Description Usage Arguments Value Creating objects Slots Methods Author(s) See Also Examples
Class to perform rowbyrow ttests on microarray or proteomics data.
1 2 3 4 5 6 7 8 9 10 11  MultiTtest(data, classes, na.rm=TRUE)
## S4 method for signature 'MultiTtest'
summary(object, ...)
## S4 method for signature 'MultiTtest'
as.data.frame(x, row.names=NULL, optional=FALSE, ...)
## S4 method for signature 'MultiTtest'
hist(x, xlab='T Statistics', main=NULL, ...)
## S4 method for signature 'MultiTtest,missing'
plot(x, y, ylab='T Statistics', ...)
## S4 method for signature 'MultiTtest,ANY'
plot(x, y, xlab='T Statistics', ylab=deparse(substitute(y)), ...)

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

classes 
If 
na.rm 
logical scalar. If 
object 
object of class 
x 
object of class 
y 
numeric vector 
xlab 
character string specifying the label for the x axis 
ylab 
character string specifying the label for the y axis 
main 
character string specifying the plot title 
row.names 
see the base version 
optional 
see the base version 
... 
extra arguments for generic or plotting routines 
The graphical routines invisibly return the object against which they were invoked.
Although objects can be created using new
, the preferred method is
to use the MultiTtest
generator. In the simplest case, you
simply pass in a data matrix and a logical vector assigning classes to
the columns, and the constructor performs rowbyrow twosample
ttests and computes the associated (single test) pvalues. To adjust
for multiple testing, you can pass the pvalues on to the
Bum
class.
If you use a factor instead of a logical vector, then the ttest
compares the first level of the factor to everything else. To handle
the case of multiple classes, see the MultiLinearModel
class.
As with other class comparison functions that are part of the OOMPA,
we can also perform statistical tests on
ExpressionSet
objects from
the BioConductor libraries. In this case, we pass in an
ExpressionSet
object along with the name of a factor to use for
splitting the data.
t.statistics
:Object of class numeric
containing the computed tstatistics.
p.values
:Object of class numeric
containing
the computed pvalues.
df
:Numeric vector of the degrees of freedom per gene. Introduced to allow for missing data.
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.
Write out a summary of the object.
Produce a histogram of the tstatistics.
Produces a scatter plot of the tstatistics against their index.
Produces a scatter plot of the tstatistics in the
object x
against the numeric vector y
.
Kevin R. Coombes krc@silicovore.com
matrixT
,
Bum
,
Dudoit
,
MultiLinearModel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  showClass("MultiTtest")
ng < 10000
ns < 50
dat < matrix(rnorm(ng*ns), ncol=ns)
cla < factor(rep(c('A', 'B'), each=25))
res < MultiTtest(dat, cla)
summary(res)
hist(res, breaks=101)
plot(res)
plot(res, res@p.values)
hist(res@p.values, breaks=101)
dat[1,1] < NA
mm < matrixMean(dat, na.rm=TRUE)
vv < matrixVar(dat, mm, na.rm=TRUE)
tt < matrixT(dat, cla, na.rm=TRUE)
mtt < MultiTtest(dat,cla)

Loading required package: oompaBase
Class "MultiTtest" [package "ClassComparison"]
Slots:
Name: t.statistics p.values df groups call
Class: numeric numeric numeric character call
Known Subclasses: "Dudoit", "MultiTtestPaired", "MultiTtestUnequal"
Rowbyrow twosample ttests with 10000 rows
Positive sign indicates an increase in class: A
Call: MultiTtest(data = dat, classes = cla)
Tstatistics:
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.044723 0.690665 0.006479 0.001559 0.684854 3.666768
Pvalues:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0001892 0.2460818 0.4956192 0.4972294 0.7507269 0.9999624
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