plotIndiv

Individuals representation

In this plot the samples are represented as point placed according to their relation to two dimensions among those chosen in the analysis (the ncomp parameter). Such points tends to aggregate together when they are share similarities.

Usage in mixOmics

Individual 2D plots can be obtained in mixOmics via the function plotIndiv() as displayed below:

library(mixOmics)
data(nutrimouse)

diet = unmap(nutrimouse$diet)
blocks = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, diet = diet)

design = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE)


nutri.sgcca <- wrapper.sgcca(blocks,design=design, ncomp = 3)

plotIndiv(nutri.sgcca, 
          group = nutrimouse$diet, 
          ind.names = nutrimouse$diet,
          legend =TRUE, 
          ellipse = TRUE,
          ellipse.level = 0.5, 
          blocks = "lipid", 
          main = 'sgcca',
          star = TRUE, 
          centroid = TRUE)

X <- nutrimouse$lipid
Y <- nutrimouse$gene
nutri.res <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)

plotIndiv(nutri.res,
          group = nutrimouse$genotype,
          ind.names = FALSE,
          legend = TRUE)

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))
group=rep(c("group 1","group 2","group 3","group 4"),each = 16)
##test add.legend,group

plotIndiv(toxicity.spls, 
          legend=TRUE,
          ind.names=FALSE,
          group=group)

data("breast.tumors")
X = breast.tumors$gene.exp
Y = breast.tumors$sample$treatment
breast.plsda<- plsda(X,Y, ncomp=10)    
 ##plot.ellipse and ellipse.level

plotIndiv(breast.plsda,
          comp= c(1,2),
          ind.names = breast.tumors$sample$treatment,
          ellipse.level=0.5,
          ellipse=TRUE)

liver.spca= spca(liver.toxicity$gene,ncomp=3)

plotIndiv(liver.spca,
          ind.names=FALSE,
          legend=TRUE,
          ellipse=TRUE,
          group=liver.toxicity$treatment[, 3],
          style="graphics") 

### Multilevel
data(vac18)
X <- vac18$genes
Y <- vac18$stimulation

# sample indicates the repeated measurements
# setup the design matrix by indicating the repeated measurements
design <- data.frame(sample = vac18$sample)

# multilevel sPLS-DA model
vac18.splsda.multilevel <- splsda(X, 
                                  Y = vac18$stimulation, 
                                  multilevel = design, 
                                  ncomp = 3, 
                                  keepX = c(30, 137, 123))

plotIndiv(vac18.splsda.multilevel, 
          rep.space = 'XY-variate',
          ind.names = vac18$stimulation,
          pch = 20,
          main = 'Both Gene expression and Stimulation subspaces',
          legend = TRUE)

plotIndiv(nutri.sgcca, 
          group = nutrimouse$diet, 
          ind.names = nutrimouse$diet,
          legend =TRUE, 
          ellipse = TRUE,
          ellipse.level = 0.5, 
          blocks = "lipid", 
          main = '3D-Nutrimouse',
          star = TRUE, 
          centroid = TRUE,
          style='3d')