Preliminary analysis of one group of samples for use in
the SmoothTtest class. A key feature is the standard
quality control plot.
SingleGroup(avg, sd, span=0.5, name='')
# S4 method for SingleGroup
as.data.frame(x, row.names=NULL, optional=FALSE)
# S4 method for SingleGroup
summary(object, ...)
# S4 method for SingleGroup
print(x, ...)
# S4 method for SingleGroup
show(object)
# S4 method for SingleGroup,missing
plot(x, multiple=3, ccl=0, main=x@name,
xlab='Mean', ylab='Std Dev', xlim=0, ylim=0, ...)
numeric vector of mean values
numeric vector of standard deviations
parameter is passed onto loess
character string specifying the name of this object
object of class SingleGroup
object of class SingleGroup
numeric scalar specifying the multiple of the smoothed standard deviation to call significant
list containing objects of the
ColorCoding class. If left at its default
value of zero, colors are chosen automatically.
character string specifying plot title
character string specifying label for the x axis
character string specifying label for the y axis
Plotting limits for the x axis. If left at the default value of zero, then the limits are automatically generated
Plotting limits for the y axis. If left at the default value of zero, then the limits are automatically generated
See the base version of as.data.frame.default
See the base version of as.data.frame.default
extra arguments for generic or plotting routines
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.
name:character string specifying the name of this object
avg:numeric vector of mean values
sd:numeric vector of standard deviations
span:parameter used in the loess function
to fit sd as a function of avg.
fit:list containing components x and
y resulting from the loess fit
score:numeric vector specifying the ratio of the pointwise standard deviations to their smooth (loess) estimates
Combine the slots containing numeric vectors into a data frame, suitable for printing or exporting.
Write out a summary of the object.
Print the entire object. You never want to do this.
Print the entire object. You never want to do this.
Produce a scatter plot of the standard
deviations (x@sd) as a function of the means (x@avg).
The appropriate multiple of the loess fit is overlaid, and
points that exceed this multiple are flagged in a different
color. Colors in the plot are controlled by the current values of
oompaColor$CENTRAL.LINE,
oompaColor$CONFIDENCE.CURVE,
oompaColor$BORING,
oompaColor$BAD.REPLICATE, and
oompaColor$WORST.REPLICATE.
Kevin R. Coombes krc@silicovore.com
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.
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.
SmoothTtest
showClass("SingleGroup")
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)
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