classifyTestsF-MArrayLM2-method: Multiple Testing Genewise Across Contrasts

In GabrielHoffman/variancePartition: Quantify and interpret drivers of variation in multilevel gene expression experiments

Multiple Testing Genewise Across Contrasts

Description

For each gene, classify a series of related t-statistics as up, down or not significant.

Usage

## S4 method for signature 'MArrayLM2'
classifyTestsF(
  object,
  cor.matrix = NULL,
  df = Inf,
  p.value = 0.01,
  fstat.only = FALSE
)

Arguments

object	numeric matrix of t-statistics or an 'MArrayLM2' object from which the t-statistics may be extracted.
cor.matrix	covariance matrix of each row of t-statistics. Defaults to the identity matrix.
df	numeric vector giving the degrees of freedom for the t-statistics. May have length 1 or length equal to the number of rows of tstat.
p.value	numeric value between 0 and 1 giving the desired size of the test
fstat.only	logical, if 'TRUE' then return the overall F-statistic as for 'FStat' instead of classifying the test results

Details

Works like limma::classifyTestsF, except object can have a list of covariance matrices object$cov.coefficients.list, instead of just one in object$cov.coefficients

See Also

limma::classifyTestsF

GabrielHoffman/variancePartition documentation built on April 20, 2024, 7:29 p.m.