run.knetl {iCellR} | R Documentation |
This function takes an object of class iCellR and and runs kNet for dimensionality reduction.
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 )
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. |
An object of class iCellR.