Package: zinbwave 1.29.0

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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NEWS

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

Peer review:

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

Datasets:
  • toydata - Toy dataset to check the model

On BioConductor:zinbwave-1.27.0(bioc 3.21)zinbwave-1.28.0(bioc 3.20)

immunooncologydimensionreductiongeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecell

10.59 score 43 stars 7 packages 190 scripts 1.6k downloads 12 mentions 53 exports 68 dependencies

Last updated 23 days agofrom:4cf7edc24a. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-winWARNINGNov 18 2024
R-4.5-linuxWARNINGNov 18 2024
R-4.4-winWARNINGNov 18 2024
R-4.4-macWARNINGNov 18 2024
R-4.3-winWARNINGNov 18 2024
R-4.3-macWARNINGNov 18 2024

Exports:computeDevianceResidualscomputeObservationalWeightsgetAlpha_mugetAlpha_pigetBeta_mugetBeta_pigetEpsilon_alphagetEpsilon_beta_mugetEpsilon_beta_pigetEpsilon_gamma_mugetEpsilon_gamma_pigetEpsilon_WgetEpsilon_zetagetGamma_mugetGamma_pigetLogitPigetLogMugetMugetPhigetPigetThetagetV_mugetV_pigetWgetX_mugetX_pigetZetaglmWeightedFimputeZeroslogliknFactorsnFeaturesnParamsnSamplesorthogonalizeTraceNormpenaltyshowsolveRidgeRegressionzinb.loglikzinb.loglik.dispersionzinb.loglik.dispersion.gradientzinb.loglik.regressionzinb.loglik.regression.gradientzinbAICzinbBICzinbFitzinbInitializezinbModelzinbOptimizezinbOptimizeDispersionzinbSimzinbsurfzinbwave

Dependencies:abindannotateAnnotationDbiaskpassBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobcachemclicodetoolscpp11crayoncurlDBIDelayedArrayedgeRfastmapformatRfutile.loggerfutile.optionsgenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesgluehttrIRangesjsonliteKEGGRESTlambda.rlatticelifecyclelimmalocfitMatrixMatrixGenericsmatrixStatsmemoisemimeopensslpkgconfigplogrpngR6rlangRSQLiteS4ArraysS4VectorsSingleCellExperimentsnowsoftImputeSparseArraystatmodSummarizedExperimentsurvivalsysUCSC.utilsvctrsXMLxtableXVectorzlibbioc

An introduction to ZINB-WaVE

Davide Risso

Rendered fromintro.Rmdusingknitr::rmarkdownon Nov 18 2024.

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

Readme and manuals

Help Manual

Help pageTopics
Deviance residuals of the zero-inflated negative binomial modelcomputeDevianceResiduals
Observational weights of the zero-inflated negative binomial model for each entry in the matrix of countscomputeObservationalWeights
Returns the matrix of paramters alpha_mugetAlpha_mu
Returns the matrix of paramters alpha_pigetAlpha_pi
Returns the matrix of paramters beta_mugetBeta_mu
Returns the matrix of paramters beta_pigetBeta_pi
Returns the vector of regularization parameter for alphagetEpsilon_alpha
Returns the vector of regularization parameter for beta_mugetEpsilon_beta_mu
Returns the vector of regularization parameter for beta_pigetEpsilon_beta_pi
Returns the vector of regularization parameter for gamma_mugetEpsilon_gamma_mu
Returns the vector of regularization parameter for gamma_pigetEpsilon_gamma_pi
Returns the vector of regularization parameter for WgetEpsilon_W
Returns the regularization parameter for the dispersion parametergetEpsilon_zeta
Returns the matrix of paramters gamma_mugetGamma_mu
Returns the matrix of paramters gamma_pigetGamma_pi
Returns the matrix of logit of probabilities of zerogetLogitPi
Returns the matrix of logarithm of mean parametersgetLogMu
Returns the matrix of mean parametersgetMu
Returns the vector of dispersion parametersgetPhi
Returns the matrix of probabilities of zerogetPi
Returns the vector of inverse dispersion parametersgetTheta
Returns the gene-level design matrix for mugetV_mu
Returns the gene-level design matrix for pigetV_pi
Returns the low-dimensional matrix of inferred sample-level covariates WgetW
Returns the sample-level design matrix for mugetX_mu
Returns the sample-level design matrix for pigetX_pi
Returns the vector of log of inverse dispersion parametersgetZeta
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 dataloglik loglik,ZinbModel,matrix-method
Generic function that returns the number of latent factorsnFactors
Generic function that returns the number of featuresnFeatures
Generic function that returns the total number of parameters of the modelnParams nParams,ZinbModel-method
Generic function that returns the number of samplesnSamples
Orthogonalize matrices to minimize trace norm of their productorthogonalizeTraceNorm
Compute the penalty of a modelpenalty penalty,ZinbModel-method
Perform independent filtering in differential expression analysis.pvalueAdjustment
Solve ridge regression or logistic regression problemssolveRidgeRegression
Toy dataset to check the modeltoydata
Log-likelihood of the zero-inflated negative binomial modelzinb.loglik
Log-likelihood of the zero-inflated negative binomial model, for a fixed dispersion parameterzinb.loglik.dispersion
Derivative of the log-likelihood of the zero-inflated negative binomial model with respect to the log of the inverse dispersion parameterzinb.loglik.dispersion.gradient
Log-likelihood of the zero-inflated negative binomial model for each entry in the matrix of countszinb.loglik.matrix
Penalized log-likelihood of the ZINB regression modelzinb.loglik.regression
Gradient of the penalized log-likelihood of the ZINB regression modelzinb.loglik.regression.gradient
Parse ZINB regression modelzinb.regression.parseModel
Compute the AIC or BIC of a model given some datazinbAIC zinbAIC,ZinbModel,matrix-method zinbBIC zinbBIC,ZinbModel,matrix-method
Fit a ZINB regression modelzinbFit zinbFit,dgCMatrix-method zinbFit,matrix-method zinbFit,SummarizedExperiment-method
Initialize the parameters of a ZINB regression modelzinbInitialize
Initialize an object of class ZinbModelzinbModel
Class ZinbModelgetAlpha_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 modelzinbOptimize
Optimize the dispersion parameters of a ZINB regression modelzinbOptimizeDispersion
Simulate counts from a zero-inflated negative binomial modelzinbSim 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