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

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

View source: R/cimDiablo.R

Description

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

Usage

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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

...

Other valid arguments passed to cim.

Details

This function is a small wrapper of link{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

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## 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)

mixOmics documentation built on Nov. 8, 2020, 11:12 p.m.