Plot of Individuals (Experimental Units)

Description

This function provides scatter plots for individuals (experimental units) representation in (sparse)(I)PCA, (regularized)CCA, (sparse)PLS(DA) and (sparse)(R)GCCA(DA).

Usage

## S3 method for class 'mixo_pls'
plotIndiv(object, comp = NULL, rep.space = NULL, ind.names = TRUE, group, col.per.group,
style = "ggplot2", ellipse = FALSE, ellipse.level = 0.95, centroid = FALSE, star = FALSE,
title = NULL, subtitle, legend = FALSE,
X.label = NULL, Y.label = NULL, Z.label = NULL, abline = FALSE,
xlim = NULL, ylim = NULL, col, cex, pch, pch.levels, alpha = 0.2, axes.box = "box",
layout = NULL,
size.title = rel(2), size.subtitle = rel(1.5), size.xlabel = rel(1),
size.ylabel = rel(1), size.axis = rel(0.8), size.legend = rel(1),
size.legend.title = rel(1.1), legend.title = "Legend", legend.title.pch = "Legend",
legend.position = "right",
point.lwd = 1, background = NULL, ... )

## S3 method for class 'mint.spls'
plotIndiv(object, comp = NULL, study = "global", rep.space = NULL, group, col.per.group,
style = "ggplot2", ellipse = FALSE, ellipse.level = 0.95, centroid = FALSE, star = FALSE,
title = NULL, subtitle, legend=FALSE,
X.label = NULL, Y.label = NULL, abline = FALSE,
xlim = NULL, ylim = NULL, col, cex, pch, layout = NULL,
size.title = rel(2), size.subtitle = rel(1.5), size.xlabel = rel(1),
size.ylabel = rel(1), size.axis = rel(0.8), size.legend = rel(1),
size.legend.title = rel(1.1), legend.title = "Legend", legend.position = "right",
point.lwd = 1, ... )

## S3 method for class 'sgcca'
plotIndiv(object, comp = NULL, blocks = NULL, ind.names = TRUE, group, col.per.group,
style = "ggplot2", ellipse = FALSE, ellipse.level = 0.95, centroid = FALSE, star = FALSE,
title = NULL, subtitle, legend = FALSE,
X.label = NULL, Y.label = NULL, Z.label = NULL, abline = FALSE,
xlim = NULL, ylim = NULL, col, cex, pch, pch.levels,
alpha = 0.2, axes.box = "box", layout = NULL,
size.title = rel(2), size.subtitle = rel(1.5), size.xlabel = rel(1),
size.ylabel = rel(1), size.axis = rel(0.8), size.legend = rel(1),
size.legend.title = rel(1.1), legend.title = "Legend", legend.title.pch = "Legend",
legend.position = "right",
point.lwd = 1, ... )

Arguments

Details

plotIndiv method makes scatter plot for individuals representation depending on the subspace of projection. Each point corresponds to an individual.

If ind.names=TRUE and row names is NULL, then ind.names=1:n, where n is the number of individuals. Also, if pch is an input, then ind.names is set to FALSE as we do not show both names and shapes.

plotIndiv can have a two layers legend. This is especially convenient when you have two grouping factors, such as a gender effect and a study effect, and you want to highlight both simulatenously on the graphical output. A first layer is coded by the group factor, the second by the pch argument. When pch is missing, a single layer legend is shown. If the group factor is missing, the col argument is used to create the grouping factor group. When a second grouping factor is needed and added via pch, pch needs to be a vector of length the number of samples. In the case where pch is a vector or length the number of groups, then we consider that the user wants a different pch for each level of group. This leads to a single layer legend and we merge col and pch. In the similar case where pch is a single value, then this value is used to represent all samples. See examples below for object of class plsda and splsda.

In the specific case of a single 'omics supervised model (plsda, splsda), users can overlay prediction results to sample plots in order to visualise the prediction areas of each class, via the background input parameter. Note that this functionality is only available for models with less than 2 components as the surfaces obtained for higher order components cannot be projected onto a 2D representation in a meaningful way. For more details, see background.predict

For customized plots (i.e. adding points, text), use the style = 'graphics' (default is ggplot2).

Note: the ellipse options were borrowed from the ellipse.

Value

none

Author(s)

Ignacio González, Benoit Gautier, Francois Bartolo, Florian Rohart

See Also

text, background.predict, points and http://mixOmics.org/graphics for more details.

Examples

## 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)


## Not run: 

# 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, #levels of the second factor, for the legend
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 consensus plot (average the components across datasets)
plotIndiv(nutrimouse.sgccda1, blocks = "consensus", group = nutrimouse$diet)

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


## End(Not run)