Description Usage Arguments Details Value Creating Objects Slots Methods Author(s) References See Also Examples
The MultiWilcoxonTest
class is used to perform rowbyrow Wilcoxon
ranksum tests on a data matrix. Significance cutoffs are determined by the
empirical Bayes method of Efron and Tibshirani.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  MultiWilcoxonTest(data, classes, histsize=NULL)
## S4 method for signature 'MultiWilcoxonTest'
summary(object, prior=1, significance=0.9, ...)
## S4 method for signature 'MultiWilcoxonTest'
hist(x, xlab='Rank Sum',
ylab='Prob(Different  Y)', main='', ...)
## S4 method for signature 'MultiWilcoxonTest,missing'
plot(x, prior=1, significance=0.9,
ylim=c(0.5, 1), xlab='Rank Sum', ylab='Prob(Different  Y)', ...)
## S4 method for signature 'MultiWilcoxonTest'
cutoffSignificant(object, prior, significance, ...)
## S4 method for signature 'MultiWilcoxonTest'
selectSignificant(object, prior, significance, ...)
## S4 method for signature 'MultiWilcoxonTest'
countSignificant(object, prior, significance, ...)

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

classes 
If 
histsize 
An integer; the number of bins used for the histogram
summarizing the Wilcoxon statistics. When 
object 
an object of the 
x 
an object of the 
xlab 
character string specifying label for the x axis 
ylab 
character string specifying label for the y axis 
ylim 
Plotting limits on the yaxis 
main 
character string specifying graph title 
prior 
Prior probability that an arbitrary gene is not differentially expressed, or that an arbitrary row does not yield a significant Wilcoxon ranksum statistic. 
significance 
Desired level of posterior probability 
... 
extra arguments for generic or plotting routines 
See the paper by Efron and Tibshirani.
The standard methods summary
, hist
, and plot
return what you would expect.
The cutoffSignificant
method returns a list of two
integers. Ranksum values smaller than the first value or larger than
the second value are statistically significant in the sense that their
posterior probability exceeds the specified significance
level
given the assumptions about the prior
probability of not being
significant.
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 MultiWilcoxonTest
function. The
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 constructor computes rowbyrow Wilcoxon ranksum statistics on
the input data
, comparing the two groups defined by the
classes
argument. It also estimates the observed and
theoretical (expected) density functions for the collection of
ranksum statistics.
The additional input argument, histsize
is usually best left to
its default value. In certain pathological cases, we have found it
necessary to use fewer bins; one suspects that the underlying model
does not adequately capture the complexity of those situations.
rank.sum.statistics
:numeric vector containing the computed ranksum statistics.
xvals
:numeric vector, best thought of as the vector of possible ranksum statistics given the sizes of the two groups.
theoretical.pdf
:numeric vector containing the
theoretical density function evaluated at the points of
xvals
.
pdf
:numeric vector containing the empirical density
function computed at the points of xvals
.
unravel
:numeric vector containing a smoothed
estimate (by Poisson regression using Bsplines) of the empirical
density function evaluated at xvals
.
groups
:A vector containing the names of the groups
defined by classes
.
call
:object of class call
representing the
function call that created the object.
Write out a summary of the object. For a given value of the
prior
probability of not being differentially expressed and
a given significance cutoff on the posterior probability, reports
the cutoffs and number of items in both tails of the distribution.
Plot a histogram of the ranksum statistics, with overlaid curves
representing the expected and observed distributions. Colors of
the curves are controlled by
oompaColor$EXPECTED
and
oompaColor$OBSERVED
.
Plots the posterior probability of being differentially expressed
for given values of the prior
. Horizontal lines are added
at each specified significance
level for the posterior
probability.
Determine cutoffs on the ranksum statistic at the desired significance level.
Compute a logical vector for selecting significant test results.
Count the number of significant test results.
Kevin R. Coombes krc@silicovore.com
Efron B, Tibshirani R.
Empirical bayes methods and false discovery rates for microarrays.
Genet Epidemiol 2002, 23: 7086.
Pounds S, Morris SW.
Estimating the occurrence of false positives and false negatives in
microarray studies by approximating and partitioning the empirical
distribution of pvalues.
Bioinformatics. 2003 Jul 1;19(10):123642.
Implementation is handled in part by the functions dwil
and rankSum
. The empirical Bayes results for
alternative tests (such as MultiTtest
) can be obtained
using the betauniform mixture model in the Bum
class.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  showClass("MultiWilcoxonTest")
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
a < MultiWilcoxonTest(fake.data, fake.class)
hist(a)
plot(a)
plot(a, prior=0.85)
abline(h=0)
cutoffSignificant(a, prior=0.85, signif=0.95)
countSignificant(a, prior=0.85, signif=0.95)

Loading required package: oompaBase
Class "MultiWilcoxonTest" [package "ClassComparison"]
Slots:
Name: xvals rank.sum.statistics pdf
Class: numeric numeric numeric
Name: theoretical.pdf unravel groups
Class: numeric numeric character
Name: call
Class: call
Warning message:
In hist.default(wilstats, breaks = histbreaks, plot = FALSE, probability = TRUE) :
argument 'probability' is not made use of
$low
[1] 148
$high
[1] 318
[1] 325
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