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. |