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

cimDiablo {mixOmics} | R Documentation

## 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.

### Author(s)

Amrit Singh, Florian Rohart

### References

Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and Lê 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.

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)
    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)

[Package mixOmics version 6.3.2 Index]