Package: nnSVG 1.9.0
Lukas M. Weber
nnSVG: Scalable identification of spatially variable genes in spatially-resolved transcriptomics data
Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.
Authors:
nnSVG_1.9.0.tar.gz
nnSVG_1.9.0.zip(r-4.5)nnSVG_1.9.0.zip(r-4.4)nnSVG_1.9.0.zip(r-4.3)
nnSVG_1.9.0.tgz(r-4.4-any)nnSVG_1.9.0.tgz(r-4.3-any)
nnSVG_1.9.0.tar.gz(r-4.5-noble)nnSVG_1.9.0.tar.gz(r-4.4-noble)
nnSVG_1.9.0.tgz(r-4.4-emscripten)nnSVG_1.9.0.tgz(r-4.3-emscripten)
nnSVG.pdf |nnSVG.html✨
nnSVG/json (API)
NEWS
| # Install 'nnSVG' in R: |
| install.packages('nnSVG', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/lmweber/nnsvg/issues
On BioConductor:nnSVG-1.9.0(bioc 3.20)nnSVG-1.8.0(bioc 3.19)
Last updated 2 months agofrom:c6a2917014
Exports:filter_genesnnSVG
Dependencies:abindaskpassBHBiobaseBiocFileCacheBiocGenericsBiocParallelbitbit64blobBRISCcachemclicodetoolscpp11crayoncurlDBIdbplyrDelayedArraydplyrfansifastmapfilelockformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesgluehttrIRangesjsonlitelambda.rlatticelifecyclemagickmagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslpbapplypillarpkgconfigplogrpurrrR6RANNRcppRcppArmadillordistrjsonrlangRSQLiteS4ArraysS4VectorsSingleCellExperimentsnowSparseArraySpatialExperimentstringistringrSummarizedExperimentsystibbletidyrtidyselectUCSC.utilsutf8vctrswithrXVectorzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Preprocessing function to filter genes | filter_genes |
| nnSVG | nnSVG |
