mixOmics (version 6.3.2)

plotDiablo: Graphical output for the DIABLO framework

Description

Function to visualise correlation between components from different data sets

Usage

plotDiablo(x, ncomp = 1, legend = TRUE, legend.ncol, ...)

Arguments

x

object of class inheriting from "block.splsda".

ncomp

Which component to plot calculated from each data set. Has to be lower than the minimum of object$ncomp

legend

boolean. Whether the legend should be added. Default is TRUE.

legend.ncol

Number of columns for the legend. Default to min(5,nlevels(x$Y))

…

not used

Details

The function uses a plot.data.frame to plot the component ncomp calculated from each data set to visualise whether DIABLO (block.splsda) is successful at maximising the correlation between each data sets' component. The lower triangular panel indicated the Pearson's correlation coefficient, the upper triangular panel the scatter plot.

References

Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and L<U+00EA> Cao K.A. (2016). DIABLO - multi omics integration for biomarker discovery. Submitted.

See Also

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

Examples

# NOT RUN {
data('breast.TCGA')
Y = breast.TCGA$data.train$subtype

data = list(mrna =  breast.TCGA$data.train$mrna,
mirna =  breast.TCGA$data.train$mirna, prot =  breast.TCGA$data.train$protein)

# set number of component per data set
ncomp = 3
# set number of variables to select, per component and per data set (arbitrarily set)
list.keepX = list(mrna = rep(20, 3), mirna = rep(10,3), prot = rep(10,3))

# set up a full design where every block is connected 
design = matrix(1, ncol = length(data), nrow = length(data),
dimnames = list(names(data), names(data)))
diag(design) =  0
design  

BC.diablo = block.splsda(X = data, Y = Y, ncomp = ncomp, keepX = list.keepX, design = design)
plotDiablo(BC.diablo, ncomp = 1)
# }