mixOmics (version 6.3.2)

cimDiablo: Clustered Image Maps (CIMs) ("heat maps") for DIABLO

Description

This function generates color-coded Clustered Image Maps (CIMs) ("heat maps") to represent "high-dimensional" data sets analysed with DIABLO.

Usage

cimDiablo(object,
color = NULL,
color.Y,
color.blocks,
comp = NULL,
margins = c(2, 15),
legend.position = "topright",
transpose = FALSE,
row.names = TRUE,
col.names = TRUE,
size.legend = 1.5)

Arguments

object

An object of class inheriting from "block.splsda".

color

a character vector of colors such as that generated by terrain.colors, topo.colors, rainbow, color.jet or similar functions.

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

comp

positive integer. The similarity matrix is computed based on the variables selected on those specified components. See example. Defaults to comp = 1.

margins

numeric vector of length two containing the margins (see par(mar)) for column and row names respectively.

legend.position

position of the legend, one of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center".

transpose

logical indicating if the matrix should be transposed for plotting. Defaults to FALSE.

row.names, col.names

logical, should the name of rows and/or columns of mat be shown? If TRUE (defaults) rownames(mat) and/or colnames(mat) are used. Possible character vectors with row and/or column labels can be used.

size.legend

size of the legend

Details

This function is a small wrapper of link{cim} specific to the DIABLO framework.

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

Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceeding of the National Academy of Sciences of the USA 95, 14863-14868.

Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace Jr., A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L. V., Anderson, N. L., Buolamwini, J. K., van Osdol, W. W., Monks, A. P., Scudiero, D. A., Sausville, E. A., Zaharevitz, D. W., Bunow, B., Viswanadhan, V. N., Johnson, G. S., Wittes, R. E. and Paull, K. D. (1997). An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343-349.

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).

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

See Also

cim, heatmap, hclust, plotVar, network and

http://mixomics.org/mixDIABLO/ for more details on all options available.

Examples

# NOT RUN {
## default method: shows cross correlation between 2 data sets
#------------------------------------------------------------------
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 <- block.splsda(X = data,
Y = Y,
design = design,
keepX = list(gene = c(10,10), lipid = c(15,15)),
ncomp = 2,
scheme = "centroid")

cimDiablo(nutrimouse.sgccda)

# }