Package: zinbwave 1.35.0

Davide Risso

zinbwave: Zero-Inflated Negative Binomial Model for RNA-Seq Data

Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.

Authors:Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
zinbwave/json (API)

# Install 'zinbwave' in R:

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

Bug tracker:https://github.com/drisso/zinbwave/issues

Datasets:

toydata - Toy dataset to check the model

On BioConductor:zinbwave-1.35.0(bioc 3.24)zinbwave-1.34.0(bioc 3.23)

immunooncology dimensionreduction geneexpression rnaseq software transcriptomics sequencing singlecell

10.12 score 44 stars 5 packages 285 scripts 12 mentions 53 exports 64 dependencies

Last updated from:6100d53205. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	301
linux-devel-x86_64	NOTE	470
source / vignettes	OK	400
linux-release-x86_64	NOTE	467
macos-release-arm64	NOTE	275
macos-oldrel-arm64	NOTE	252
windows-devel	NOTE	1396
windows-release	NOTE	1435
windows-oldrel	NOTE	1356
wasm-release	OK	245

Exports:computeDevianceResiduals computeObservationalWeights getAlpha_mu getAlpha_pi getBeta_mu getBeta_pi getEpsilon_alpha getEpsilon_beta_mu getEpsilon_beta_pi getEpsilon_gamma_mu getEpsilon_gamma_pi getEpsilon_W getEpsilon_zeta getGamma_mu getGamma_pi getLogitPi getLogMu getMu getPhi getPi getTheta getV_mu getV_pi getW getX_mu getX_pi getZeta glmWeightedF imputeZeros loglik nFactors nFeatures nParams nSamples orthogonalizeTraceNorm penalty show solveRidgeRegression zinb.loglik zinb.loglik.dispersion zinb.loglik.dispersion.gradient zinb.loglik.regression zinb.loglik.regression.gradient zinbAIC zinbBIC zinbFit zinbInitialize zinbModel zinbOptimize zinbOptimizeDispersion zinbSim zinbsurf zinbwave

Dependencies:abind annotate AnnotationDbi askpass BH Biobase BiocGenerics BiocParallel Biostrings bit bit64 blob cachem cli codetools cpp11 crayon curl DBI DelayedArray edgeR fastmap formatR futile.logger futile.options genefilter generics GenomicRanges glue httr IRanges jsonlite KEGGREST lambda.r lattice lifecycle limma locfit Matrix MatrixGenerics matrixStats memoise mime openssl pkgconfig png R6 rlang RSQLite S4Arrays S4Vectors Seqinfo SingleCellExperiment snow softImpute SparseArray statmod SummarizedExperiment survival sys vctrs XML xtable XVector

