mixOmics (version 6.3.2)

circosPlot: circosPlot for DIABLO

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.

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)).

Details

circosPlot function depicts correlations of variables selected with block.splsda among different blocks, using a generalisation of the method presented in Gonz<U+00E1>lez et al 2012. If ncomp is specified, then only the variables selected on that component are displayed.

References

Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and L<U+00EA> 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, L<U+00EA> Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

Gonz<U+00E1>lez I., L<U+00EA> Cao K.A., Davis M.J., D<U+00E9>jean 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

# NOT RUN {
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)

# }