circosPlot: circosPlot for DIABLO
In mixOmics: Omics Data Integration Project

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

Displays variable correlation among different blocks

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

`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`	Logical. Whether the legend should be added. Default is TRUE.
`linkWidth`	Numeric. Specifies the range of sizes used for lines linking the correlated variables (see details). Must be of length 2 or 1. Default to c(1). See details.
`...`	Advanced plot parameters: var.adj Numeric. Adjusts the radial location of variable names in units of the arc band width. Positive values push feature names radially outward. Default to -0.33. block.labels.adj Numeric between -1 and 1. Adjusts the radial location of block names radially inward or outward. Default to 0.

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.

The linkWidth argument specifies the width of the links drawn. If a vector of length 2 is provided, the smaller value will correspond to a similarity values designated by cutoff argument, while the larger value will be used for a link with perfect similarity (1), if any.

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)).

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

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).

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

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)
## links widths based on strength of their similarity
circosPlot(nutrimouse.sgccda, cutoff = 0.7, linkWidth = c(1, 10))
## custom legend
circosPlot(nutrimouse.sgccda, cutoff = 0.7, size.legend = 1.1)

## Not run: 
## more customisation
circosPlot(nutrimouse.sgccda, cutoff = 0.7, 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, size.legend = 1.1)
## also show intra-block correlations
circosPlot(nutrimouse.sgccda, cutoff = 0.7,
           size.legend = 1.1, showIntraLinks = TRUE)
## show lines
circosPlot(nutrimouse.sgccda, cutoff = 0.7, line = TRUE, ncol.legend = 1,
           size.legend = 1.1, showIntraLinks = TRUE)
## custom line legends
circosPlot(nutrimouse.sgccda, cutoff = 0.7, line = TRUE, ncol.legend = 2,
           size.legend = 1.1, showIntraLinks = TRUE)


## adjust feature and block names radially
circosPlot(nutrimouse.sgccda, cutoff = 0.7, size.legend = 1.1)
circosPlot(nutrimouse.sgccda, cutoff = 0.7, size.legend = 1.1, var.adj = 0.8, block.labels.adj = -0.5)

## End(Not run)