Run Data Fusion

Run Data Fusion.

RunFuseNet(
  object,
  n_iters = 100,
  ratio = 0.05,
  pca_dims = 0,
  k = 100,
  t = 0,
  norm_type = c("l1", "l2"),
  return_perturb_mat = FALSE,
  n_cores = NULL,
  ...
)

Arguments

object

A FuseNet object.

n_iters

Number of bootstrapping iterations. Default is 100.

ratio

Fraction of features to be downsampled in the original data matrix. Default is 0.05 aka 5%.

pca_dims

Number of principle components. Default is 0 and PCA is not run.

k

Number of nearest neighbors used. Default is 100.

t

Matrix power used for the distance matrix. Default is 0 and powering is not performed.

norm_type

Type of norm used:

• l1, L1-like norm. See details L1Norm.

• l2, L1-like norm. See details L2Norm.

return_perturb_mat

Whether to return the perturb matrix. Default is FALSE.

n_cores

Number of cores used. Default is to use all existing cores. See details makeCluster.

...

Additional parameters pass to makeCluster.

Value

Returns a FuseNet object.

Examples

{
object <- InitiateFuseNet(t(iris[,1:4]), project_name = "FuseNet", k = 3)
object <- RunFuseNet(object, n_iters = 1, k = 10, ratio = 0.5, n_cores = 1)
}
#> Initiate FuseNet
#> Normalize Data
#> Find Nearest Neighbors
#> Matrix Power
#> Finalize
#> Run Bootstrapping
#> Finalize