qa: Parallel Quality Assessment Summary

In affyPara: Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays

Description

Creates a Summary Matrix from parallel quality assessment results.

Usage

summaryM1M2Para(method1, method2, 
		level, verbose=FALSE)

Arguments

method1	Result object form boxplotPara.
method2	Result object from MAplotPara.
level	level- numerical - indicates which level of "bad" quality arrays should be plot if plotDraw =TRUE: 1 - only first level "bad" quality will be considered. First level "bad" array quality are the arrays considered as "bad" after the three possible parameter: S, loess, and sigma 2 - first level "bad" quality and second level will be considered. Second level "bad" quality Arrays are the arrays which has been classified as bad after two of the three possible parameter 3 - all levels will be plot : first, second and third. Third level "bad" quality Arrays are the arrays which are considered as "bad" after one of the three parameter.
verbose	A logical value. If TRUE it writes out some messages. default: getOption("verbose")

Details

summaryM1M2Para creates a Summary Matrix from parallel quality assessment results. In the rows there are the arrays and in the colums the qa-methods: 0 = good quality, 1 = bad quality.

If the rowSum is bigger than 2, than the arrays should be considered as bad quality.

Value

A matrix of all arrays (rows) and qa-methods (colums): 0 = good quality, 1 = bad quality

Author(s)

Esmeralda Vicedo <[e-mail]>, Markus Schmidberger [e-mail], Ulrich Mansmann [e-mail]

Examples

## Not run: 
library(affyPara)
if (require(affydata)) {
  data(Dilution)

  makeCluster(3, type='MPI')

  box1 <- boxplotPara(Dilution)
  ma1 <- MAplotPara(Dilution)
  
  summaryM1M2Para(box1, ma1, level=3)

  stopCluster()
}

## End(Not run)

affyPara documentation built on Nov. 8, 2020, 11:08 p.m.