GlobalAncova (version 3.36.0)

Plot.subjects: Subjects Plot for GlobalAncova

Description

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.

Usage

"Plot.subjects"(xx, formula.full, formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...)
"Plot.subjects"(xx, formula.full,formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...)
"Plot.subjects"(xx, formula.full, formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...)

Arguments

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.

test.genes

Vector of gene names or gene indices specifying the gene set. If missing, the plot refers to all genes in xx.

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"').

returnValues

Shall bar heights (subject-wise reduction in sum of squares) be returned?

bar.names

Vector of bar labels. If missing, column names of xx are taken.

...

Graphical parameters for specifying colors, titles etc.

Methods

xx = "matrix", formula.full = "formula", formula.red = "formula", model.dat = "ANY", group = "missing", covars = "missing", test.terms = "missing"

In this method, besides the expression matrix 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 = "matrix", formula.full = "formula", formula.red = "missing", model.dat = "ANY", group = "missing", covars = "missing", test.terms = "character"

In this method, besides the expression matrix 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 = "matrix", formula.full = "missing", formula.red = "missing", model.dat = "missing", group = "ANY", covars = "ANY", test.terms = "missing"

Besides the expression matrix 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.

See Also

GlobalAncova, Plot.genes, Plot.sequential

Examples

data(vantVeer)
data(phenodata)
data(pathways)

Plot.subjects(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases")
Plot.subjects(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases")
Plot.subjects(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes = pathways[[3]])

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