optimizeParameters: Optimize parameters of removal of unwanted variation.

In PeteHaitch/RUVcorr: Removal of unwanted variation for gene-gene correlations and related analysis.

Description

optimizeParameters returns the optimal parameters to be used in the removal of unwanted variation procedure when using simulated data.

Usage

optimizeParameters(Y, kW.hat = seq(5, 25, 5), nu.hat = c(0, 10, 100, 1000,
  10000), nc_index, methods = c("all", "fnorm", "wrong.sign"), cpus = 1,
  parallel = FALSE, check.input = FALSE)

Arguments

Y	An object of the class simualteGEdata.
kW.hat	A vector of integers for kW in RUVNaiveRidge.
nu.hat	A vector of values for nu in RUVNaiveRidge.
nc_index	A vector of indices of the negative controls used in RUVNaiveRidge.
methods	The method used for quality assessment; if method="fnorm" the Frobenius norm is used; if method="wrong.sign" the percentage of wrongly estimated signs is calculated if method="all" both are calculated.
cpus	Numerical amount of CPUs requested for the cluster. If not set, values from the commandline are taken.
parallel	Logical determinating parallel or sequential execution. If not set values from commandline are taken.
check.input	Logical; if TRUE all input is checked; not advisable for large simulations.

Details

The simulated data is cleaned using removal of unwanted variation with all combinations of the input parameters. The quality of each cleaning is judged by the Frobenius Norm of the correlation as estimated from the cleaned data and the known data or the percentage of correlations with estimated to have the wrong sign.

Value

optimizeParameters returns output of the class optimizeParameters. An object of class optimizeParameters is a list containing the following components:

All.results

A matrix of output of the quality assessment for all combinations of input parameters.

Compare.raw

A vector of the quality assessment for the uncorrected data.

Optimal.parameter

A matrix or a vector giving the optimal parameter combination.

Author(s)

Saskia Freytag

See Also

assessQuality, RUVNaiveRidge

Examples

Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1, 250, 100, check.input=FALSE)
# This calculation may take a couple of minutes (if possible change number of cpus used)
opt<-optimizeParameters(Y, kW.hat=c(1,5,10), nu.hat=c(100,1000), nc_index=251:500,
methods=c("fnorm"), cpus=1, parallel=FALSE, check.input=TRUE)
opt

PeteHaitch/RUVcorr documentation built on May 8, 2019, 1:31 a.m.