tuneCluster.block.spls {timeOmics} | R Documentation

## Feature Selection Optimization for block (s)PLS method

### Description

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.

### Usage

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

### Arguments

`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`)

### 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

`block.spls`, `getCluster`, `plotLong`

### Examples

    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)

[Package timeOmics version 1.4.0 Index]