Single-Cell Clustering using Autoencoder and Network Fusion

A single-cell Clustering method using 'Autoencoder' and Network fusion ('scCAN') Bang Tran (2022) for segregating the cells from the high-dimensional 'scRNA-Seq' data. The software automatically determines the optimal number of clusters and then partitions the cells in a way such that the results are robust to noise and dropouts. 'scCAN' is fast and it supports Windows, Linux, and Mac OS.

scCAN

scCAN is a R software package that can perform unsupervised clustering for single-cell RNA sequencing (scRNA-seq) data. scCAN overcomes the excessive noise level from scRNA-seq data by using autoencoders and accurately cluster the cells into correct cell types.

How to install:

• The package can be installed from this repository.
• Install devtools: utils::install.packages('devtools')
• Install the package using: devtools::install_github('bangtran365/scCAN') Or, install with manual and vignette: devtools::install_github('bangtran365/scCAN', build_manual = T, build_vignettes = T)

To run the sample example:

• Load the package: library(scCAN)
• Load SCE dataset: data('SCE'); data <- t(SCE$data); label <- as.character(SCE$cell_type1)
• Add log transformation: if(max(data)>100) data <- log2(data + 1)
• Generate clustering result, the input matrix has rows as samples and columns as genes: result <- scCAN(data, r.seed = 1)
• The clustering result can be found here: cluster <- result$cluster
• Calculating adjusted Rand Index: ari <- round(scCAN::adjustedRandIndex(cluster,label), 2)
• Print out ARI value: print(paste0("ARI = ", ari))