← Back to home
Comparison · Analytics

GeneNMF vs hydroloom

A side-by-side editorial comparison of GeneNMF and hydroloom — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

GeneNMF vs hydroloom: at a glance

FeatureGeneNMFhydroloom
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themessingle-cell-genomics, nmf, gene-programs, bioinformaticshydrology, network-analysis, geospatial, r-package
Last editorial update1h ago6h ago
WebsiteVisit →Visit →

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.

Read the full GeneNMF trajectory →

What is hydroloom?

USGS puts a type system over its river network toolkit so errors surface at dispatch

hydroloom builds and navigates hydrologic flow networks, carrying functionality migrated out of nhdplusTools. Version 1.2.0 introduces an S3 class hierarchy — hy_topo, hy_leveled, hy_node, hy_flownetwork — assigned automatically by hy() and by producer functions, letting the package validate input at dispatch time and emit guided errors. Outlet detection is now defined explicitly: a row is an outlet when its toid is not in id, with reserved values, NA and implicit absence all accepted.

Read the full hydroloom trajectory →

GeneNMF vs hydroloom: editorial side-by-side

G
GeneNMF
ANALYTICS
0.0

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.

H
hydroloom
ANALYTICS
0.0

USGS puts a type system over its river network toolkit so errors surface at dispatch

◆ Current state

hydroloom builds and navigates hydrologic flow networks, carrying functionality migrated out of nhdplusTools. Version 1.2.0 introduces an S3 class hierarchy — hy_topo, hy_leveled, hy_node, hy_flownetwork — assigned automatically by hy() and by producer functions, letting the package validate input at dispatch time and emit guided errors. Outlet detection is now defined explicitly: a row is an outlet when its toid is not in id, with reserved values, NA and implicit absence all accepted.

◆ Where it's heading

The package spent its first releases porting and broadening — non-dendritic network support, divergence routing, subsetting that follows diversions out of a basin — and has now turned to making that surface safe to use. The class hierarchy is the structural expression of that turn: instead of every function re-checking whether a data frame has the columns it needs, the type carries the guarantee. The explicit outlet rule resolves a category of failure where valid networks errored on NA or orphan toid values.

◆ Prediction

The release notes flag that subclass attributes are stripped by standard dplyr operations, which is the kind of rough edge that usually generates follow-up work — expect attribute preservation or restoration helpers next.

Alternatives to GeneNMF and hydroloom

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 hydroloom.

See all GeneNMF alternatives → · See all hydroloom alternatives →

Recent activity from GeneNMF and hydroloom

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 2mo agohydroloomhydroloom v1.2.0
  2. 5mo agohydroloomTest tolerances relaxed for CRAN Fedora checks
  3. 5mo agohydroloomNetwork subsetting and divergence-routed accumulation
  4. 10mo agohydroloomSort and indexing fixes
  5. 11mo agoGeneNMFSingle-sample runs fixed; gene weight definition refined
  6. 1y agoGeneNMFMetaprogram composition exposed and custom signature DBs supported
  7. 1y agoGeneNMFSimilarity heatmap downsampling and meta-program removal
  8. 1y agohydroloomUpmain and downmain navigation for non-dendritic networks
  9. 2y agoGeneNMFMeta-programs rebuilt on gene weight vectors and cosine similarity
  10. 2y agoGeneNMFFirst stable release published to CRAN
  11. 2y agohydroloomInitial release completing the nhdplusTools migration

Frequently asked questions

What is the difference between GeneNMF and hydroloom?

Both compete on the same themes — r-package — within Analytics. GeneNMF and hydroloom 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 hydroloom?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. GeneNMF and hydroloom 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 hydroloom?

Top hydroloom alternatives in Analytics are ranked by recent ship velocity. Browse the "hydroloom alternatives" section above for the current picks, or visit /alternatives/hydroloom for the full list with editorial commentary on each.