https://pachterlab.r-universe.devRecent package updates in pachterlabR-universehttps://github.com/pachterlab.pnghttps://pachterlab.r-universe.devTue, 19 May 2026 22:21:02 GMTjmrich@caltech.edu (Joseph Rich)Sort k-partite graphs with node order, layer order, and node grouping optimized with a heuristic to (nearly) minimize edge crossings. Useful for improving visualizations with alluvial plots by "untangling" the graphs.https://github.com/r-universe/pachterlab/actions/runs/28433943095Tue, 19 May 2026 22:21:02 GMTwompwomp0.99.0successhttps://pachterlab.r-universe.devhttps://github.com/pachterlab/wompwompplotting.Rmdplotting.htmlPlotting with ggalluvial2025-12-04 08:02:222026-03-30 20:18:51wompwomp_intro.Rmdwompwomp_intro.htmlQuick Start: An Introduction to wompwomp2025-08-25 19:08:102026-05-19 22:21:02dl3764@columbia.edu (Lambda Moses)Builds upon the existing ArtifactDB project, expending alabaster.spatial for language agnostic on disk serialization of SpatialFeatureExperiment.https://github.com/r-universe/bioc/actions/runs/26676092762Tue, 28 Apr 2026 13:04:38 GMTalabaster.sfe1.5.0successhttps://bioc.r-universe.devhttps://github.com/bioc/alabaster.sfeOverview.RmdOverview.htmlSave/load SpatialFeatureExperiment to/from file2024-11-08 05:27:322024-12-23 04:15:25kaylajac@caltech.edu (Kayla Jackson)Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs.https://github.com/r-universe/bioc/actions/runs/26679572534Tue, 28 Apr 2026 13:00:35 GMTconcordexR1.13.0successhttps://bioc.r-universe.devhttps://github.com/bioc/concordexRconcordex-nonspatial.Rmdconcordex-nonspatial.htmlUsing concordex in to assess cluster boundaries in scRNA-seq2024-07-22 20:18:272025-06-12 19:49:44overview.Rmdoverview.htmlOverview of concordexR2023-03-31 08:35:192024-07-22 20:18:27dl3764@columbia.edu (Lambda Moses)SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.https://github.com/r-universe/bioc/actions/runs/26630667509Tue, 28 Apr 2026 12:59:10 GMTVoyager1.15.0successhttps://bioc.r-universe.devhttps://github.com/bioc/Voyageroverview.Rmdoverview.htmlOverview of Voyager2022-07-07 06:52:042024-04-29 09:41:05dl3764@columbia.edu (Lambda Moses)A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.https://github.com/r-universe/bioc/actions/runs/26631024868Tue, 28 Apr 2026 12:59:09 GMTSpatialFeatureExperiment1.15.0successhttps://bioc.r-universe.devhttps://github.com/bioc/SpatialFeatureExperimentSFE.RmdSFE.htmlIntroduction to the SpatialFeatureExperiment class2022-03-30 02:56:052026-04-10 23:37:43