`select.features.Rd`

Select features using supervised or unsupervised kernel method. A
supervised feature selection method is performed if `Y`

is provided.

- X
a numeric matrix (or data frame) used to select variables.

`NA`

s not allowed.- Y
a numeric matrix (or data frame) used to select variables.

`NA`

s not allowed.- kx.func
the kernel function name to use on

`X`

. Widely used kernel functions are pre-implemented, and can be directly used by setting`kx.func`

to one of the following values:`"linear"`

,`"gaussian.radial.basis"`

or`"bray"`

. Default:`"linear"`

. If`Y`

is provided, the kernel`"bray"`

is not allowed.- ky.func
the kernel function name to use on

`Y`

. Available kernels are:`"linear"`

, and`"gaussian.radial.basis"`

. Default:`"linear"`

. This value is ignored when`Y`

is not provided.- keepX
the number of variables to select.

- method
the method to use. Either an unsupervised variable selection method (

`"kernel"`

), a kernel PCA oriented variable selection method (`"kpca"`

) or a structure driven variable selection selection (`"graph"`

). Default:`"kernel"`

.- lambda
the penalization parameter that controls the trade-off between the minimization of the distorsion and the sparsity of the solution parameter.

- n_components
how many principal components should be used with method

`"kpca"`

. Required with method`"kpca"`

. Default:`2`

.- Lg
the Laplacian matrix of the graph representing relations between the input dataset variables. Required with method

`"graph"`

.- mu
the penalization parameter that controls the trade-off between the the distorsion and the influence of the graph. Default:

`1`

.- max_iter
the maximum number of iterations. Default:

`100`

.- nstep
the number of values used for the regularization path. Default:

`50`

.- ...
the kernel function arguments. In particular

`sigma`

(`"gaussian.radial.basis"`

): double. The inverse kernel width used by`"gaussian.radial.basis"`

.

`ukfs`

returns a vector of sorted selected features indexes.

Brouard C., Mariette J., Flamary R. and Vialaneix N. (2022). Feature selection
for kernel methods in systems biology. *NAR Genomics and Bioinformatics*,
*Forthcoming*.

```
## These examples require the installation of python modules
## See installation instruction at: http://mixkernel.clementine.wf
data("Koren.16S")
if (FALSE) {
sf.res <- select.features(Koren.16S$data.raw, kx.func = "bray", lambda = 1,
keepX = 40, nstep = 1)
colnames(Koren.16S$data.raw)[sf.res]
}
data("nutrimouse")
if (FALSE) {
grb.func <- "gaussian.radial.basis"
genes <- center.scale(nutrimouse$gene)
lipids <- center.scale(nutrimouse$lipid)
sf.res <- select.features(genes, lipids, kx.func = grb.func, ky.func = grb.func,
keepX = 40)
colnames(nutrimouse$gene)[sf.res]
}
```