MultiWilcoxonTest class is used to perform row-by-row Wilcoxon
rank-sum tests on a data matrix. Significance cutoffs are determined by the
empirical Bayes method of Efron and Tibshirani.
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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, ...)
either a data frame or matrix with numeric values, or an
An integer; the number of bins used for the histogram
summarizing the Wilcoxon statistics. When
an object of the
an object of the
character string specifying label for the x axis
character string specifying label for the y axis
Plotting limits on the y-axis
character string specifying graph title
Prior probability that an arbitrary gene is not differentially expressed, or that an arbitrary row does not yield a significant Wilcoxon rank-sum statistic.
Desired level of posterior probability
extra arguments for generic or plotting routines
See the paper by Efron and Tibshirani.
The standard methods
return what you would expect.
cutoffSignificant method returns a list of two
integers. Rank-sum 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
given the assumptions about the
prior probability of not being
selectSignificant method returns a vector of logical values
identifying the significant test results, and
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 row-by-row
statistical tests throughout the ClassComparison package; a detailed
description can be found in the
The constructor computes row-by-row Wilcoxon rank-sum statistics on
data, comparing the two groups defined by the
classes argument. It also estimates the observed and
theoretical (expected) density functions for the collection of
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.
numeric vector containing the computed rank-sum statistics.
numeric vector, best thought of as the vector of possible rank-sum statistics given the sizes of the two groups.
numeric vector containing the
theoretical density function evaluated at the points of
numeric vector containing the empirical density
function computed at the points of
numeric vector containing a smoothed
estimate (by Poisson regression using B-splines) of the empirical
density function evaluated at
A vector containing the names of the groups
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 rank-sum statistics, with overlaid curves
representing the expected and observed distributions. Colors of
the curves are controlled by
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
Determine cutoffs on the rank-sum 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 firstname.lastname@example.org
Efron B, Tibshirani R.
Empirical bayes methods and false discovery rates for microarrays.
Genet Epidemiol 2002, 23: 70-86.
Pounds S, Morris SW.
Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values.
Bioinformatics. 2003 Jul 1;19(10):1236-42.
Implementation is handled in part by the functions
rankSum. The empirical Bayes results for
alternative tests (such as
MultiTtest) can be obtained
using the beta-uniform mixture model in the
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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  148 $high  318  325
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