pairadise-FUN: pairadise
In PAIRADISE: PAIRADISE: Paired analysis of differential isoform expression

Description Usage Arguments Details Value Examples

Primary function of the PAIRADISE package. Analyzes matched pairs for differences in isoform expression. Uses parallel processing to speed up computation.

pairadise(
  pdat,
  nIter = 100,
  tol = 10^(-2),
  pseudocount = 0,
  seed = 12321,
  equal.variance = FALSE,
  numCluster = 2,
  BPPARAM = MulticoreParam(numCluster)
)

`pdat`	A PDseDataSet object
`nIter`	Positive integer. Specifies the maximum number of iterations of the optimization algorithm allowed. Default is nIter = 100
`tol`	Positive number. Specifies the tolerance level for terminating the optimization algorithm, defined as the difference in log-likelihood ratios between iterations. Default is tol = 10^(-2)
`pseudocount`	Positive number. Specifies a value for a pseudocount added to each count at the beginning of the analysis. Default is pseudocount = 0
`seed`	An integer to set seed.
`equal.variance`	Are the group variances assumed equal? Default value is FALSE.
`numCluster`	Number of clusters to use for parallel computing.
`BPPARAM`	parallel parameters from package BiocParallel.

This is the primary function of the PAIRADISE package that implements the PAIRADISE algorithm.

A PDseDataSet object contains outputs from PAIRADISE algorithm.

#############################
## Example: Simulated data ##
#############################

set.seed(12345)
data("sample_dataset")
pdat <- PDseDataSetFromMat(sample_dataset)
pdat <- pairadise(pdat, numCluster =4)
results(pdat)