InitiateFuseNet: Initialize FuseNet Object

In stevexniu/FuseNet: Non-Parametric Network Fusion

Initialize FuseNet Object

Description

Initialize a FuseNet object.

Usage

InitiateFuseNet(
  raw_data,
  project_name = "",
  normalization = c("cosine", "lognorm", "none"),
  normalize_factor = 10000,
  zero_percent = 0.7,
  pca_dims = 0,
  kernel = c("gaussian", "euclidean"),
  k = 100,
  t = 0,
  verbose = FALSE,
  seed = 1
)

Arguments

raw_data	Raw data. An N x M matrix with N rows of features and M columns of data points.
project_name	Project name. Default is none.
normalization	Normalization method used. Default is cosine. See details Normalization. cosine, cosine normalization: feature counts for each data point are divided by the L2 norm of them. lognorm, log normalization: feature counts for each data point are divided by the total sum of them. Then the data is multiplied by the scale.factor before taking a log-transformed by log(1+x). none, additional normalization is not performed.
normalize_factor	Normalization factor used with lognorm method. Default is 10000.
zero_percent	Zero-entry percentage threshold. If the number of zero entries in the returned matrices is above this number, a sparse matrix will be returned. Default is 0.7 aka 70%.
pca_dims	Number of dimensions used. Default is 0 and PCA is not performed.
kernel	Kernel distance used: gaussian, gaussian distance kernel. See details EuclideanDist. euclidean, euclidean distance kernel. See details GaussianDist.
k	Number of nearest neighbors. Default is 100. See details from nn2.
t	Matrix power used for the distance matrix. Default is 0 and powering is not performed. See MatrixPower for details.
verbose	Whether to display a process bar. Default is FALSE.
seed	Random seed number. Default is 1.

Value

Returns a FuseNet object.

Examples

{
object <- InitiateFuseNet(t(iris[,1:4]), project_name = "FuseNet", k = 10)
}

stevexniu/FuseNet documentation built on May 16, 2022, 12:23 p.m.