STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
Comparison · Analytics
GeneNMF vs incase
A side-by-side editorial comparison of GeneNMF and incase — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
GeneNMF vs incase: 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 incase?
A safer case_when that keeps hardening its guarantees while realigning to tidyverse naming.
incase supplies in_case(), switch_case(), grep_case() and fn_case() as vectorised recoding functions in the dplyr::case_when idiom, with _fct and _list variants that return factors or lists instead of forcing atomic type conversion. The 0.4.0 release deprecates the undotted preserve, default and ordered arguments in favour of dotted forms, starting a removal clock, and adds .exhaustive to error on unmatched inputs.
GeneNMF vs incase: 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.
incase
0.0ANALYTICS
A safer case_when that keeps hardening its guarantees while realigning to tidyverse naming.
◆ Current state
incase supplies in_case(), switch_case(), grep_case() and fn_case() as vectorised recoding functions in the dplyr::case_when idiom, with _fct and _list variants that return factors or lists instead of forcing atomic type conversion. The 0.4.0 release deprecates the undotted preserve, default and ordered arguments in favour of dotted forms, starting a removal clock, and adds .exhaustive to error on unmatched inputs.
◆ Where it's heading
The arc is consistently toward catching recoding mistakes at the call site rather than letting them pass silently. Early releases broadened how a match can be expressed — pattern matching, function application, factor and list returns. Recent work has shifted to guarantees about the result: correct factor level ordering relative to .default, and now an exhaustiveness check. Notably 0.4.0 reverses the 0.3.2 decision to accept arguments with or without dots, trading that flexibility for namespace safety against user-supplied case names.
◆ Prediction
The deprecation warnings introduced in 0.4.0 point to a follow-up release that removes the undotted arguments outright. Whether .exhaustive eventually becomes the default is unclear from these entries.
Alternatives to GeneNMF and incase
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 incase.
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 incase alternatives →
Recent activity from GeneNMF and incase
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 11mo agoGeneNMFSingle-sample runs fixed; gene weight definition refined
- 11mo agoincaseDotted arguments and an .exhaustive matching check
- 1y agoGeneNMFMetaprogram composition exposed and custom signature DBs supported
- 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
- 2y agoincaseDotted and undotted arguments both accepted
- 5y agoincaseFix NULL return when no condition matches
- 5y agoincaseFactor and list return families arrive
- 5y agoincaseDrop unused stats import to clear a check NOTE
- 5y agoincasePattern and function-based matching families added
Frequently asked questions
What is the difference between GeneNMF and incase?
Both compete on the same themes — r-package — within Analytics. GeneNMF and incase 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 incase?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. GeneNMF and incase 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 incase?
Top incase alternatives in Analytics are ranked by recent ship velocity. Browse the "incase alternatives" section above for the current picks, or visit /alternatives/incase for the full list with editorial commentary on each.