Zero-Inflated Negative Binomial Model for RNA-Seq Data

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Documentation for package ‘zinbwave’ version 1.29.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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