plot.sgccda: Graphical output for the DIABLO framework

In ajabadi/mixOmics2: Omics Data Integration Project

Description

Function to visualise correlation between components from different data sets

Usage

## S3 method for class 'sgccda'
plot(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.

Value

none

Author(s)

Amrit Singh

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. Submitted.

See Also

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

Examples

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)

ajabadi/mixOmics2 documentation built on Aug. 9, 2019, 1:08 a.m.