STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
Comparison · Analytics
GeneNMF vs palettecore
A side-by-side editorial comparison of GeneNMF and palettecore — release velocity, themes, recent moves, and the top alternatives to consider.
GeneNMF vs palettecore: 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 palettecore?
Accessible palettes generated from one seed, with every audit number computed on the hex you actually get
palettecore generates sequential, diverging and categorical colour palettes from a single seed using CIEDE2000 arc-length spacing in OKLCH, and audits each one for colour-vision-deficiency separation, greyscale survival, gamut and WCAG contrast. It exists as a numpy-only Python core and an R mirror validated hex-exact against shared parity fixtures, plus a CLI the project describes as its agent-friendly entry point. Four releases landed inside 24 hours in late July.
GeneNMF vs palettecore: 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.
palettecore
6.3ANALYTICS
Accessible palettes generated from one seed, with every audit number computed on the hex you actually get
◆ Current state
palettecore generates sequential, diverging and categorical colour palettes from a single seed using CIEDE2000 arc-length spacing in OKLCH, and audits each one for colour-vision-deficiency separation, greyscale survival, gamut and WCAG contrast. It exists as a numpy-only Python core and an R mirror validated hex-exact against shared parity fixtures, plus a CLI the project describes as its agent-friendly entry point. Four releases landed inside 24 hours in late July.
◆ Where it's heading
The arc is about making the audit honest rather than making the palettes prettier. The 0.2.2 release, prompted by an external review, moved every diagnostic to compute on the 8-bit quantised hex codes actually returned rather than on internal floats — a change that flips results near thresholds and had been quietly overstating one deuteranopia score. The 0.4.0 helix kind and the vividness control extend the generator, but the same release also tightens its own claims, reframing helix as checked rather than assumed CVD-safe.
◆ Prediction
Given the pattern of auditing its own assertions, expect the next release to extend the cvd_luminance_monotonic diagnostic beyond helix to the other palette kinds.
Alternatives to GeneNMF and palettecore
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 palettecore.
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 palettecore alternatives →
Recent activity from GeneNMF and palettecore
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 24d agopalettecorepalettecore 0.4.0
- 24d agopalettecorepalettecore 0.2.2
- 25d agopalettecorepalettecore 0.2.1
- 25d agopalettecorepalettecore 0.2.0 ⚡
- 11mo agoGeneNMFSingle-sample runs fixed; gene weight definition refined
- 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
Frequently asked questions
What is the difference between GeneNMF and palettecore?
They serve adjacent needs but don't currently overlap on shipped themes. palettecore is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is GeneNMF better than palettecore?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. palettecore is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. 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 palettecore?
Top palettecore alternatives in Analytics are ranked by recent ship velocity. Browse the "palettecore alternatives" section above for the current picks, or visit /alternatives/palettecore for the full list with editorial commentary on each.