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 rtrek — 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.
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
rtrek bundles Star Trek datasets — book series, timelines, episode transcripts, species and homeworlds, map tile sets — and layers live retrieval on top through memory_alpha() and memory_beta() plus their ma_* and mb_* helpers. Recent releases are almost entirely repairs to that retrieval layer as the source wikis change their page structure.
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.
rtrek bundles Star Trek datasets — book series, timelines, episode transcripts, species and homeworlds, map tile sets — and layers live retrieval on top through memory_alpha() and memory_beta() plus their ma_* and mb_* helpers. Recent releases are almost entirely repairs to that retrieval layer as the source wikis change their page structure.
The package's centre of gravity shifted once, at 0.2.0, from shipping static data to querying Memory Alpha and Memory Beta at runtime. Everything since has been the maintenance bill for that decision: HTML update fixes, parser improvements, portal retrieval bugs. Note that version numbers on this feed do not track time — 0.2.5 is stamped a year before 0.1.0, and three tags were backfilled within four minutes in November 2020 — so neither rank nor version ordering here indicates release sequence.
Expect the next release to fix retrieval against another Memory Alpha layout change, which is what the last four have done. The entries give no indication of new datasets or functions in progress.
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 rtrek.
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 thin EIA energy-data client whose whole story is making bulk queries survive the API's limits.
See all GeneNMF alternatives → · See all rtrek alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. GeneNMF and rtrek 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 rtrek 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 rtrek alternatives in Analytics are ranked by recent ship velocity. Browse the "rtrek alternatives" section above for the current picks, or visit /alternatives/rtrek for the full list with editorial commentary on each.