STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of ecotraj and GeneNMF — release velocity, themes, recent moves, and the top alternatives to consider.
Ecological trajectory analysis builds out its cyclical branch, largely through one contributor
ecotraj analyses ecological community trajectories through multivariate space, and since 1.0.0 has carried cyclical ecological trajectory analysis (CETA) alongside the linear methods. Recent releases add convergence plotting, cycle shift arrows, correspondence and reduced major axis functions, and now trajectory averaging — most credited to a single contributor, N. Djeghri. Several older release notes are bare pointers to NEWS rather than descriptions.
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.
ecotraj analyses ecological community trajectories through multivariate space, and since 1.0.0 has carried cyclical ecological trajectory analysis (CETA) alongside the linear methods. Recent releases add convergence plotting, cycle shift arrows, correspondence and reduced major axis functions, and now trajectory averaging — most credited to a single contributor, N. Djeghri. Several older release notes are bare pointers to NEWS rather than descriptions.
The centre of gravity has shifted to cycles. The 1.0.0 release introduced CETA and reworked the underlying data structures for it, and every release since extends the cyclical branch or teaches an existing function to handle cycle objects — trajectoryDistances now compares cycles using dates for time comparison, and averageTrajectories covers both trajectories and cycles. A dependency on the MannKendall package was dropped in favour of base cor.test, trimming the install footprint.
The pattern of teaching existing linear-trajectory functions to accept cycle objects has repeated across several releases, so further functions gaining cycle support is the most grounded expectation.
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.
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 ecotraj or GeneNMF.
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 Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
See all ecotraj alternatives → · See all GeneNMF alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. ecotraj and GeneNMF 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. ecotraj and GeneNMF 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 ecotraj alternatives in Analytics are ranked by recent ship velocity. Browse the "ecotraj alternatives" section above for the current picks, or visit /alternatives/ecotraj for the full list with editorial commentary on each.
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.