examples/plotVar-examples.R

In mixOmicsTeam/mixOmics: Omics Data Integration Project

## variable representation for objects of class 'rcc'
# ----------------------------------------------------
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
nutri.res <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)

plotVar(nutri.res) #(default)


plotVar(nutri.res, comp = c(1,3), cutoff = 0.5)

\dontrun{
## variable representation for objects of class 'pls' or 'spls'
# ----------------------------------------------------
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50),
keepY = c(10, 10, 10))

plotVar(toxicity.spls, cex = c(1,0.8))

# with a customized legend
plotVar(toxicity.spls, legend = c("block 1", "my block 2"),
legend.title="my legend")

## variable representation for objects of class 'splsda'
# ----------------------------------------------------

data(liver.toxicity)
X <- liver.toxicity$gene
Y <- as.factor(liver.toxicity$treatment[, 4])

ncomp <- 2
keepX <- rep(20, ncomp)

splsda.liver <- splsda(X, Y, ncomp = ncomp, keepX = keepX)
plotVar(splsda.liver)

## variable representation for objects of class 'sgcca' (or 'rgcca')
# ----------------------------------------------------
## see example in ??wrapper.sgcca
data(nutrimouse)
# need to unmap the Y factor diet
Y = unmap(nutrimouse$diet)
# set up the data as list
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)

# set up the design matrix:
# with this design, gene expression and lipids are connected to the diet factor
# design = matrix(c(0,0,1,
#                   0,0,1,
#                   1,1,0), ncol = 3, nrow = 3, byrow = TRUE)

# with this design, gene expression and lipids are connected to the diet factor
# and gene expression and lipids are also connected
design = matrix(c(0,1,1,
1,0,1,
1,1,0), ncol = 3, nrow = 3, byrow = TRUE)


#note: the penalty parameters will need to be tuned
wrap.result.sgcca = wrapper.sgcca(X = data, design = design, penalty = c(.3,.3, 1),
ncomp = 2,
scheme = "centroid")
wrap.result.sgcca

#variables selected on component 1 for each block
selectVar(wrap.result.sgcca, comp = 1, block = c(1,2))$'gene'$name
selectVar(wrap.result.sgcca, comp = 1, block = c(1,2))$'lipid'$name

#variables selected on component 2 for each block
selectVar(wrap.result.sgcca, comp = 2, block = c(1,2))$'gene'$name
selectVar(wrap.result.sgcca, comp = 2, block = c(1,2))$'lipid'$name

plotVar(wrap.result.sgcca, comp = c(1,2), block = c(1,2), comp.select = c(1,1),
title = c('Variables selected on component 1 only'))


plotVar(wrap.result.sgcca, comp = c(1,2), block = c(1,2), comp.select = c(2,2),
title = c('Variables selected on component 2 only'))

# -> this one shows the variables selected on both components
plotVar(wrap.result.sgcca, comp = c(1,2), block = c(1,2),
title = c('Variables selected on components 1 and 2'))

## variable representation for objects of class 'rgcca'
# ----------------------------------------------------

data(nutrimouse)
# need to unmap Y for an unsupervised analysis, where Y is included as a data block in data
Y = unmap(nutrimouse$diet)

data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
# with this design, all blocks are connected
design = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3,
byrow = TRUE, dimnames = list(names(data), names(data)))

nutrimouse.rgcca <- wrapper.rgcca(X = data,
design = design,
tau = "optimal",
ncomp = 2,
scheme = "centroid")

plotVar(nutrimouse.rgcca, comp = c(1,2), block = c(1,2), cex = c(1.5, 1.5))


plotVar(nutrimouse.rgcca, comp = c(1,2), block = c(1,2))


# set up the data as list
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y =Y)
# with this design, gene expression and lipids are connected to the diet factor
# design = matrix(c(0,0,1,
#                   0,0,1,
#                   1,1,0), ncol = 3, nrow = 3, byrow = TRUE)

# with this design, gene expression and lipids are connected to the diet factor
# and gene expression and lipids are also connected
design = matrix(c(0,1,1,
1,0,1,
1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
#note: the tau parameter is the regularization parameter
wrap.result.rgcca = wrapper.rgcca(X = data, design = design, tau = c(1, 1, 0),
ncomp = 2,
scheme = "centroid")
#wrap.result.rgcca
plotVar(wrap.result.rgcca, comp = c(1,2), block = c(1,2))

}