Package: NewWave 1.17.0

Federico Agostinis

NewWave: Negative binomial model for scRNA-seq

A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.

Authors:Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele Sales [aut], Davide Risso [aut]

NewWave_1.17.0.tar.gz
NewWave_1.17.0.zip(r-4.5)NewWave_1.17.0.zip(r-4.4)NewWave_1.17.0.zip(r-4.3)
NewWave_1.17.0.tgz(r-4.4-any)NewWave_1.17.0.tgz(r-4.3-any)
NewWave_1.17.0.tar.gz(r-4.5-noble)NewWave_1.17.0.tar.gz(r-4.4-noble)
NewWave_1.17.0.tgz(r-4.4-emscripten)NewWave_1.17.0.tgz(r-4.3-emscripten)
NewWave.pdf |NewWave.html✨
NewWave/json (API)
NEWS

# Install 'NewWave' in R:
install.packages('NewWave', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/fedeago/newwave/issues

On BioConductor:NewWave-1.17.0(bioc 3.21)NewWave-1.16.0(bioc 3.20)

softwaregeneexpressiontranscriptomicssinglecellbatcheffectsequencingcoverageregressionbatch-effectsdimensionality-reductionnegative-binomialscrna-seq

4.90 score 4 stars 8 scripts 228 downloads 1 mentions 29 exports 47 dependencies

Last updated 2 months agofrom:ef0cec5123. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 18 2024
R-4.5-winNOTEDec 18 2024
R-4.5-linuxNOTEDec 18 2024
R-4.4-winNOTEDec 18 2024
R-4.4-macNOTEDec 18 2024
R-4.3-winOKDec 18 2024
R-4.3-macOKDec 18 2024

Exports:newAICnewAlphanewBetanewBICnewEpsilon_alphanewEpsilon_betanewEpsilon_gammanewEpsilon_WnewEpsilon_zetanewFitnewGammanewlogliknewLogMunewmodelnewMunewpenaltynewPhinewSimnewThetanewVnewWnewWavenewXnewZetanumberFactorsnumberFeaturesnumberParamsnumberSamplesshow

Dependencies:abindaskpassassortheadbeachmatBHBiobaseBiocGenericsBiocParallelBiocSingularcodetoolscpp11crayoncurlDelayedArrayformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangeshttrIRangesirlbajsonlitelambda.rlatticeMatrixMatrixGenericsmatrixStatsmimeopensslR6RcpprsvdS4ArraysS4VectorsScaledMatrixSharedObjectSingleCellExperimentsnowSparseArraySummarizedExperimentsysUCSC.utilsXVectorzlibbioc

Dimensionality reduction and batch effect removal using NewWave

Rendered fromvignette.Rmdusingknitr::rmarkdownon Dec 18 2024.

Last update: 2020-09-28
Started: 2020-07-14

Readme and manuals

Help Manual

Help pageTopics
Compute the AIC of a model given some datanewAIC newAIC,newmodel,matrix-method
Returns the matrix of paramters alphanewAlpha
Returns the matrix of paramters betanewBeta
Compute the BIC of a model given some datanewBIC newBIC,newmodel,matrix-method
Returns the vector of regularization parameter for alphanewEpsilon_alpha
Returns the vector of regularization parameter for betanewEpsilon_beta
Returns the vector of regularization parameter for gammanewEpsilon_gamma
Returns the vector of regularization parameter for WnewEpsilon_W
Returns the regularization parameter for the dispersion parameternewEpsilon_zeta
Fit a nb regression modelnewFit newFit,DelayedMatrix-method newFit,dgCMatrix-method newFit,matrix-method newFit,SummarizedExperiment-method
Returns the matrix of paramters gammanewGamma
Compute the log-likelihood of a model given some datanewloglik newloglik,newmodel,matrix-method
Returns the matrix of logarithm of mean parametersnewLogMu
Initialize an object of class newmodelnewmodel
Class newmodelnewAlpha,newmodel-method newBeta,newmodel-method newEpsilon_alpha,newmodel-method newEpsilon_beta,newmodel-method newEpsilon_gamma,newmodel-method newEpsilon_W,newmodel-method newEpsilon_zeta,newmodel-method newGamma,newmodel-method newLogMu,newmodel-method newmodel-class newMu,newmodel-method newPhi,newmodel-method newTheta,newmodel-method newV,newmodel-method newW,newmodel-method newX,newmodel-method newZeta,newmodel-method numberFactors,newmodel-method numberFeatures,newmodel-method numberSamples,newmodel-method show,newmodel-method
Returns the matrix of mean parametersnewMu
Compute the penalty of a modelnewpenalty newpenalty,newmodel-method
Returns the vector of dispersion parametersnewPhi
Simulate counts from a negative binomial modelnewSim newSim,newmodel-method
Returns the vector of inverse dispersion parametersnewTheta
Returns the gene-level design matrix for munewV
Returns the low-dimensional matrix of inferred sample-level covariates WnewW
Perform dimensionality reduction using a nb regression model with gene and cell-level covariates.newWave newWave,SummarizedExperiment-method
Returns the sample-level design matrix for munewX
Returns the vector of log of inverse dispersion parametersnewZeta
Generic function that returns the number of latent factorsnumberFactors
Generic function that returns the number of featuresnumberFeatures
Generic function that returns the total number of parameters of the modelnumberParams numberParams,newmodel-method
Generic function that returns the number of samplesnumberSamples