Plot.subjects {GlobalAncova} | R Documentation |
Produces a plot to show the influence of the samples on the test result produced by 'GlobalAncova'.
There are three possible ways of using GlobalAncova
.
Also Plot.subjects
can be invoked with these three alternatives.
## S4 method for signature 'matrix, formula, formula, ANY, ## missing, missing, missing': Plot.subjects(xx, formula.full, formula.red, model.dat, colorgroup = NULL, sort = FALSE, legendpos = "topright") ## S4 method for signature 'matrix, formula, missing, ANY, ## missing, missing, character': Plot.subjects(xx, formula.full, test.terms, model.dat, colorgroup = NULL, sort = FALSE, legendpos = "topright") ## S4 method for signature 'matrix, missing, missing, ## missing, numeric, ANY, missing': Plot.subjects(xx, group, covars = NULL, colorgroup = NULL, sort = FALSE, legendpos = "topright")
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of xx . |
formula.full |
Model formula for the full model. |
formula.red |
Model formula for the reduced model (that does not contain the terms of interest.) |
model.dat |
Data frame that contains all the variable information for each sample. |
group |
Vector with the group membership information. |
covars |
Vector or matrix which contains the covariate information for each sample. |
test.terms |
Character vector that contains names of the terms of interest. |
colorgroup |
Character variable giving the group that specifies coloring.
If the function is called using the argument group then this variable is assumed to
be relevant for coloring. |
sort |
Should the samples be ordered by colorgroup ? |
legendpos |
Position of the legend (a single keyword from the list '"bottomright"', '"bottom"', '"bottomleft"', '"left"', '"topleft"', '"top"', '"topright"', '"right"' and '"center"'). |
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 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.This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Reinhard Meister meister@tfh-berlin.de
Ulrich Mansmann mansmann@ibe.med.uni-muenchen.de
Manuela Hummel hummel@ibe.med.uni-muenchen.de
GlobalAncova
, Plot.genes
, GlobalAncova.closed
data(vantVeer) data(phenodata) Plot.subjects(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, colorgroup = "metastases") Plot.subjects(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, colorgroup = "metastases") Plot.subjects(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus)