Stata/SPSS labelled data and tidy R keeps widening, one integration at a time.
◆ Current state
labelled manages variable labels, value labels and user-defined missing values on data imported from Stata, SPSS and SAS, filling the gap between those formats' metadata and R's native types. Recent releases have pushed outward from the core label accessors: survey design objects from the survey package are now supported throughout, look_for() results can be rendered as formatted gt tables, and dictionary data frames convert in both directions. Error messaging moved wholesale to cli in 2.14.0.
◆ Where it's heading
The arc is toward being usable wherever labelled data ends up, not just where it is loaded. Each recent release either extends support to another object type — survey designs, packed columns, plain vectors, tibbles with list columns — or adds a conversion path between labels and some other representation. The look_for() search function has become a second centre of gravity alongside the label accessors, accumulating its own output formats and long-format conversions.
◆ Prediction
The pattern of adding compatibility with one more object type or output format per release is stable and likely continues. Nothing in these entries indicates a change to the underlying haven_labelled representation the package is built on.
Alternatives to GeneNMF and labelled
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 labelled.
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 labelled alternatives →
Recent activity from GeneNMF and labelled
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 9mo agolabelledFormatted look_for() tables and two-way dictionary conversion
- 11mo agolabelledSurvey design objects supported across the package
- 11mo agoGeneNMFSingle-sample runs fixed; gene weight definition refined
- 1y agolabelledRegression in set_variable_labels() corrected
- 1y agoGeneNMFMetaprogram composition exposed and custom signature DBs supported
- 1y agolabelledcli adopted for all messaging; null_action gains options
- 1y agoGeneNMFSimilarity heatmap downsampling and meta-program removal
- 2y agoGeneNMFMeta-programs rebuilt on gene weight vectors and cosine similarity ⚡
- 2y agolabelledCustom functions can rewrite variable and value labels in bulk
- 2y agoGeneNMFFirst stable release published to CRAN
- 3y agolabelledPacked columns supported and label attributes exposed directly
Frequently asked questions
What is the difference between GeneNMF and labelled?
Both compete on the same themes — r-package — within Analytics. GeneNMF and labelled 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 labelled?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. GeneNMF and labelled 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 labelled?
Top labelled alternatives in Analytics are ranked by recent ship velocity. Browse the "labelled alternatives" section above for the current picks, or visit /alternatives/labelled for the full list with editorial commentary on each.