examples/plotIndiv-examples.R

In mixOmicsTeam/mixOmics: Omics Data Integration Project

## plot of individuals 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)

# default, panel plot for X and Y subspaces
plotIndiv(nutri.res)


\dontrun{

# ellipse with respect to genotype in the XY space,
# names also indicate genotype
plotIndiv(nutri.res, rep.space= 'XY-variate',
ellipse = TRUE, ellipse.level = 0.9,
group = nutrimouse$genotype, ind.names = nutrimouse$genotype)

# ellipse with respect to genotype in the XY space, with legend
plotIndiv(nutri.res, rep.space= 'XY-variate', group = nutrimouse$genotype,
legend = TRUE)


# lattice style
plotIndiv(nutri.res, rep.space= 'XY-variate', group = nutrimouse$genotype,
legend = TRUE, style = 'lattice')

# classic style, in the Y space
plotIndiv(nutri.res, rep.space= 'Y-variate', group = nutrimouse$genotype,
legend = TRUE, style = 'graphics')

## plot of individuals 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))

#default
plotIndiv(toxicity.spls)


# two layers legend: a first grouping with Time.Group and 'group'
# and a second with Dose.Group and 'pch'
plotIndiv(toxicity.spls, rep.space="X-variate", ind.name = FALSE,
group = liver.toxicity$treatment[, 'Time.Group'], # first factor
pch = as.numeric(factor(liver.toxicity$treatment$Dose.Group)), #second factor
pch.levels =liver.toxicity$treatment$Dose.Group, 
legend = TRUE)



# indicating the centroid
plotIndiv(toxicity.spls, rep.space= 'X-variate', ind.names = FALSE,
group = liver.toxicity$treatment[, 'Time.Group'], centroid = TRUE)

# indicating the star and centroid
plotIndiv(toxicity.spls, rep.space= 'X-variate', ind.names = FALSE,
group = liver.toxicity$treatment[, 'Time.Group'], centroid = TRUE, star = TRUE)


# indicating the star and ellipse
plotIndiv(toxicity.spls, rep.space= 'X-variate', ind.names = FALSE,
group = liver.toxicity$treatment[, 'Time.Group'], centroid = TRUE,
star = TRUE, ellipse = TRUE)



# in the Y space, colors indicate time of necropsy, text is the dose
plotIndiv(toxicity.spls, rep.space= 'Y-variate',
group = liver.toxicity$treatment[, 'Time.Group'],
ind.names = liver.toxicity$treatment[, 'Dose.Group'],
legend = TRUE)


## plot of individuals for objects of class 'plsda' or 'splsda'
# ----------------------------------------------------
data(breast.tumors)
X <- breast.tumors$gene.exp
Y <- breast.tumors$sample$treatment

splsda.breast <- splsda(X, Y,keepX=c(10,10),ncomp=2)

# default option: note the outcome color is included by default!
plotIndiv(splsda.breast)

# also check ?background.predict for to visualise the prediction
# area with a plsda or splsda object!



# default option with no ind name: pch and color are set automatically
plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2))

# default option with no ind name: pch and color are set automatically, 
# with legend
plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2), legend = TRUE)

# trying the different styles
plotIndiv(splsda.breast, ind.names = TRUE, comp = c(1, 2),
ellipse = TRUE, style = "ggplot2", cex = c(1, 1))
plotIndiv(splsda.breast, ind.names = TRUE, comp = c(1, 2),
ellipse = TRUE, style = "lattice", cex = c(1, 1))

# changing pch of the two groups
plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2),
pch = c(15,16), legend = TRUE)

# creating a second grouping factor with a pch of length 3,
#  which is recycled to obtain a vector of length n
plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2),
pch = c(15,16,17), legend = TRUE)

#same thing as
pch.indiv = c(rep(15:17,15), 15, 16) # length n
plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2),
pch = pch.indiv, legend = TRUE)

# change the names of the second legend with pch.levels
plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2),
pch = 15:17, pch.levels = c("a","b","c"),legend = TRUE)


## plot of individuals for objects of class 'mint.plsda' or 'mint.splsda'
# ----------------------------------------------------
data(stemcells)
res = mint.splsda(X = stemcells$gene, Y = stemcells$celltype, ncomp = 2, 
                  keepX = c(10, 5), study = stemcells$study)

plotIndiv(res)


#plot study-specific outputs for all studies
plotIndiv(res, study = "all.partial")

#plot study-specific outputs for study "2"
plotIndiv(res, study = "2")


## variable representation for objects of class 'sgcca' (or 'rgcca')
# ----------------------------------------------------

data(nutrimouse)
Y = unmap(nutrimouse$diet)
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
design1 = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
nutrimouse.sgcca <- wrapper.sgcca(X = data,
design = design1,
penalty = c(0.3, 0.5, 1),
ncomp = 3,
scheme = "horst")

# default style: one panel for each block
plotIndiv(nutrimouse.sgcca)

# for the block 'lipid' with ellipse plots and legend, different styles
plotIndiv(nutrimouse.sgcca, group = nutrimouse$diet, legend =TRUE,
ellipse = TRUE, ellipse.level = 0.5, blocks = "lipid", title = 'my plot')
plotIndiv(nutrimouse.sgcca, style = "lattice", group = nutrimouse$diet,
legend = TRUE, ellipse = TRUE, ellipse.level = 0.5, blocks = "lipid",
title = 'my plot')
plotIndiv(nutrimouse.sgcca, style = "graphics", group = nutrimouse$diet,
legend = TRUE, ellipse = TRUE, ellipse.level = 0.5, blocks = "lipid",
title = 'my plot')


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

# Note: the code differs from above as we use a 'supervised' GCCA analysis
data(nutrimouse)
Y = nutrimouse$diet
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid)
design1 = matrix(c(0,1,0,1), ncol = 2, nrow = 2, byrow = TRUE)

nutrimouse.sgccda1 <- wrapper.sgccda(X = data,
Y = Y,
design = design1,
ncomp = 2,
keepX = list(gene = c(10,10), lipid = c(15,15)),
scheme = "centroid")


# plotIndiv
# ----------

# displaying all blocks. bu default colors correspond to outcome Y
plotIndiv(nutrimouse.sgccda1)


# displaying only 2 blocks
plotIndiv(nutrimouse.sgccda1, blocks = c(1,2), group = nutrimouse$diet)

# include the average plot (average the components across datasets)
plotIndiv(nutrimouse.sgccda1, blocks = "average", group = nutrimouse$diet)

# include the weighted average plot (average of components weighted by 
# correlation of each dataset with Y)
plotIndiv(
    nutrimouse.sgccda1,
    blocks = c("average", "weighted.average"),
    group = nutrimouse$diet
)

# with some ellipse, legend and title
plotIndiv(nutrimouse.sgccda1, blocks = c(1,2), group = nutrimouse$diet,
ellipse = TRUE, legend = TRUE, title = 'my sample plot')

}