Package: bluster 1.17.0
Aaron Lun
bluster: Clustering Algorithms for Bioconductor
Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results.
Authors:
bluster_1.17.0.tar.gz
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bluster.pdf |bluster.html✨
bluster/json (API)
NEWS
| # Install 'bluster' in R: |
| install.packages('bluster', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
On BioConductor:bluster-1.17.0(bioc 3.21)bluster-1.16.0(bioc 3.20)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
immunooncologysoftwaregeneexpressiontranscriptomicssinglecellclusteringcpp
Last updated 2 months agofrom:510e7a15b0. Checks:OK: 7 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 10 2024 |
| R-4.5-win-x86_64 | NOTE | Dec 10 2024 |
| R-4.5-linux-x86_64 | NOTE | Dec 10 2024 |
| R-4.4-win-x86_64 | OK | Dec 10 2024 |
| R-4.4-mac-x86_64 | OK | Dec 10 2024 |
| R-4.4-mac-aarch64 | OK | Dec 10 2024 |
| R-4.3-win-x86_64 | OK | Dec 10 2024 |
| R-4.3-mac-x86_64 | OK | Dec 10 2024 |
| R-4.3-mac-aarch64 | OK | Dec 10 2024 |
Exports:.defaultScalarArguments.extractScalarArguments.showScalarArgumentsAffinityParamAgnesParamapproxSilhouettebootstrapStabilitycenterscenters<-ClaraParamclusterRMSDclusterRowsclusterSweepcompareClusteringsDbscanParamDianaParamDmmParamHclustParamKmeansParamKNNGraphParamlinkClusterslinkClustersMatrixmakeKNNGraphmakeSNNGraphMbkmeansParammergeCommunitiesneighborPurityneighborsToKNNGraphneighborsToSNNGraphnestedClustersNNGraphParampairwiseModularitypairwiseRandPamParamshowSNNGraphParamSomParamTwoStepParamupdateObject
Dependencies:assortheadBHBiocGenericsBiocNeighborsBiocParallelcliclustercodetoolscpp11formatRfutile.loggerfutile.optionsgenericsglueigraphlambda.rlatticelifecyclemagrittrMatrixpkgconfigRcpprlangS4Vectorssnowvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Define the default arguments | .defaultScalarArguments .defaultScalarArguments,BlusterParam-method .extractScalarArguments .showScalarArguments |
| Affinity propogation | AffinityParam AffinityParam-class clusterRows,ANY,AffinityParam-method show,AffinityParam-method |
| Agglomerative nesting | .defaultScalarArguments,AgnesParam-method AgnesParam AgnesParam-class clusterRows,ANY,AgnesParam-method show,AgnesParam-method |
| Approximate silhouette width | approxSilhouette |
| The BlusterParam class | BlusterParam-class show,BlusterParam-method [[,BlusterParam-method [[<-,BlusterParam-method |
| Assess cluster stability by bootstrapping | bootstrapStability |
| Clustering Large Applications | .defaultScalarArguments,ClaraParam-method ClaraParam ClaraParam-class clusterRows,ANY,ClaraParam-method show,ClaraParam-method |
| Compute the RMSD per cluster | clusterRMSD |
| Cluster rows of a matrix | clusterRows |
| Clustering parameter sweeps | clusterSweep |
| Compare pairs of clusterings | compareClusterings |
| Density-based clustering with DBSCAN | clusterRows,ANY,DbscanParam-method DbscanParam DbscanParam-class show,DbscanParam-method |
| Divisive analysis clustering | .defaultScalarArguments,DianaParam-method clusterRows,ANY,DianaParam-method DianaParam DianaParam-class show,DianaParam-method |
| Dirichlet multinomial mixture clustering | clusterRows,ANY,DmmParam-method DmmParam DmmParam-class show,DmmParam-method |
| The FixedNumberParam class | centers centers,FixedNumberParam-method centers<- centers<-,FixedNumberParam-method FixedNumberParam-class show,FixedNumberParam-method |
| Hierarchical clustering | .defaultScalarArguments,HclustParam-method clusterRows,ANY,HclustParam-method HclustParam HclustParam-class show,HclustParam-method updateObject,HclustParam-method [[,HclustParam-method |
| The HierarchicalParam class | .defaultScalarArguments,HierarchicalParam-method HierarchicalParam-class show,HierarchicalParam-method |
| K-means clustering | clusterRows,ANY,KmeansParam-method KmeansParam KmeansParam-class show,KmeansParam-method updateObject,KmeansParam-method |
| Create a graph between different clusterings | linkClusters linkClustersMatrix |
| Build a nearest-neighbor graph | makeKNNGraph makeSNNGraph neighborsToKNNGraph neighborsToSNNGraph |
| Mini-batch k-means clustering | clusterRows,ANY,MbkmeansParam-method MbkmeansParam MbkmeansParam-class show,MbkmeansParam-method |
| Merge communities from graph-based clustering | mergeCommunities |
| Compute neighborhood purity | neighborPurity |
| Map nested clusterings | nestedClusters |
| Graph-based clustering | clusterRows,ANY,KNNGraphParam-method clusterRows,ANY,SNNGraphParam-method KNNGraphParam KNNGraphParam-class NNGraphParam NNGraphParam-class show,NNGraphParam-method SNNGraphParam SNNGraphParam-class |
| Compute pairwise modularity | pairwiseModularity |
| Compute pairwise Rand indices | pairwiseRand |
| Partitioning around medoids | .defaultScalarArguments,PamParam-method clusterRows,ANY,PamParam-method PamParam PamParam-class show,PamParam-method |
| Clustering with self-organizing maps | clusterRows,ANY,SomParam-method show,SomParam-method SomParam SomParam-class |
| Two step clustering with vector quantization | clusterRows,ANY,TwoStepParam-method show,TwoStepParam-method TwoStepParam TwoStepParam-class |
