Package: scry 1.19.0
Kelly Street
scry: Small-Count Analysis Methods for High-Dimensional Data
Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.
Authors:
scry_1.19.0.tar.gz
scry_1.19.0.zip(r-4.5)scry_1.19.0.zip(r-4.4)scry_1.19.0.zip(r-4.3)
scry_1.19.0.tgz(r-4.4-any)scry_1.19.0.tgz(r-4.3-any)
scry_1.19.0.tar.gz(r-4.5-noble)scry_1.19.0.tar.gz(r-4.4-noble)
scry_1.19.0.tgz(r-4.4-emscripten)scry_1.19.0.tgz(r-4.3-emscripten)
scry.pdf |scry.html✨
scry/json (API)
| # Install 'scry' in R: |
| install.packages('scry', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kstreet13/scry/issues
On BioConductor:scry-1.19.0(bioc 3.21)scry-1.18.0(bioc 3.20)
dimensionreductiongeneexpressionnormalizationprincipalcomponentrnaseqsoftwaresequencingsinglecelltranscriptomics
Last updated 2 months agofrom:ae09b7d492. Checks:OK: 5 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 30 2024 |
| R-4.5-win | NOTE | Nov 30 2024 |
| R-4.5-linux | NOTE | Nov 30 2024 |
| R-4.4-win | OK | Nov 30 2024 |
| R-4.4-mac | OK | Nov 30 2024 |
| R-4.3-win | OK | Nov 30 2024 |
| R-4.3-mac | OK | Nov 30 2024 |
Exports:devianceFeatureSelectionGLMPCAnullResiduals
Dependencies:abindaskpassassortheadbeachmatBHBiobaseBiocGenericsBiocParallelBiocSingularcodetoolscpp11crayoncurlDelayedArrayformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesglmpcahttrIRangesirlbajsonlitelambda.rlatticeMASSMatrixMatrixGenericsmatrixStatsmimeopensslR6RcpprsvdS4ArraysS4VectorsScaledMatrixSingleCellExperimentsnowSparseArraySummarizedExperimentsysUCSC.utilsXVectorzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Feature selection by approximate multinomial deviance | devianceFeatureSelection devianceFeatureSelection,DelayedArray-method devianceFeatureSelection,Matrix-method devianceFeatureSelection,matrix-method devianceFeatureSelection,SummarizedExperiment-method |
| Generalized principal components analysis for non-normally distributed data | GLMPCA GLMPCA,Matrix-method GLMPCA,matrix-method GLMPCA,SummarizedExperiment-method |
| Residuals from an approximate multinomial null model | nullResiduals nullResiduals,ANY-method nullResiduals,Matrix-method nullResiduals,matrix-method nullResiduals,SingleCellExperiment-method nullResiduals,SummarizedExperiment-method |
