mixOmics: plotIndiv – R documentation

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plotIndiv

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

plotIndiv(object, ...)

## S3 method for class 'mint.pls'
plotIndiv(
  object,
  comp = NULL,
  study = "global",
  rep.space = c("X-variate", "XY-variate", "Y-variate", "multi"),
  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 'mint.spls'
plotIndiv(
  object,
  comp = NULL,
  study = "global",
  rep.space = c("X-variate", "XY-variate", "Y-variate", "multi"),
  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 'mint.plsda'
plotIndiv(
  object,
  comp = NULL,
  study = "global",
  rep.space = c("X-variate", "XY-variate", "Y-variate", "multi"),
  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 'mint.splsda'
plotIndiv(
  object,
  comp = NULL,
  study = "global",
  rep.space = c("X-variate", "XY-variate", "Y-variate", "multi"),
  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 'pca'
plotIndiv(
  object,
  comp = NULL,
  ind.names = TRUE,
  group,
  col.per.group,
  style = "ggplot2",
  ellipse = FALSE,
  ellipse.level = 0.95,
  centroid = FALSE,
  star = FALSE,
  title = NULL,
  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,
  ...
)

## 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 '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,
  ...
)

## S3 method for class 'rgcca'
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

`object`	object of class inherited from any mixOmics: `PLS, sPLS, PLS-DA, SPLS-DA, rCC, PCA, sPCA, IPCA, sIPCA, rGCCA, sGCCA, sGCCDA`
`...`	Optional arguments or type par can be added with `style = 'graphics'`
`comp`	integer vector of length two (or three to 3d). The components that will be used on the horizontal and the vertical axis respectively to project the individuals.
`study`	Indicates which study-specific outputs to plot. A character vector containing some levels of `object$study`, "all.partial" to plot all studies or "global" is expected. Default to "global".
`rep.space`	For objects of class `"pca", "plsda", "plsda"` default is `"X-variate"`. For the objects of class `"pls", "rcc"` default is a panel plot representing each data subspace. For objects of class `"rgcca"` and `"sgcca"`, numerical value(s) indicating the block data set to represent needs to be specified.
`group`	factor indicating the group membership for each sample, useful for ellipse plots. Coded as default for the supervised methods `PLS-DA, SPLS-DA,sGCCDA`, but needs to be input for the unsupervised methods `PCA, sPCA, IPCA, sIPCA, PLS, sPLS, rCC, rGCCA, sGCCA`
`col.per.group`	character (or symbol) color to be used when 'group' is defined. Vector of the same length as the number of groups.
`style`	argument to be set to either `'graphics'`, `'lattice'`, `'ggplot2'` or `'3d'` for a style of plotting. Default set to 'ggplot2'. See details. `3d` is not available for MINT objects.
`ellipse`	Logical indicating if ellipse plots should be plotted. In the non supervised objects `PCA, sPCA, IPCA, sIPCA, PLS, sPLS, rCC, rGCCA, sGCCA` ellipse plot is only be plotted if the argument `group` is provided. In the `PLS-DA, SPLS-DA,sGCCDA` supervised object, by default the ellipse will be plotted accoding to the outcome `Y`.
`ellipse.level`	Numerical value indicating the confidence level of ellipse being plotted when `ellipse =TRUE` (i.e. the size of the ellipse). The default is set to 0.95, for a 95% region.
`centroid`	Logical indicating whether centroid points should be plotted. In the non supervised objects `PCA, sPCA, IPCA, sIPCA, PLS, sPLS, rCC, rGCCA, sGCCA` the centroid will only be plotted if the argument `group` is provided. The centroid will be calculated based on the group categories. In the supervised objects `PLS-DA, SPLS-DA,sGCCDA` the centroid will be calculated according to the outcome `Y`.
`star`	Logical indicating whether a star plot should be plotted, with arrows starting from the centroid (see argument `centroid`, and ending for each sample belonging to each group or outcome. In the non supervised objects `PCA, sPCA, IPCA, sIPCA, PLS, sPLS, rCC, rGCCA, sGCCA` star plot is only be plotted if the argument `group` is provided. In the supervised objects `PLS-DA, SPLS-DA,sGCCDA` the star plot is plotted according to the outcome `Y`.
`title`	set of characters indicating the title plot.
`subtitle`	subtitle for each plot, only used when several `block` or `study` are plotted.
`legend`	Logical. Whether the legend should be added. Default is FALSE.
`X.label`	x axis titles.
`Y.label`	y axis titles.
`abline`	should the vertical and horizontal line through the center be plotted? Default set to `FALSE`
`xlim, ylim`	numeric list of vectors of length 2 and length =length(blocks), giving the x and y coordinates ranges.
`col`	character (or symbol) color to be used, possibly vector.
`cex`	numeric character (or symbol) expansion, possibly vector.
`pch`	plot character. A character string or a vector of single characters or integers. See `points` for all alternatives.
`layout`	layout parameter passed to mfrow. Only used when `study` is not "global"
`size.title`	size of the title
`size.subtitle`	size of the subtitle
`size.xlabel`	size of xlabel
`size.ylabel`	size of ylabel
`size.axis`	size of the axis
`size.legend`	size of the legend
`size.legend.title`	size of the legend title
`legend.title`	title of the legend
`legend.position`	position of the legend, one of "bottom", "left", "top" and "right".
`point.lwd`	`lwd` of the points, used when `ind.names = FALSE`
`ind.names`	either a character vector of names for the individuals to be plotted, or `FALSE` for no names. If `TRUE`, the row names of the first (or second) data matrix is used as names (see Details).
`Z.label`	z axis titles (when style = '3d').
`pch.levels`	Only used when `pch` is different from `col` or `col.per.group`, ie when `pch` creates a second factor. Only used for the legend.
`alpha`	Semi-transparent colors (0 < `'alpha'` < 1)
`axes.box`	for style '3d', argument to be set to either `'axes'`, `'box'`, `'bbox'` or `'all'`, defining the shape of the box.
`legend.title.pch`	title of the second legend created by `pch`, if any.
`background`	color the background by the predicted class, see `background.predict`
`blocks`	integer value or name(s) of block(s) to be plotted using the GCCA module. "average" and "weighted.average" will create average and weighted average plots, respectively. See details and examples.

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

The argument block = 'average' averages the components from all blocks to produce a consensus plot. The argument block='weighted.average' is a weighted average of the components according to their correlation with the outcome Y.

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, Kim-Anh Lê Cao, Al J Abadi

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, 
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')


## End(Not run)

mixOmics

Omics Data Integration Project

v6.14.1

GPL (>= 2)

Authors

Kim-Anh Le Cao [aut, cre], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al Abadi [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb]

Initial release

2021-04-11