twoGaussiansNull {Ringo} | R Documentation

## 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

`x` | 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`

### 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.

### Value

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

### Note

Please note that the use of the package 'mclust' is only free for strict academic use (see the license of 'mclust' here: http://www.stat.washington.edu/mclust/license.txt ). The alternative function `upperBoundNull` does not have this restriction.

Thanks to Richard Bourgon for pointing out the necessity of providing this method as an alternative way of estimating the threshold.

### Author(s)

Joern Toedling, Aleksandra Pekowska

### See Also

`mclust`, `p.adjust`, `upperBoundNull`

### Examples

      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"))))

[Package Ringo version 1.44.0 Index]