There are three possible ways of using GlobalAncova
. The general way is to define
formulas for the full and reduced model, respectively, where the formula terms correspond
to variables in model.dat
.
An alternative is to specify the full model and the name of the model terms
that shall be tested regarding differential expression.
In order to make this layout compatible with the
function call in the first version of the package there is also a method where simply
a group variable (and possibly covariate information) has to be given. This is maybe
the easiest usage in cases where no 'special' effects like e.g. interactions are
of interest.
"GlobalAncova"(xx, formula.full, formula.red, model.dat, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50)
"GlobalAncova"(xx, formula.full, model.dat,test.terms, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50)
"GlobalAncova"(xx, group, covars = NULL, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50)
xx
."permutation"
) or by means of an approximation for a mixture of chi-square
distributions ("approx"
).
Both p-values are provided when specifying method = "both"
. With option "Fstat"
only the global F-statistics are
returned without p-values or further information.test.genes = NULL
a list with components
test.genes
a matrix is returned whose columns show the number of genes, value of the
F-statistic, theoretical p-value, permutation-based and/or asymptotic p-value for each of the gene sets.
xx
, model formulas for the full
and reduced model and a data frame model.dat
specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group
, covars
and test.terms
are '"missing"'
since they are not needed for this method.xx
, a model formula for the full
model and a data frame model.dat
specifying corresponding model
terms are required. The character argument test.terms
names the terms of interest
whose association with differential expression will be tested. The basic idea
behind this method is that one can select single terms, possibly from the list of
terms provided by previous GlobalAncova
output, and test them without having
to specify each time a model formula for the reduced model.
The arguments formula.red
, group
and covars
are '"missing"'
since they are not needed for this method.xx
a clinical variable group
is
required. Covariate adjustment is possible via the argument covars
but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full
, formula.red
, model.dat
and
test.terms
are '"missing"' since they are not needed for this method.Plot.genes
, Plot.subjects
, GlobalAncova.closed
, GAGO
, GlobalAncova.decomp
data(vantVeer)
data(phenodata)
data(pathways)
GlobalAncova(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes=pathways[1], method="both", perm = 100)
GlobalAncova(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes=pathways[1], method="both", perm = 100)
GlobalAncova(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes=pathways[1], method="both", perm = 100)
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