cc08-0-SingleGroup-class: Class "SingleGroup"

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

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'
show(object)
## 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

avg	numeric vector of mean values
sd	numeric vector of standard deviations
span	parameter is passed onto loess
name	character string specifying the name of this object
object	object of class SingleGroup
x	object of class SingleGroup
multiple	numeric scalar specifying the multiple of the smoothed standard deviation to call significant
ccl	list containing objects of the ColorCoding class. If left at its default value of zero, colors are chosen automatically.
main	character string specifying plot title
xlab	character string specifying label for the x axis
ylab	character string specifying label for the y axis
xlim	Plotting limits for the x axis. If left at the default value of zero, then the limits are automatically generated
ylim	Plotting limits for the y axis. If left at the default value of zero, then the limits are automatically generated
row.names	See the base version of as.data.frame.default
optional	See the base version of as.data.frame.default
...	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.

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:

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

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.

show(object)

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 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.

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.

See Also

SmoothTtest

Examples

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)

Example output

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

Slots:
                                                                  
Name:      score       fit       avg        sd      span      name
Class:   numeric      list   numeric   numeric   numeric character
Name: bogus 
      avg                sd               score              fit.x        
 Min.   :-0.3744   Min.   :-3.05533   Min.   :0.000899   Min.   :-0.3744  
 1st Qu.: 6.2709   1st Qu.:-0.05016   1st Qu.:0.584048   1st Qu.: 6.2709  
 Median : 7.8207   Median : 0.69847   Median :1.277342   Median : 7.8207  
 Mean   : 7.8639   Mean   : 0.67477   Mean   :1.504064   Mean   : 7.8639  
 3rd Qu.: 9.4716   3rd Qu.: 1.37243   3rd Qu.:2.190395   3rd Qu.: 9.4716  
 Max.   :15.5032   Max.   : 4.19852   Max.   :6.213029   Max.   :15.5032  
     fit.y       
 Min.   :0.6120  
 1st Qu.:0.6485  
 Median :0.6695  
 Mean   :0.6620  
 3rd Qu.:0.6753  
 Max.   :0.6966  

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