The SingleGroup Class

Description

Preliminary analysis of one group of samples for use in the SmoothTtest class. A key feature is the standard quality control plot.

Usage

SingleGroup(avg, sd, span = 0.5, name = '')
## S4 method for signature 'SingleGroup':
as.data.frame(x, row.names=NULL, optional=FALSE)
## S4 method for signature 'SingleGroup':
summary(object, ...)
## S4 method for signature 'SingleGroup':
print(x, ...)
## S4 method for signature 'SingleGroup, missing':
plot(x, multiple=3, ccl=0, main=x@name,
 xlab='Mean', ylab='Std Dev', xlim=0, ylim=0, ...)

Arguments

{The usual extra parameters to 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 strenght 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.

Creating Objects

Objects can be created by calls to the SingleGroup constructor. Users rarely have need to create these objects directly; they are usually created as a consequence of the construction of an object of the SmoothTtest class.

Slots

name:

A character string; the name of this object

avg:

The numeric vector of mean values

sd:

The numeric vector of standard deviations

span:

The span parameter used in the loess function to fit sd as a function of avg.

fit:

A list containing components x and y resulting from the loess fit.

score:

A numeric vector; the ratio of the pointwise standard deviations to their smooth (loess) estimates.

Methods

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

Combine the slots containing numeric vectors into a data frame, suitable for printing or exporting.

summary(object, ...)

Write out a summary of the object.

print(x, ...)

Print the entire object. You never want to do this.

plot(x, multiple=3, ccl=0, main=x@name, xlab='Mean', ylab='Std Dev', xlim=0, ylim=0, ...)

Produce a scatter plot of the standard deviations (x@sd) as a function of the means (x@avg). The appropriate mutliple of the loess fit is overlaid, and points that exceed this multiple are flaged in a different color. Colors in the plotare controlled by the current values of COLOR.CENTRAL.LINE, COLOR.CONFIDENCE.CURVE, COLOR.BORING, COLOR.BAD.REPLICATE, and COLOR.WORST.REPLICATE.

Author(s)

Kevin R. Coombes <kcoombes@mdanderson.org>

References

Baggerly, K.A., Coombes, K.R., Hess, K.R., Stivers, D.N., Abruzzo, L.V., Zhang, W. Identifying differentially expressed genes in cDNA microarray experiments. J Comp Biol. 8:639-659, 2001.

Coombes, K.R., Highsmith, W.E., Krogmann, T.A., Baggerly, K.A., Stivers, D.N., Abruzzo, L.V. Identifying and quantifying sources of variation in microarray data using high-density cDNA membrane arrays. J Comp Biol. 9:655-669, 2002.

See Also

SmoothTtest

Examples

m <- rnorm(1000, 8, 2.5)
v <- rnorm(1000, 0.7)
plot(m, v)

x <- SingleGroup(m, v, name='bogus')

summary(x)

plot(x)
plot(x, multiple=2)

# cleanup
rm(m, v, x)