estimateBetaPriorVar: Steps for estimating the beta prior variance

In DESeq2: Differential gene expression analysis based on the negative binomial distribution

Description

These lower-level functions are called within DESeq or nbinomWaldTest. End users should use those higher-level function instead. NOTE: estimateBetaPriorVar returns a numeric vector, not a DESEqDataSet! For advanced users: to use these functions, first run estimateMLEForBetaPriorVar and then run estimateBetaPriorVar.

Usage

estimateBetaPriorVar(
  object,
  betaPriorMethod = c("weighted", "quantile"),
  upperQuantile = 0.05,
  modelMatrix = NULL
)

estimateMLEForBetaPriorVar(
  object,
  maxit = 100,
  useOptim = TRUE,
  useQR = TRUE,
  modelMatrixType = NULL
)

Arguments

object	a DESeqDataSet
betaPriorMethod	the method for calculating the beta prior variance, either "quanitle" or "weighted": "quantile" matches a normal distribution using the upper quantile of the finite MLE betas. "weighted" matches a normal distribution using the upper quantile, but weighting by the variance of the MLE betas.
upperQuantile	the upper quantile to be used for the "quantile" or "weighted" method of beta prior variance estimation
modelMatrix	an optional matrix, typically this is set to NULL and created within the function
maxit	as defined in link{nbinomWaldTest}
useOptim	as defined in link{nbinomWaldTest}
useQR	as defined in link{nbinomWaldTest}
modelMatrixType	an optional override for the type which is set internally

Value

for estimateMLEForBetaPriorVar, a DESeqDataSet, with the necessary information stored in order to calculate the prior variance. for estimateBetaPriorVar, the vector of variances for the prior on the betas in the DESeq GLM

DESeq2 documentation built on Feb. 22, 2021, 10 a.m.