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, model, levels, d, event = 1, adjust, ...)

`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, or a `formula` object using
these names. If the clinical outcome is survival, `Y` should
contain the survival times. |

`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` |
Globaltest will try to determine the correct model from
the input of `Y` and `d` . To override the automatic choice, use
`model = "logistic"` for a two-valued outcome `Y` ,
`model = "linear"` for a continuous outcome and
`model = "survival"` for a survival outcome. |

`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. |

`d` |
A vector or the name of a covariate in the phenoData slot
of the exprSet `X` , to indicate which samples experienced an event.
Providing a value for `d` automatically sets `model = "survival"` |

`event` |
The value or values of `d` that indicates that
there was an event. |

`adjust` |
Confounders or risk factors for which the test must
be adjusted. Must be either a data frame or (if `X` is an
exprSet) the names of covariates in the phenoData slot `X`
or a `formula` object using these
names. Default: no adjustment. |

`...` |
Captures deprecated input for compatibility with older versions of globaltest. |

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 variable. 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`

.

1. The options globaltest options sampling and permutation
have been replaced by separate functions from version 3.0. See
`sampling`

and `permutations`

.

2. The scaling of the test statistic Q is arbitrary and does not influence the test result. The scaling used in the software slightly different from the unscaled version presented in the paper. The mechanism of the rescaling is as follows: Remember that Q is a average of a Qi for all genes i in the tested geneset, so EQ is the average of the EQi. The rescaling is such that the EQ for the pathway of all genes (i.e. the mean of all EQi) is 10.

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`

,
`sampling`

, `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

[Package *globaltest* version 3.2.0 Index]