Description Usage Arguments Details Value Objects from the Class Slots Extends Methods Author(s) References See Also Examples
An implementation of the method of Dudoit and colleagues to apply the WestfallYoung adjustment to pvalues to control the familywise error rate when analyzing microarray data.
1 2 3 4 5 6 7 8 9  Dudoit(data, classes, nPerm=1000, verbose=TRUE)
## S4 method for signature 'Dudoit,missing'
plot(x, y, xlab='TStatistic', ylab='PValue', ...)
## S4 method for signature 'Dudoit'
cutoffSignificant(object, alpha, ...)
## S4 method for signature 'Dudoit'
selectSignificant(object, alpha, ...)
## S4 method for signature 'Dudoit'
countSignificant(object, alpha, ...)

data 
either a data frame or matrix with numeric values, or an

classes 
If 
nPerm 
integer scalar specifying the number of permutations to perform 
verbose 
logical scalar. If 
object 
object of class 
alpha 
numeric scalar specifying the target familywise error rate 
x 
object of class 
y 
Nothing, since it is supposed to be missing. Changes to the Rd processor require documenting the missing entry. 
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 
In 2002, Dudoit and colleagues introduced a method to adjust the pvalues when performing genebygene tests for differential expression. The adjustment was based on the method of Westfall and Young, with the goal of controlling the familywise error rate.
The standard method for plot
returns what you would expect.
The cutoffSignificant
method returns a real number (its input
value alpha
). The selectSignificant
method returns a
vector of logical values identifying the significant test results, and
countSignificant
returns an integer counting the number of
significant test results.
As usual, objects can be created by new
, but better methods are
available in the form of the Dudoit
function. The basic
inputs to this function are the same as those used for rowbyrow
statistical tests throughout the ClassComparison package; a detailed
description can be found in the MultiTtest
class.
The additional input determines the number, nPerm
, of
permutations to perform. The accuracy of the pvalue adjustment
depends on this value. Since the implementation is in R (and does not
call out to something compiled like C or FORTRAN), however, the
computations are slow. The default value of 1000 can take a long
time with modern microarrays that contain 40,000 spots.
adjusted.p
:numeric vector of adjusted pvalues.
t.statistics
:Object of class numeric
containing the computed tstatistics.
p.values
:Object of class numeric
containing
the computed pvalues.
groups
:Object of class character
containing
the names of the classes being compared.
call
:Object of class call
containing the
function call that created the object.
Class MultiTtest
, directly. In particular, objects of this class
inherit methods for summary
, hist
, and plot
from
the base class.
Determine cutoffs on
the adjusted pvalues at the desired significance level. In other
words, this function simply returns alpha
.
Compute a logical vector for selecting significant test results.
Count the number of significant test results.
signature(x=Dudoit, y=missing)
: ...
Kevin R. Coombes krc@silicovore.com
Dudoit S, Yang YH, Callow MJ, Speed TP.
Statistical Methods for Identifying Differentially Expressed Genes in
Replicated cDNA Microarray Experiments.
Statistica Sinica (2002), 12(1): 111139.
Westfall PH, Young SS.
Resamplingbased multiple testing: examples and methods for pvalue
adjustment.
Wiley series in probability and mathematics statistics.
John Wiley and Sons, 1993.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  showClass("Dudoit")
ng < 10000
ns < 15
nd < 200
fake.class < factor(rep(c('A', 'B'), each=ns))
fake.data < matrix(rnorm(ng*ns*2), nrow=ng, ncol=2*ns)
fake.data[1:nd, 1:ns] < fake.data[1:nd, 1:ns] + 2
fake.data[(nd+1):(2*nd), 1:ns] < fake.data[(nd+1):(2*nd), 1:ns]  2
# the permutation test is slow. it really needs many more
# than 10 permutations, but this is just an example...
dud < Dudoit(fake.data, fake.class, nPerm=10)
summary(dud)
plot(dud)
countSignificant(dud, 0.05)

Loading required package: oompaBase
Class "Dudoit" [package "ClassComparison"]
Slots:
Name: adjusted.p t.statistics p.values df groups
Class: numeric numeric numeric numeric character
Name: call
Class: call
Extends: "MultiTtest"
1.2.3.4.5.6.7.8.9.10.
Rowbyrow twosample ttests with 10000 rows
Positive sign indicates an increase in class: A
Call: Dudoit(data = fake.data, classes = fake.class, nPerm = 10)
Tstatistics:
Min. 1st Qu. Median Mean 3rd Qu. Max.
9.47743 0.74137 0.02788 0.01781 0.69399 9.64569
Pvalues:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.2166 0.4782 0.4764 0.7298 0.9999
[1] 228
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.