circosPlot: circosPlot for DIABLO

In mixOmics: Omics Data Integration Project

Description

Displays variable correlation among different blocks

Usage

circosPlot(object,
comp = 1 : min(object$ncomp),
cutoff,
color.Y,
color.blocks,
color.cor,
var.names = NULL,
showIntraLinks = FALSE,
line = TRUE,
size.legend = 0.8,
ncol.legend = 1,
size.variables = 0.25,
size.labels = 1,
legend = TRUE)

Arguments

object	An object of class inheriting from "block.splsda".
comp	Numeric vector indicating which component to plot. Default to all
cutoff	Only shows links with a correlation higher than cutoff
color.Y	a character vector of colors to be used for the levels of the outcome
color.blocks	a character vector of colors to be used for the blocks
color.cor	a character vector of two colors. First one is for the negative correlation, second one is for the positive correlation
var.names	Optional parameter. A list of length the number of blocks in object$X, containing the names of the variables of each block. If NULL, the colnames of the data matrix are used.
showIntraLinks	if TRUE, shows the correlation higher than the threshold inside each block.
line	if TRUE, shows the overall expression of the selected variables. see examples.
size.legend	size of the legend
ncol.legend	number of columns for the legend
size.variables	size of the variable labels
size.labels	size of the block labels
legend	boolean. Whether the legend should be added. Default is TRUE.

Details

circosPlot function depicts correlations of variables selected with block.splsda among different blocks, using a generalisation of the method presented in González et al 2012. If ncomp is specified, then only the variables selected on that component are displayed.

Value

If saved in an object, the circos plot will output the similarity matrix and the names of the variables displayed on the plot (see attributes(object)).

Author(s)

Michael Vacher, Amrit Singh, Florian Rohart, Kim-Anh Lê Cao

References

Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and Lê Cao K.A. (2016). DIABLO: multi omics integration for biomarker discovery. BioRxiv available here: http://biorxiv.org/content/early/2016/08/03/067611

mixOmics article:

Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

González I., Lê Cao K.A., Davis M.J., Déjean S. (2012). Visualising associations between paired 'omics' data sets. BioData Mining; 5(1).

See Also

block.splsda, references and http://www.mixOmics.org/mixDIABLO for more details.

Examples

data(nutrimouse)
Y = nutrimouse$diet
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid)
design = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE)


nutrimouse.sgccda <- wrapper.sgccda(X=data,
Y = Y,
design = design,
keepX = list(gene=c(10,10), lipid=c(15,15)),
ncomp = 2,
scheme = "horst")

circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2, size.legend = 1.1)

circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2, size.legend = 1.1,
color.Y = 1:5, color.blocks = c("green","brown"), color.cor = c("magenta", "purple"))

par(mfrow=c(2,2))
circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2,
    size.legend = 1.1)
circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2,
    size.legend = 1.1, showIntraLinks = TRUE)
circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 1,
    size.legend = 1.1, showIntraLinks = TRUE)
circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2,
    size.legend = 1.1, showIntraLinks = TRUE, line = FALSE, size.variables = 0.5)

mixOmics documentation built on June 1, 2018, 5:06 p.m.