Package: NewWave 1.23.0

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]

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DESCRIPTION |NEWS
card.svg |card.png
NewWave/json (API)

# Install 'NewWave' in R:

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

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

On BioConductor:NewWave-1.23.0(bioc 3.24)NewWave-1.21.0(bioc 3.23)

software geneexpression transcriptomics singlecell batcheffect sequencing coverage regression batch-effects dimensionality-reduction negative-binomial scrna-seq

5.38 score 4 stars 30 scripts 344 downloads 1 mentions 29 exports 35 dependencies

Last updated from:79ebef3a81. Checks:1 WARNING, 2 FAIL, 2 OK, 5 NOTE. Indexed: yes.

The latest version of this package failed to build. Look at thebuild logs for more information.

Target	Result	Time
bioc-checks	WARNING	210
linux-devel-x86_64	FAIL	169
source / vignettes	OK	310
linux-release-x86_64	NOTE	339
macos-release-arm64	NOTE	211
macos-oldrel-arm64	NOTE	176
windows-devel	FAIL	118
windows-release	NOTE	259
windows-oldrel	NOTE	241
wasm-release	OK	145

Exports:newAIC newAlpha newBeta newBIC newEpsilon_alpha newEpsilon_beta newEpsilon_gamma newEpsilon_W newEpsilon_zeta newFit newGamma newloglik newLogMu newmodel newMu newpenalty newPhi newSim newTheta newV newW newWave newX newZeta numberFactors numberFeatures numberParams numberSamples show

Dependencies:abind assorthead beachmat BH Biobase BiocGenerics BiocParallel BiocSingular codetools cpp11 DelayedArray formatR futile.logger futile.options generics GenomicRanges IRanges irlba lambda.r lattice Matrix MatrixGenerics matrixStats Rcpp rsvd S4Arrays S4Vectors ScaledMatrix Seqinfo SharedObject SingleCellExperiment snow SparseArray SummarizedExperiment XVector

Dimensionality reduction and batch effect removal using NewWave

Rendered fromvignette.Rmdusingknitr::rmarkdown

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

Help page	Topics
Compute the AIC of a model given some data	newAIC newAIC,newmodel,matrix-method
Returns the matrix of paramters alpha	newAlpha
Returns the matrix of paramters beta	newBeta
Compute the BIC of a model given some data	newBIC newBIC,newmodel,matrix-method
Returns the vector of regularization parameter for alpha	newEpsilon_alpha
Returns the vector of regularization parameter for beta	newEpsilon_beta
Returns the vector of regularization parameter for gamma	newEpsilon_gamma
Returns the vector of regularization parameter for W	newEpsilon_W
Returns the regularization parameter for the dispersion parameter	newEpsilon_zeta
Fit a nb regression model	newFit newFit,DelayedMatrix-method newFit,dgCMatrix-method newFit,matrix-method newFit,SummarizedExperiment-method
Returns the matrix of paramters gamma	newGamma
Compute the log-likelihood of a model given some data	newloglik newloglik,newmodel,matrix-method
Returns the matrix of logarithm of mean parameters	newLogMu
Initialize an object of class newmodel	newmodel
Class newmodel	newAlpha,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 parameters	newMu
Compute the penalty of a model	newpenalty newpenalty,newmodel-method
Returns the vector of dispersion parameters	newPhi
Simulate counts from a negative binomial model	newSim newSim,newmodel-method
Returns the vector of inverse dispersion parameters	newTheta
Returns the gene-level design matrix for mu	newV
Returns the low-dimensional matrix of inferred sample-level covariates W	newW
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 mu	newX
Returns the vector of log of inverse dispersion parameters	newZeta
Generic function that returns the number of latent factors	numberFactors
Generic function that returns the number of features	numberFeatures
Generic function that returns the total number of parameters of the model	numberParams numberParams,newmodel-method
Generic function that returns the number of samples	numberSamples

Package: NewWave 1.23.0

NewWave: Negative binomial model for scRNA-seq

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