scDHA.vis: scDHA visulization

In scDHA: Single-Cell Decomposition using Hierarchical Autoencoder

scDHA visulization

Description

Generating 2D embeded data for visulation.

Usage

scDHA.vis(sc = sc, method = "UMAP", ncores = 10L, seed = NULL)

Arguments

sc	Embedding object produced by the scDHA function.
method	Visualization method to use. It can be "UMAP" or "scDHA". The default setting is "UMAP".
ncores	Number of processor cores to use.
seed	Seed for reproducibility.

Value

a list with the following keys:

• pred - A matrix representing the 2D projection of single-cell data, where rows represent samples and columns represent latent components.

Examples

library(scDHA)
#Load example data (Goolam dataset)
data('Goolam'); data <- t(Goolam$data); label <- as.character(Goolam$label)
#Log transform the data 
data <- log2(data + 1)
if(torch::torch_is_installed()) #scDHA need libtorch installed
{
  #Generate clustering result, the input matrix has rows as samples and columns as genes
  result <- scDHA(data, ncores = 2, seed = 1)
  #Generate 2D representation, the input is the output from scDHA function
  result <- scDHA.vis(result, ncores = 2, seed = 1)
  #Plot the representation of the dataset, different colors represent different cell types
  plot(result$pred, col=factor(label), xlab = "scDHA1", ylab = "scDHA2")
}

scDHA documentation built on April 4, 2023, 5:11 p.m.