R: circosPlot for DIABLO

circosPlot {mixOmics} | R Documentation

## circosPlot for DIABLO

### Description

Displays variable correlation among different blocks

### Usage

    circosPlot(object,
    comp = 1 : min(object$ncomp),
    cutoff,
    color.Y,
    color.blocks,
    color.cor,
    var.names = NULL,
    showIntraLinks = FALSE,
    line = TRUE,
    size.legend = 0.8,
    ncol.legend = 1,
    size.variables = 0.25,
    size.labels = 1,
    legend = TRUE)

### Arguments

`object` | An object of class inheriting from `"block.splsda"`.
`comp` | Numeric vector indicating which component to plot. Default to all
`cutoff` | Only shows links with a correlation higher than `cutoff`
`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
`color.cor` | a character vector of two colors. First one is for the negative correlation, second one is for the positive correlation
`var.names` | Optional parameter. A list of length the number of blocks in `object$X`, containing the names of the variables of each block. If `NULL`, the colnames of the data matrix are used.
`showIntraLinks` | if TRUE, shows the correlation higher than the threshold inside each block.
`line` | if TRUE, shows the overall expression of the selected variables. see examples.
`size.legend` | size of the legend
`ncol.legend` | number of columns for the legend
`size.variables` | size of the variable labels
`size.labels` | size of the block labels
`legend` | boolean. Whether the legend should be added. Default is TRUE.

### Details

`circosPlot` function depicts correlations of variables selected with `block.splsda` among different blocks, using a generalisation of the method presented in González et al 2012. If `ncomp` is specified, then only the variables selected on that component are displayed.

### Value

If saved in an object, the circos plot will output the similarity matrix and the names of the variables displayed on the plot (see `attributes(object)`).

### Author(s)

Michael Vacher, Amrit Singh, Florian Rohart, Kim-Anh Lê Cao

### 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

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

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

### See Also

`block.splsda`, references and http://www.mixOmics.org/mixDIABLO for more details.

### Examples

    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 <- wrapper.sgccda(X=data,
    Y = Y,
    design = design,
    keepX = list(gene=c(10,10), lipid=c(15,15)),
    ncomp = 2,
    scheme = "horst")

    circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2, size.legend = 1.1)

    circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2, size.legend = 1.1,
    color.Y = 1:5, color.blocks = c("green","brown"), color.cor = c("magenta", "purple"))

    par(mfrow=c(2,2))
    circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2,
        size.legend = 1.1)
    circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2,
        size.legend = 1.1, showIntraLinks = TRUE)
    circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 1,
        size.legend = 1.1, showIntraLinks = TRUE)
    circosPlot(nutrimouse.sgccda, cutoff = 0.7, ncol.legend = 2,
        size.legend = 1.1, showIntraLinks = TRUE, line = FALSE, size.variables = 0.5)

[Package mixOmics version 6.3.2 Index]