pairadise-FUN: pairadise

In PAIRADISE: PAIRADISE: Paired analysis of differential isoform expression

Description

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

Usage

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

Arguments

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.

Details

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

Value

A PDseDataSet object contains outputs from PAIRADISE algorithm.

Examples

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

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

PAIRADISE documentation built on Nov. 8, 2020, 8:22 p.m.