tuneCluster.spls: Feature Selection Optimization for sPLS method
In timeOmics: Time-Course Multi-Omics data integration

Description Usage Arguments Details Value See Also Examples

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.

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

`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`)

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.

`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

spls, getCluster, plotLong

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)