# Zero-Inflated Negative Binomial Model for RNA-Seq Data

## Documentation for package ‘zinbwave’ version 1.35.0

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

## Help Pages

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.