tuneCluster.spls: Feature Selection Optimization for sPLS method

In abodein/timeOmics_BioC: Time-Course Multi-Omics data integration

Feature Selection Optimization for sPLS method

Description

This function identify the number of feautures to keep per component and thus by cluster in mixOmics::spls by optimizing the silhouette coefficient, which assesses the quality of clustering.

Usage

tuneCluster.spls(
  X,
  Y,
  ncomp = 2,
  test.keepX = rep(ncol(X), ncomp),
  test.keepY = rep(ncol(Y), ncomp),
  ...
)

Arguments

X	numeric matrix (or data.frame) with features in columns and samples in rows
Y	numeric matrix (or data.frame) with features in columns and samples in rows (same rows as X)
ncomp	integer, number of component to include in the model
test.keepX	vector of integer containing the different value of keepX to test for block X.
test.keepY	vector of integer containing the different value of keepY to test for block Y.
...	other parameters to be included in the spls model (see mixOmics::spls)

Details

For each component and for each keepX/keepY value, a spls is done from these parameters. Then the clustering is performed and the silhouette coefficient is calculated for this clustering.

We then calculate "slopes" where keepX/keepY are the coordinates and the silhouette is the intensity. A z-score is assigned to each slope. We then identify the most significant slope which indicates a drop in the silhouette coefficient and thus a deterioration of the clustering.

Value

silhouette	silhouette coef. computed for every combinasion of keepX/keepY
ncomp	number of component included in the model
test.keepX	list of tested keepX
test.keepY	list of tested keepY
block	names of blocks
slopes	"slopes" computed from the silhouette coef. for each keepX and keepY, used to determine the best keepX and keepY
choice.keepX	best keepX for each component
choice.keepY	best keepY for each component

See Also

spls, getCluster, plotLong

Examples

demo <- suppressWarnings(get_demo_cluster())
X <- demo$X
Y <- demo$Y

# tuning
tune.spls <- tuneCluster.spls(X, Y, ncomp= 2, test.keepX= c(5,10,15,20), test.keepY= c(2,4,6))
keepX <- tune.spls$choice.keepX
keepY <- tune.spls$choice.keepY

# final model
spls.res <- mixOmics::spls(X, Y, ncomp= 2, keepX= keepX, keepY= keepY)

# get clusters and plot longitudinal profile by cluster
spls.cluster <- getCluster(spls.res)
plotLong(spls.res)

abodein/timeOmics_BioC documentation built on April 10, 2024, 10:01 a.m.