scDHA

The scDHA software package can perform cell segregation through unsupervised learning, dimension reduction and visualization, cell classification, and time-trajectory inference on single-cell RNA sequencing data.

News

• The package is now available on CRAN.
• The machine learning framework is changed from Tensorflow to Torch. Torch can be called directly from R. Python environment is no longer required.

How to install:

• The package can be installed from this repository.
• Install devtools: utils::install.packages('devtools')
• Install the package using: devtools::install_github('duct317/scDHA')
  Or, install with manual and vignette: devtools::install_github('duct317/scDHA', build_manual = T, build_vignettes = T)
• When the package is loaded, it will check for the necessary libtorch: library(scDHA)
libtorch can be installed using: torch::install_torch()

To run the Goolam example:

• Load the package: library(scDHA)
• Load Goolam dataset: data('Goolam'); data <- t(Goolam$data); label <- as.character(Goolam$label)
• Log transform the data: data <- log2(data + 1)
• Generating clustering result: result <- scDHA(data, seed = 1)
• The clustering result can be found here: cluster <- result$cluster
• Calculating adjusted Rand Index using mclust package: mclust::adjustedRandIndex(cluster,label)
• A detailed tutorial on how to use scDHA package is available at http://scdha.tinnguyen-lab.com/
  Or, a vignette in R Notebook format is available here

How to use the package for new data

To use our package for new data, the package includes these functions:
- scDHA: main function, doing dimension reuction and clustering. The input is a matrix with rows as samples and columns as genes. - scDHA.w: plot the normalized weight variances to select suitable cutoff for gene filtering (optional). - scDHA.vis: visualization. The input is demension reduction output. - scDHA.pt: generating pseudotime. The input is demension reduction output. - scDHA.class: classification new data using available one. The inputs consist of train data matrix, train data label and new data matrix. - The result is reproducible by setting seed for these functions. - More detail about parameters for each function could be found in the manual.

Citation:

Duc Tran, Hung Nguyen, Bang Tran, Carlo La Vecchia, Hung N. Luu, Tin Nguyen (2021). Fast and precise single-cell data analysis using a hierarchical autoencoder. Nature Communications, 12, 1029. doi: 10.1038/s41467-021-21312-2 (link)