cimDiablo: Clustered Image Maps (CIMs) ("heat maps") for DIABLO
In mixOmics: Omics Data Integration Project

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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,
  ...
)

`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
`...`	Other valid arguments passed to `cim`.

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

A list containing the following components:

`M`	the mapped matrix used by `cim`.
`rowInd, colInd`	row and column index permutation vectors as returned by `order.dendrogram`.
`ddr, ddc`	object of class `"dendrogram"` which describes the row and column trees produced by `cim`.
`mat.cor`	the correlation matrix used for the heatmap. Available only when mapping = "XY".
`row.names, col.names`	character vectors with row and column labels used.
`row.sideColors, col.sideColors`	character vector containing the color names for vertical and horizontal side bars used to annotate the rows and columns.

Amrit Singh, Florian Rohart, Kim-Anh Lê Cao, Al J Abadi

Singh A., Shannon C., Gautier B., Rohart F., Vacher M., Tebbutt S. and Lê Cao K.A. (2019), DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays, Bioinformatics, Volume 35, Issue 17, 1 September 2019, Pages 3055–3062.

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.

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

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

cim, heatmap, hclust, plotVar, network and

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

## 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)