twoGaussiansNull {Ringo} | R Documentation |
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.
twoGaussiansNull(x, p.adj.method = "BY", max.adj.p = 0.1, var.equal = FALSE, ...)
x |
numeric vector of data values |
p.adj.method |
method for adjusting the p-values for multiple
testing; must be one of |
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 |
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.
Single numeric value. The threshold that is the minimal data value with an adjusted p-value smaller than a specified threshold.
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.
Joern Toedling, Aleksandra Pekowska
mclust
, p.adjust
,
upperBoundNull
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"))))