Zero-Inflated Negative Binomial Model for RNA-Seq Data


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Documentation for package ‘zinbwave’ version 1.29.0

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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.