iCellR (version 1.5.1)

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",
  k = 400,
  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

x

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".

k

KNN 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.