tuneCluster.block.spls: Feature Selection Optimization for block (s)PLS 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::block.spls by optimizing the silhouette coefficient, which assesses the quality of clustering.

tuneCluster.block.spls(
  X,
  Y = NULL,
  indY = NULL,
  ncomp = 2,
  test.list.keepX = NULL,
  test.keepY = NULL,
  ...
)

`X`	list of numeric matrix (or data.frame) with features in columns and samples in rows (with samples order matching in all data sets).
`Y`	(optional) numeric matrix (or data.frame) with features in columns and samples in rows (same rows as `X`).
`indY`	integer, to supply if Y is missing, indicates the position of the matrix response in the list `X`.
`ncomp`	integer, number of component to include in the model
`test.list.keepX`	list of integers with the same size as X. Each entry corresponds to the different keepX value to test for each block of `X`.
`test.keepY`	only if Y is provideid. 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::block.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

block.spls, getCluster, plotLong

demo <- suppressWarnings(get_demo_cluster())
X <- list(X = demo$X, Z = demo$Z)
Y <- demo$Y
test.list.keepX <- list("X" = c(5,10,15,20), "Z" = c(2,4,6,8))
test.keepY <- c(2:5)

# tuning
tune.block.spls <- tuneCluster.block.spls(X= X, Y= Y, 
                                          test.list.keepX= test.list.keepX, 
                                          test.keepY= test.keepY, 
                                          mode= "canonical")
keepX <- tune.block.spls$choice.keepX
keepY <- tune.block.spls$choice.keepY

# final model
block.spls.res <- mixOmics::block.spls(X= X, Y= Y, keepX = keepX, 
                             keepY = keepY, ncomp = 2, mode = "canonical")
# get clusters and plot longitudinal profile by cluster
block.spls.cluster <- getCluster(block.spls.res)