run.knetl: iCellR KNN Network

Description

This function takes an object of class iCellR and and runs kNet for dimensionality reduction.

Usage

run.knetl(
  x = NULL,
  dist.method = "euclidean",
  zoom = 300,
  data.type = "pca",
  dims = 1:20,
  joint = FALSE,
  col.by = "clusters",
  my.seed = 1,
  layout.2d = "layout_nicely",
  layout.3d = "layout_with_fr",
  add.3d = FALSE,
  dim.redux = "umap",
  do.redux = TRUE,
  run.iclust = FALSE,
  return.graph = FALSE
)

Arguments

An object of class iCellR.

dist.method

the distance measure to be used to compute the dissimilarity matrix. This must be one of: "euclidean", "maximum", "mandatattan", "canberra", "binary", "minkowski" or "NULL". By default, distance="euclidean". If the distance is "NULL", the dissimilarity matrix (diss) should be given by the user. If distance is not "NULL", the dissimilarity matrix should be "NULL".

zoom

Adjusting zoom the higher the number the less sensitivity, default = 400.

data.type

Choose between "tsne", "pca", "umap", default = "pca".

dims

PCA dimentions to be use for clustering, default = 1:20.

joint

Run in Combined or joint fashion as in CCCA and CPCA, default = FALSE.

col.by

If return.graph is TRUE the choose the cluster colors. Choose between "clusters", "conditions".

my.seed

seed number, default = 1.

layout.2d

Choose your 2D layout, default = "layout_nicely".

layout.3d

Choose your 3D layout, default = "layout_with_fr".

add.3d

Add 3D KNetL as well, default = FALSE.

dim.redux

Choose between "tsne", "pca", "umap" to unpack the nodes, default = "umap".

do.redux

Perform dim reudx for unpaking the nodes, default = TRUE.

run.iclust

Perform clustering as well (nor recomanded), default = FALSE.

return.graph

return igraph object, default = FALSE.

Value

An object of class iCellR.