run.knetl: iCellR KNN Network

In iCellR: Analyzing High-Throughput Single Cell Sequencing Data

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

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".
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.

iCellR documentation built on Oct. 9, 2021, 5:07 p.m.