cc09-0-SmoothTtest-class: Class "SmoothTtest"

In ClassComparison: Classes and Methods for "Class Comparison" Problems on Microarrays

Description

Implements the smooth t-test for differential expression as developed by Baggerly and Coombes.

Usage

SmoothTtest(stats, aname='Group One', bname='Group Two',
 name=paste(aname, 'vs.', bname))
## S4 method for signature 'SmoothTtest'
as.data.frame(x, row.names=NULL, optional=FALSE)
## S4 method for signature 'SmoothTtest'
summary(object, ...)
## S4 method for signature 'SmoothTtest,missing'
plot(x, folddiff=3, goodflag=2, badch=4, ccl=0,
 name=x@name, pch='.', xlab='log intensity', ylab='log ratio', ...)

Arguments

stats	object of class TwoGroupStats
aname	character string specifying the name of the first group
bname	character string specifying the name of the second group
name	character string specifying the name of this object
object	object of class SmoothTtest
x	object of class SmoothTtest
row.names	See the base version of as.data.frame.default
optional	See the base version of as.data.frame.default
folddiff	numeric scalar specifying the level of fold difference considered large enough to be indicated in the plots
goodflag	numeric scalar specifying the level (in standard deviation units) of the smooth t-statistic considered large enough to be indicated in the plot
badch	numeric scalar specifying the level of variability in single groups considered large enough to be worrisome. See the multiple argument to the plot method in the SingleGroup class.
ccl	list containing objects of class ColorCoding. If left at its default value of zero, colors are chosen automatically.
pch	default plotting character
xlab	character string specifying label for the x axis
ylab	character string specifying label for the y axis
...	extra arguments for generic or plotting routines

Details

In 2001 and 2002, Baggerly and Coombes developed the smooth t-test for finding differentially expressed genes in microarray data. Along with many others, they began by log-transforming the data as a reasonable step in the direction of variance stabilization. They observed, however, that the gene-by-gene standard deviations still seemed to vary in a systematic way as a function of the mean log intensity. By borrowing strength across genes and using loess to fit the observed standard deviations as a function of the mean, one presumably got a better estimate of the true standard deviation.

These smooth estimates are computed for each of two groups of samples being compared. They are then combined (gene-by-gene using the usual univariate formulas) to compute pooled "smooth" estimates of the standard deviation. These smooth estimates are then used in gene-by-gene t-tests.

The interesting question then arises of how to compute and interpret p-values associated to these individual tests. The liberal argument asserts that, because smoothing uses data from hundreds of measurements to estimate the standard deviation, it can effectively be treated as "known" in the t-tests, which should thus be compared against the normal distribution. A conservative argument claims that the null distribution should still be the t-distribution with the degrees of freedom determined in the usual way by the number of samples. The truth probably lies somewhere in between, and is probably best approximated by some kind of permutation test. In this implementation, we take the coward's way out and don't provide any of those alternatives. You have to extract the t-statistics (from the smooth.t.statistics slot of the object) and compute your own p-values in your favorite way. If you base the computations on a theoretical model rather than a permutation test, then the Bum class provides a convenient way to account for multiple testing.

Creating Objects

In practice, users will first use a data frame and a classification vector (or an ExpressionSet) to construct an object of the TwoGroupStats object. This object can then be handed directly to the SmoothTtest function to perform the smooth t-test.

Slots

one:

object of class SingleGroup representing a loess smooth of standard deviation as a function of the mean in the first group of samples

two:

object of class SingleGroup representing a loess smooth of standard deviation as a function of the mean in the second group of samples

smooth.t.statistics:

numeric vector containing the smooth t-statistics

fit:

data.frame with two columns, x and y, containing the smooth estimates of the pooled standard deviation

dif:

numeric vector of the differences in mean values between the two groups

avg:

numeric vector of the overall mean value

aname:

character string specifying the name of the first group

bname:

character string specifying the name of the second group

name:

character string specifying the name of this object

stats:

object of class TwoGroupStats that was used to create this object

Methods

as.data.frame(x, row.names=NULL, optional=FALSE)

Convert the object into a data frame suitable for printing or exporting.

summary(object, ...)

Write out a summary of the object.

plot(x, folddiff=3, goodflag=2, badch=4, ccl=0, name=x@name, pch='.', xlab='log intensity', ylab='log ratio', ...)

Create a set of six plots. The first two plots are the QC plots from the SingleGroup objects representing the two groups of samples. The third plot is a scatter plot comparing the means in the two groups. The fourth plot is Bland-Altman plot of the overall mean against the difference in means (also known colloquially as an M-vs-A plot). The fifth plot is a histogram of the smooth t-statistics. The final plot is a scatter plot of the smooth t-statistics as a function of the mean intensity. Colors in the plots are controlled by the current values of oompaColor$BORING, oompaColor$SIGNIFICANT, oompaColor$BAD.REPLICATE, oompaColor$WORST.REPLICATE, oompaColor$FOLD.DIFFERENCE, oompaColor$CENTRAL.LINE, and oompaColor$CONFIDENCE.CURVE.

Author(s)

Kevin R. Coombes [e-mail]

References

Baggerly KA, Coombes KR, Hess KR, Stivers DN, Abruzzo LV, Zhang W.
Identifying differentially expressed genes in cDNA microarray experiments.
J Comp Biol. 8:639-659, 2001.

Coombes KR, Highsmith WE, Krogmann TA, Baggerly KA, Stivers DN, Abruzzo LV.
Identifying and quantifying sources of variation in microarray data using high-density cDNA membrane arrays.
J Comp Biol. 9:655-669, 2002.

Altman DG, Bland JM.
Measurement in Medicine: the Analysis of Method Comparison Studies.
The Statistician, 1983; 32: 307-317.

See Also

Bum, MultiTtest, SingleGroup, TwoGroupStats

Examples

showClass("SmoothTtest")
bogus <- matrix(rnorm(30*1000, 8, 3), ncol=30, nrow=1000)
splitter <- rep(FALSE, 30)
splitter[16:30] <- TRUE
x <- TwoGroupStats(bogus, splitter)
y <- SmoothTtest(x)

opar <- par(mfrow=c(2, 3), pch='.')
plot(y, badch=2, goodflag=1)
par(opar)

Example output

Loading required package: oompaBase
Class "SmoothTtest" [package "ClassComparison"]

Slots:
                                                                  
Name:                  one                 two smooth.t.statistics
Class:         SingleGroup         SingleGroup             numeric
                                                                  
Name:                  fit                 dif                 avg
Class:          data.frame             numeric             numeric
                                                                  
Name:                aname               bname                name
Class:           character           character           character
                          
Name:                stats
Class:       TwoGroupStats

ClassComparison documentation built on May 6, 2019, 5:02 p.m.