An introduction to ZINB-WaVE

Rendered fromintro.Rmdusingknitr::rmarkdown

Last update: 2024-03-07
Started: 2017-06-22

Help page	Topics
Deviance residuals of the zero-inflated negative binomial model	computeDevianceResiduals
Observational weights of the zero-inflated negative binomial model for each entry in the matrix of counts	computeObservationalWeights
Returns the matrix of paramters alpha_mu	getAlpha_mu
Returns the matrix of paramters alpha_pi	getAlpha_pi
Returns the matrix of paramters beta_mu	getBeta_mu
Returns the matrix of paramters beta_pi	getBeta_pi
Returns the vector of regularization parameter for alpha	getEpsilon_alpha
Returns the vector of regularization parameter for beta_mu	getEpsilon_beta_mu
Returns the vector of regularization parameter for beta_pi	getEpsilon_beta_pi
Returns the vector of regularization parameter for gamma_mu	getEpsilon_gamma_mu
Returns the vector of regularization parameter for gamma_pi	getEpsilon_gamma_pi
Returns the vector of regularization parameter for W	getEpsilon_W
Returns the regularization parameter for the dispersion parameter	getEpsilon_zeta
Returns the matrix of paramters gamma_mu	getGamma_mu
Returns the matrix of paramters gamma_pi	getGamma_pi
Returns the matrix of logit of probabilities of zero	getLogitPi
Returns the matrix of logarithm of mean parameters	getLogMu
Returns the matrix of mean parameters	getMu
Returns the vector of dispersion parameters	getPhi
Returns the matrix of probabilities of zero	getPi
Returns the vector of inverse dispersion parameters	getTheta
Returns the gene-level design matrix for mu	getV_mu
Returns the gene-level design matrix for pi	getV_pi
Returns the low-dimensional matrix of inferred sample-level covariates W	getW
Returns the sample-level design matrix for mu	getX_mu
Returns the sample-level design matrix for pi	getX_pi
Returns the vector of log of inverse dispersion parameters	getZeta
Zero-inflation adjusted statistical tests for assessing differential expression.	glmWeightedF
Impute the zeros using the estimated parameters from the ZINB model.	imputeZeros
Perform independent filtering in differential expression analysis.	independentFiltering
Compute the log-likelihood of a model given some data	loglik loglik,ZinbModel,matrix-method
Generic function that returns the number of latent factors	nFactors
Generic function that returns the number of features	nFeatures
Generic function that returns the total number of parameters of the model	nParams nParams,ZinbModel-method
Generic function that returns the number of samples	nSamples
Orthogonalize matrices to minimize trace norm of their product	orthogonalizeTraceNorm
Compute the penalty of a model	penalty penalty,ZinbModel-method
Perform independent filtering in differential expression analysis.	pvalueAdjustment
Solve ridge regression or logistic regression problems	solveRidgeRegression
Toy dataset to check the model	toydata
Log-likelihood of the zero-inflated negative binomial model	zinb.loglik
Log-likelihood of the zero-inflated negative binomial model, for a fixed dispersion parameter	zinb.loglik.dispersion
Derivative of the log-likelihood of the zero-inflated negative binomial model with respect to the log of the inverse dispersion parameter	zinb.loglik.dispersion.gradient
Log-likelihood of the zero-inflated negative binomial model for each entry in the matrix of counts	zinb.loglik.matrix
Penalized log-likelihood of the ZINB regression model	zinb.loglik.regression
Gradient of the penalized log-likelihood of the ZINB regression model	zinb.loglik.regression.gradient
Parse ZINB regression model	zinb.regression.parseModel
Compute the AIC or BIC of a model given some data	zinbAIC zinbAIC,ZinbModel,matrix-method zinbBIC zinbBIC,ZinbModel,matrix-method
Fit a ZINB regression model	zinbFit zinbFit,dgCMatrix-method zinbFit,matrix-method zinbFit,SummarizedExperiment-method
Initialize the parameters of a ZINB regression model	zinbInitialize
Initialize an object of class ZinbModel	zinbModel
Class ZinbModel	getAlpha_mu,ZinbModel-method getAlpha_pi,ZinbModel-method getBeta_mu,ZinbModel-method getBeta_pi,ZinbModel-method getEpsilon_alpha,ZinbModel-method getEpsilon_beta_mu,ZinbModel-method getEpsilon_beta_pi,ZinbModel-method getEpsilon_gamma_mu,ZinbModel-method getEpsilon_gamma_pi,ZinbModel-method getEpsilon_W,ZinbModel-method getEpsilon_zeta,ZinbModel-method getGamma_mu,ZinbModel-method getGamma_pi,ZinbModel-method getLogitPi,ZinbModel-method getLogMu,ZinbModel-method getMu,ZinbModel-method getPhi,ZinbModel-method getPi,ZinbModel-method getTheta,ZinbModel-method getV_mu,ZinbModel-method getV_pi,ZinbModel-method getW,ZinbModel-method getX_mu,ZinbModel-method getX_pi,ZinbModel-method getZeta,ZinbModel-method nFactors,ZinbModel-method nFeatures,ZinbModel-method nSamples,ZinbModel-method show,ZinbModel-method ZinbModel ZinbModel-class
Optimize the parameters of a ZINB regression model	zinbOptimize
Optimize the dispersion parameters of a ZINB regression model	zinbOptimizeDispersion
Simulate counts from a zero-inflated negative binomial model	zinbSim zinbSim,ZinbModel-method
Perform dimensionality reduction using a ZINB regression model for large datasets.	zinbsurf zinbsurf,SummarizedExperiment-method
Perform dimensionality reduction using a ZINB regression model with gene and cell-level covariates.	zinbwave zinbwave,SummarizedExperiment-method

Package: zinbwave 1.35.0

zinbwave: Zero-Inflated Negative Binomial Model for RNA-Seq Data

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