ZinbModel-class: Class ZinbModel
In drisso/zinbwave: Zero-Inflated Negative Binomial Model for RNA-Seq Data

ZinbModel-class

R Documentation

Class ZinbModel

Description

Objects of this class store all the values needed to work with a zero-inflated negative binomial (ZINB) model, as described in the vignette. They contain all information to fit a model by penalized maximum likelihood or simulate data from a model.

Usage

## S4 method for signature 'ZinbModel'
show(object)

## S4 method for signature 'ZinbModel'
nSamples(x)

## S4 method for signature 'ZinbModel'
nFeatures(x)

## S4 method for signature 'ZinbModel'
nFactors(x)

## S4 method for signature 'ZinbModel'
getX_mu(object, intercept = TRUE)

## S4 method for signature 'ZinbModel'
getX_pi(object, intercept = TRUE)

## S4 method for signature 'ZinbModel'
getV_mu(object, intercept = TRUE)

## S4 method for signature 'ZinbModel'
getV_pi(object, intercept = TRUE)

## S4 method for signature 'ZinbModel'
getLogMu(object)

## S4 method for signature 'ZinbModel'
getMu(object)

## S4 method for signature 'ZinbModel'
getLogitPi(object)

## S4 method for signature 'ZinbModel'
getPi(object)

## S4 method for signature 'ZinbModel'
getZeta(object)

## S4 method for signature 'ZinbModel'
getPhi(object)

## S4 method for signature 'ZinbModel'
getTheta(object)

## S4 method for signature 'ZinbModel'
getEpsilon_beta_mu(object)

## S4 method for signature 'ZinbModel'
getEpsilon_gamma_mu(object)

## S4 method for signature 'ZinbModel'
getEpsilon_beta_pi(object)

## S4 method for signature 'ZinbModel'
getEpsilon_gamma_pi(object)

## S4 method for signature 'ZinbModel'
getEpsilon_W(object)

## S4 method for signature 'ZinbModel'
getEpsilon_alpha(object)

## S4 method for signature 'ZinbModel'
getEpsilon_zeta(object)

## S4 method for signature 'ZinbModel'
getW(object)

## S4 method for signature 'ZinbModel'
getBeta_mu(object)

## S4 method for signature 'ZinbModel'
getBeta_pi(object)

## S4 method for signature 'ZinbModel'
getGamma_mu(object)

## S4 method for signature 'ZinbModel'
getGamma_pi(object)

## S4 method for signature 'ZinbModel'
getAlpha_mu(object)

## S4 method for signature 'ZinbModel'
getAlpha_pi(object)

Arguments

`object`	an object of class `ZinbModel`.
`x`	an object of class `ZinbModel`.
`intercept`	logical. Whether to return the intercept (ignored if the design matrix has no intercept). Default `TRUE`

Details

For the full description of the model see the model vignette. Internally, the slots are checked so that the matrices are of the appropriate dimensions: in particular, X, O_mu, O_pi, and W need to have n rows, V needs to have J rows, zeta must be of length J.

Value

nSamples returns the number of samples; nFeatures returns the number of features; nFactors returns the number of latent factors.

Methods (by generic)

show(ZinbModel): show useful info on the object.
nSamples(ZinbModel): returns the number of samples.
nFeatures(ZinbModel): returns the number of features.
nFactors(ZinbModel): returns the number of latent factors.
getX_mu(ZinbModel): returns the sample-level design matrix for mu.
getX_pi(ZinbModel): returns the sample-level design matrix for pi.
getV_mu(ZinbModel): returns the gene-level design matrix for mu.
getV_pi(ZinbModel): returns the sample-level design matrix for pi.
getLogMu(ZinbModel): returns the logarithm of the mean of the non-zero component.
getMu(ZinbModel): returns the mean of the non-zero component.
getLogitPi(ZinbModel): returns the logit-probability of zero.
getPi(ZinbModel): returns the probability of zero.
getZeta(ZinbModel): returns the log of the inverse of the dispersion parameter.
getPhi(ZinbModel): returns the dispersion parameter.
getTheta(ZinbModel): returns the inverse of the dispersion parameter.
getEpsilon_beta_mu(ZinbModel): returns the regularization parameters for beta_mu.
getEpsilon_gamma_mu(ZinbModel): returns the regularization parameters for gamma_mu.
getEpsilon_beta_pi(ZinbModel): returns the regularization parameters for beta_pi.
getEpsilon_gamma_pi(ZinbModel): returns the regularization parameters for gamma_pi.
getEpsilon_W(ZinbModel): returns the regularization parameters for W.
getEpsilon_alpha(ZinbModel): returns the regularization parameters for alpha.
getEpsilon_zeta(ZinbModel): returns the regularization parameters for zeta.
getW(ZinbModel): returns the matrix W of inferred sample-level covariates.
getBeta_mu(ZinbModel): returns the matrix beta_mu of inferred parameters.
getBeta_pi(ZinbModel): returns the matrix beta_pi of inferred parameters.
getGamma_mu(ZinbModel): returns the matrix gamma_mu of inferred parameters.
getGamma_pi(ZinbModel): returns the matrix gamma_pi of inferred parameters.
getAlpha_mu(ZinbModel): returns the matrix alpha_mu of inferred parameters.
getAlpha_pi(ZinbModel): returns the matrix alpha_pi of inferred parameters.

Slots

X: matrix. The design matrix containing sample-level covariates, one sample per row.
V: matrix. The design matrix containing gene-level covariates, one gene per row.
O_mu: matrix. The offset matrix for mu.
O_pi: matrix. The offset matrix for pi.
which_X_mu: integer. Indeces of which columns of X to use in the regression of mu.
which_V_mu: integer. Indeces of which columns of V to use in the regression of mu.
which_X_pi: integer. Indeces of which columns of X to use in the regression of pi.
which_V_pi: integer. Indeces of which columns of V to use in the regression of pi.
X_mu_intercept: logical. TRUE if X_mu contains an intercept.
X_pi_intercept: logical. TRUE if X_pi contains an intercept.
V_mu_intercept: logical. TRUE if V_mu contains an intercept.
V_pi_intercept: logical. TRUE if V_pi contains an intercept.
W: matrix. The factors of sample-level latent factors.
beta_mu: matrix or NULL. The coefficients of X in the regression of mu.
gamma_mu: matrix or NULL. The coefficients of V in the regression of mu.
alpha_mu: matrix or NULL. The coefficients of W in the regression of mu.
beta_pi: matrix or NULL. The coefficients of X in the regression of pi.
gamma_pi: matrix or NULL. The coefficients of V in the regression of pi.
alpha_pi: matrix or NULL. The coefficients of W in the regression of pi.
zeta: numeric. A vector of log of inverse dispersion parameters.
epsilon_beta_mu: nonnegative scalar. Regularization parameter for beta_mu
epsilon_gamma_mu: nonnegative scalar. Regularization parameter for gamma_mu
epsilon_beta_pi: nonnegative scalar. Regularization parameter for beta_pi
epsilon_gamma_pi: nonnegative scalar. Regularization parameter for gamma_pi
epsilon_W: nonnegative scalar. Regularization parameter for W
epsilon_alpha: nonnegative scalar. Regularization parameter for alpha (both alpha_mu and alpha_pi)
epsilon_zeta: nonnegative scalar. Regularization parameter for zeta
epsilon_min_logit: scalar. Minimum regularization parameter for parameters of the logit model, including the intercept.