globaltest {globaltest} | R Documentation |
In microarray data, tests a (list of) group(s) of genes for significant association with a given clinical variable.
globaltest(X, Y, test.genes = NULL, model = NULL, levels = NULL, adjust = NULL, permutation = FALSE, nperm = NULL, sampling = FALSE, ndraws = NULL, verbose = TRUE)
X |
Either a matrix of gene expression data, where columns correspond to
samples and rows to genes or a Bioconductor exprSet . The data
should be properly normalized beforehand (and log- or otherwise
transformed), but missing values are allowed (coded as
NA ). Gene and sample names can be included as the row and
column names of X . |
Y |
A vector with the clinical outcome of interest, having one value
for each sample. If X is an exprSet it can
also be the name of a covariate in the phenoData slot of the exprSet |
test.genes |
Either a vector or a list of vectors. Indicates
the group(s) of genes to be tested. Each vector in
test.genes can be given in three formats. Either it can be
a vector with 1 (TRUE ) or 0 (FALSE ) for each gene
in X , with 1 indicating that the gene belongs to the
group. Or it can be a vector containing the column numbers (in
X ) of the genes belonging to the group. Or it can be a
subset of the rownames or geneNames for X . |
model |
Indicates the model the test uses:
Use model = "logistic" for a two-valued outcome Y
(the default) or model = "linear" for a continuous
outcome. If model is not supplied, globaltest will try to
determine the model from Y . |
levels |
If Y is a factor (or a category in the PhenoData slot of X )
and contains more than 2 levels: levels is a vector of levels of Y to test. If
levels is length 2: test these 2 groups against each other.
If levels is length 1: test that level against the others. |
adjust |
Confounders or risk factors for which the test must
be adjusted. Must be either a data frame or the names of
covariates in the phenoData slot of the exprSet X |
permutation |
A logical flag. If TRUE nperm
permutations are used to calculate the p-value instead of the
asymptotic formulas. Recommended for small sample size. Not
possible if an adjusted globaltest is used. |
nperm |
The number permutations used. The default is 10,000.
If a number is specified for nperm , permutation is
automatically set to TRUE . |
sampling |
A logical flag. If TRUE ndraws
random sets of genes are drawn with the same number of genes as
the tested group. Using this draws, an extra column of output
comparative.p is generated, reporting how many of these
random sets have a lower p-value than the tested group. |
ndraws |
The number of random groups of genes to be drawn.
The default is 1,000. If a number is specified for ndraws ,
sampling is automatically set to TRUE . |
verbose |
Prints some progress information if set to TRUE |
.
The Global Test tests whether a group of genes (of any size from one single gene to all genes on the array) is significantly associated with a clinical outcome. The group could be for example a known pathway, an area on the genome or the set of all genes. The test investigates whether samples with similar clinical outcomes tend to have similar gene expression patterns. For a significant result it is not necessary that the genes in the group have similar expression patterns, only that many of them are correlated with the outcome.
The function returns an object of class
gt.result-class
.
If the number of rows of a matrix X
does not match
the length of the vector Y
, but the number of columns
does, the matrix X
given is tacitly replaced by
t(X)
to make X
and Y
match. A warning is
printed if X
is square.
Jelle Goeman: j.j.goeman@lumc.nl; Jan Oosting
J. J. Goeman, S. A. van de Geer, F. de Kort and J. C. van Houwelingen, 2004, A global test for groups of genes: testing association with a clinical outcome, Bioinformatics 20 (1) 93–99. See also the vignette Globaltest.pdf included with this package.
geneplot
, sampleplot
,
permutations
, checkerboard
,
regressionplot
.
data(exampleX) # Expression data (40 samples; 1000 genes) data(exampleY) # Clinical outcome for the 40 samples pathway1 <- 1:25 # A pathway contains genes 1 to 25 pathway2 <- 26:50 # another pathway gt <- globaltest(exampleX, exampleY, list(pathway1,pathway2)) gt