Description
Single-Cell Clustering using Autoencoder and Network Fusion.
Description
A single-cell Clustering method using 'Autoencoder' and Network fusion ('scCAN') Bang Tran (2022) <doi:10.1038/s41598-022-14218-6> 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.
README.md
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))