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

In mixOmicsTeam/mixOmics: Omics Data Integration Project

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,
  trim = TRUE,
  ...
)

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
trim	(Logical or numeric) If FALSE, values are not changed. If TRUE, the values are trimmed to 3 standard deviation range. If a numeric, values with absolute values greater than the provided values are trimmed.
...	Other valid arguments passed to cim.

Details

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

Value

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.

Author(s)

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

References

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

See Also

cim, heatmap, hclust, plotVar, network and

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

Examples

## default method: shows cross correlation between 2 data sets
#------------------------------------------------------------------
data(nutrimouse)
Y = nutrimouse$diet
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid)

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

cimDiablo(nutrimouse.sgccda, comp = c(1,2))
## change trim range
cimDiablo(nutrimouse.sgccda, comp = c(1,2), trim = 4)
## do not trim values
cimDiablo(nutrimouse.sgccda, comp = c(1,2), trim = FALSE)

mixOmicsTeam/mixOmics documentation built on Oct. 26, 2023, 6:48 a.m.