circosPlot: circosPlot for DIABLO

In mixOmicsTeam/mixOmics: Omics Data Integration Project

circosPlot for DIABLO

Description

Displays variable correlation among different blocks

Usage

## S3 method for class 'block.splsda'
circosPlot(
  object,
  comp = 1:min(object$ncomp),
  cutoff,
  color.Y,
  blocks = NULL,
  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,
  legend.title = "Expression",
  linkWidth = 1,
  ...
)

## S3 method for class 'block.plsda'
circosPlot(
  object,
  comp = 1:min(object$ncomp),
  cutoff,
  color.Y,
  blocks = NULL,
  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,
  legend.title = "Expression",
  linkWidth = 1,
  ...
)

## S3 method for class 'block.spls'
circosPlot(object, ..., group = NULL, Y.name = "Y")

## S3 method for class 'block.pls'
circosPlot(object, ..., group = NULL, Y.name = "Y")

Arguments

object	An object of class inheriting from "block.plsda", "block.splsda", "block.pls" or "blocks.spls".
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
blocks	Character or integer vector indicating which blocks to show. Default to all
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.
legend.title	String. Name of the legend. Defaults to "Expression".
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.
...	For object of class block.splsda, 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. See examples. block.labels.adj Numeric between -1 and 1. Adjusts the radial location of block names radially inward or outward. Default to 0. See examples. blocks.link Character vector of blocks. If provided, only correlations from features of these blocks are shown using links. See examples. For object of class block.spls, all listed and advanced arguments passed to the block.splsda method.
group	The grouping factor used when line = TRUE
Y.name	Character, the name of the Y block

Details

circosPlot function depicts correlations of variables selected with block.splsda or block.spls 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.

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, Al J Abadi

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)
## 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)

## 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)
par(mfrow=c(1,1))

## 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)
## ---  example using breast.TCGA data
data("breast.TCGA")
data = list(mrna = breast.TCGA$data.train$mrna, 
            mirna = breast.TCGA$data.train$mirna, 
            protein = breast.TCGA$data.train$protein)
list.keepX = list(mrna = rep(20, 2), mirna = rep(10,2), protein = c(10, 2))

TCGA.block.splsda = block.splsda(X = data, 
                             Y =breast.TCGA$data.train$subtype,
                             ncomp = 2, keepX = list.keepX, 
                             design = 'full')
circosPlot(TCGA.block.splsda, cutoff = 0.7, line=TRUE)
## show only first 2 blocks
circosPlot(TCGA.block.splsda, cutoff = 0.7, line=TRUE, blocks = c(1,2))
## show only correlations including the mrna block features
circosPlot(TCGA.block.splsda, cutoff = 0.7, blocks.link = 'mrna')

data("breast.TCGA")
data = list(mrna = breast.TCGA$data.train$mrna, mirna = breast.TCGA$data.train$mirna)
list.keepX = list(mrna = rep(20, 2), mirna = rep(10,2))
list.keepY = c(rep(10, 2))

TCGA.block.spls = block.spls(X = data, 
                             Y = breast.TCGA$data.train$protein,
                             ncomp = 2, keepX = list.keepX, 
                             keepY = list.keepY, design = 'full')
circosPlot(TCGA.block.spls, group = breast.TCGA$data.train$subtype, cutoff = 0.7, 
           Y.name = 'protein')
## only show links including mrna
circosPlot(TCGA.block.spls, group = breast.TCGA$data.train$subtype, cutoff = 0.7, 
           Y.name = 'protein', blocks.link = 'mrna')

mixOmicsTeam/mixOmics documentation built on Nov. 4, 2024, 8:56 a.m.