twoGaussiansNull: Estimate a threshold from Gaussian mixture distribution

Description

Function to estimate a threshold from Gaussian mixture distribution. The data is assumed to follow a mixture of two Gaussian distributions. The one Gaussian with the lower mean value is assumed to be the null distribution and probe levels are assigned p-values based on this null distribution. The threshold is then the minimal data value with an adjusted p-value smaller than a specified threshold.

Usage

twoGaussiansNull(x, p.adj.method = "BY", max.adj.p = 0.1, var.equal = FALSE, ...)

Arguments

numeric vector of data values

p.adj.method

method for adjusting the p-values for multiple testing; must be one of p.adjust.methods

max.adj.p

which adjusted p-value to use as upper limit for estimating the threshold

var.equal

logical; is the variance of the two Gaussians assumed to be equal or different

...

further arguments passed on to function Mclust

Value

Single numeric value. The threshold that is the minimal data value with an adjusted p-value smaller than a specified threshold.

Details

This function uses the package mclust to fit a mixture of two Gaussians to the data. The threshold is then estimated from the fitted Gaussian with the lower mean value.

Examples

Run this code

  exDir <- system.file("exData",package="Ringo")
  load(file.path(exDir,"exampleProbeAnno.rda"))
  load(file.path(exDir,"exampleX.rda"))
  smoothX <- computeRunningMedians(exampleX, probeAnno=exProbeAnno,
     modColumn = "Cy5", allChr = "9", winHalfSize = 400)

  ## compare the two different ways of estimating the threshold
  y0a <- apply(exprs(smoothX), 2, upperBoundNull)
  y0b <- apply(exprs(smoothX), 2, twoGaussiansNull)

  hist(exprs(smoothX)[,1], n=10, main=NA,
       xlab="Smoothed expression level [log2]")
  abline(v=c(y0a, y0b), col=c("blue","orange"), lwd=2)
  legend(x="topright", col=c("blue","orange"), lwd=2, 
         legend=c(expression(paste(y[0]," Non-parametric")),
                  expression(paste(y[0]," Gaussian"))))