zinbFit: Fit a ZINB regression model

Description Usage Arguments Details Value Methods (by class) See Also Examples

Description

Given an object with the data, it fits a ZINB model.

Usage

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zinbFit(Y, ...)

## S4 method for signature 'SummarizedExperiment'
zinbFit(
  Y,
  X,
  V,
  K,
  which_assay,
  commondispersion = TRUE,
  zeroinflation = TRUE,
  verbose = FALSE,
  nb.repeat.initialize = 2,
  maxiter.optimize = 25,
  stop.epsilon.optimize = 1e-04,
  BPPARAM = BiocParallel::bpparam(),
  ...
)

## S4 method for signature 'matrix'
zinbFit(
  Y,
  X,
  V,
  K,
  commondispersion = TRUE,
  zeroinflation = TRUE,
  verbose = FALSE,
  nb.repeat.initialize = 2,
  maxiter.optimize = 25,
  stop.epsilon.optimize = 1e-04,
  BPPARAM = BiocParallel::bpparam(),
  ...
)

## S4 method for signature 'dgCMatrix'
zinbFit(Y, ...)

Arguments

Y

The data (genes in rows, samples in columns).

...

Additional parameters to describe the model, see zinbModel.

X

The design matrix containing sample-level covariates, one sample per row. If missing, X will contain only an intercept. If Y is a SummarizedExperiment object, X can be a formula using the variables in the colData slot of Y.

V

The design matrix containing gene-level covariates, one gene per row. If missing, V will contain only an intercept. If Y is a SummarizedExperiment object, V can be a formula using the variables in the rowData slot of Y.

K

integer. Number of latent factors.

which_assay

numeric or character. Which assay of Y to use (only if Y is a SummarizedExperiment).

commondispersion

Whether or not a single dispersion for all features is estimated (default TRUE).

zeroinflation

Whether or not a ZINB model should be fitted. If FALSE, a negative binomial model is fitted instead.

verbose

Print helpful messages.

nb.repeat.initialize

Number of iterations for the initialization of beta_mu and gamma_mu.

maxiter.optimize

maximum number of iterations for the optimization step (default 25).

stop.epsilon.optimize

stopping criterion in the optimization step, when the relative gain in likelihood is below epsilon (default 0.0001).

BPPARAM

object of class bpparamClass that specifies the back-end to be used for computations. See bpparam for details.

Details

By default, i.e., if no arguments other than Y are passed, the model is fitted with an intercept for the regression across-samples and one intercept for the regression across genes, both for mu and for pi.

This means that by default the model is fitted with X_mu = X_pi = 1_n and V_mu = V_pi = 1_J. If the user explicitly passes the design matrices, this behavior is overwritten, i.e., the user needs to explicitly include the intercept in the design matrices.

If Y is a Summarized experiment, the function uses the assay named "counts", if any, or the first assay.

Currently, if Y is a sparseMatrix, this calls the zinbFit method on as.matrix(Y)

Value

An object of class ZinbModel that has been fitted by penalized maximum likelihood on the data.

Methods (by class)

See Also

model.matrix.

Examples

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se <- SummarizedExperiment(matrix(rpois(60, lambda=5), nrow=10, ncol=6),
                           colData = data.frame(bio = gl(2, 3)))

m <- zinbFit(se, X=model.matrix(~bio, data=colData(se)),
             BPPARAM=BiocParallel::SerialParam())
bio <- gl(2, 3)
m <- zinbFit(matrix(rpois(60, lambda=5), nrow=10, ncol=6),
             X=model.matrix(~bio), BPPARAM=BiocParallel::SerialParam())

zinbwave documentation built on Nov. 8, 2020, 8:11 p.m.