scRGNet

Description

scRGNet is an R package for inferring cell-cell networks from encoded scRNA-seq data. It is the first R package that attempts to use torch(Falbel et al. 2021) in R to implement an feature(that is, gene) autoencoder from the recently proposed single cell graph neural network (scGNN) framework.(Wang et al. 2021) It generates an encoded feature matrix containing the low-dimensional representation of gene expression in each cell, and build a Cell-Cell network from the feature matrix using KNN and isolation forest(Liu, Ting, and Zhou 2008)(Cortes 2021). When using the feature autoencoder, discretized regulatory signals quantified from gene expression modeled by a left-truncated mixture Gaussian (LTMG) model can also be used as a regulariser.(Wan et al. 2019) It is unique from other R packages for scRNA-seq analysis in that scRGNet offers an option to analyse scRNA-seq data without assuming statistical distributions or relationships for gene expression.

R requirement: 4.1.2 or later version
Development environment: Ubuntu 20.04 LTS
Development platform: x86_64-pc-linux-gnu (64-bit)

Installation

To install the latest version of scRGNet:

require("devtools")
devtools::install_github("ff98li/scRGNet", build_vignettes = TRUE)

To run the Shiny app:

scRGNet::runscRGNet()

Overview

library(scRGNet)
ls("package:scRGNet")
#>  [1] "gene_counts"       "gene_counts_small" "generateNetwork"  
#>  [4] "plotCellNet"       "plotDegree"        "plotLog"          
#>  [7] "preprocessCSV"     "runFeatureAE"      "runLTMG"          
#> [10] "runscRGNet"        "scDataset"         "setHardware"      
#> [13] "setHyperParams"
data(package = "scRGNet")

Note that there are two datasets included in this package gene_counts and gene_counts_small. gene_counts is a raw scRNA-seq matrix from experiment GSE138852(Grubman et al. 2019). gene_counts_small is a subset of the gene_counts data for a quick demo of the package, containing only 48 cells and 1000 genes. For usage of functions in the package, please refer to package vignettes for more details:

browseVignettes(package = "scRGNet")

An overview of the package structure is provided below:

An overview of the package workflow is illustrated below:

Contributions

The author of the package is Feifei Li. The runLTMG function uses the LTMG object and the function for inferring LTMG tags from scgnnltmg(Wang et al. 2021). data.table R package(Dowle and Srinivasan 2021) is used for fast reading in a large size scRNA-seq raw matrix from csv. The Matrix(Bates and Maechler 2021) R package is used to store scRNA-seq data as a sparse matrix to reduce memory usage, and used to convert a tensor object to an R matrix. The scDataset object is an R6 object(Chang 2021) inherited from class dataset from torch. The feature autoencoder is also an R6 object inherited from the basic neural network modules nn_module from torch R package, and it makes use of its functional modules nnf_linear and nnf_relu.(Falbel et al. 2021) Iteration of model training makes use of coro::loop form the coro R package. The model training also uses progress R package to inform users the model trainning progress. The generateNetwork function makes use of graph_from_data_frame from igraph R package to generate a plottable igraph object.(Csardi and Nepusz 2006), and the isolation forest model from the isotree R package(Cortes 2021) is used to prune outliers in cell graphs produced by generateNetwork. The interactive visualisation of produced cell network makes use of the visNetwork R package(Almende B.V. and Contributors, Thieurmel, and Robert 2021). plotDegree and plotLog make use of the graphics R package(R Core Team 2021). cluster_label_prop and degree function from igraph R package are used to compute the communities and degrees of the network. The shiny app of this package is made with shiny(Chang et al. 2021), shinyjs(Attali 2020), and shinybusy(Meyer and Perrier 2020) R packages. Except for the LTMG modeling in runLTMG uses external R package for computation, all other functions for data processing and analysis in this package are my original R implementation.

Acknowledgements

This package was developed as part of an assessment for 2021 BCB410H: Applied Bioinformatics, University of Toronto, Toronto, CANADA.

References

Almende B.V. and Contributors, Benoit Thieurmel, and Titouan Robert. 2021. visNetwork: Network Visualization Using ’Vis.js’ Library. https://CRAN.R-project.org/package=visNetwork.

Attali, Dean. 2020. Shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds. https://CRAN.R-project.org/package=shinyjs.

Bates, Douglas, and Martin Maechler. 2021. Matrix: Sparse and Dense Matrix Classes and Methods. https://CRAN.R-project.org/package=Matrix.

Chang, Winston. 2021. R6: Encapsulated Classes with Reference Semantics. https://CRAN.R-project.org/package=R6.

Chang, Winston, Joe Cheng, JJ Allaire, Carson Sievert, Barret Schloerke, Yihui Xie, Jeff Allen, Jonathan McPherson, Alan Dipert, and Barbara Borges. 2021. Shiny: Web Application Framework for r. https://CRAN.R-project.org/package=shiny.

Cortes, David. 2021. Isotree: Isolation-Based Outlier Detection. https://CRAN.R-project.org/package=isotree.

Csardi, Gabor, and Tamas Nepusz. 2006. “The Igraph Software Package for Complex Network Research.” InterJournal Complex Systems: 1695. https://igraph.org.

Dowle, Matt, and Arun Srinivasan. 2021. Data.table: Extension of ‘Data.frame‘. https://CRAN.R-project.org/package=data.table.

Falbel, Daniel, Javier Luraschi, Dmitriy Selivanov, Athos Damiani, Christophe Regouby, Krzysztof Joachimiak, and Hamada S. Badr. 2021. “Torch: Tensors and Neural Networks with ’GPU’ Acceleration.” RStudio. https://torch.mlverse.org/.

Grubman, Alexandra, Gabriel Chew, John F Ouyang, Guizhi Sun, Xin Yi Choo, Catriona McLean, Rebecca K Simmons, et al. 2019. “A Single-Cell Atlas of Entorhinal Cortex from Individuals with Alzheimer’s Disease Reveals Cell-Type-Specific Gene Expression Regulation.” Nature Neuroscience 22 (12): 2087–97.

Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. 2008. “Isolation Forest.” In 2008 Eighth IEEE International Conference on Data Mining, 413–22. https://doi.org/10.1109/ICDM.2008.17.

Meyer, Fanny, and Victor Perrier. 2020. Shinybusy: Busy Indicator for ’Shiny’ Applications. https://CRAN.R-project.org/package=shinybusy.

R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wan, Changlin, Wennan Chang, Yu Zhang, Fenil Shah, Xiaoyu Lu, Yong Zang, Anru Zhang, et al. 2019. “LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.” Nucleic Acids Research 47 (18): e111–11. https://doi.org/10.1093/nar/gkz655.

Wang, Juexin, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Hongjun Fu, Cankun Wang, Ren Qi, Qin Ma, and Dong Xu. 2021. “scGNN Is a Novel Graph Neural Network Framework for Single-Cell RNA-Seq Analyses.” Nature Communications. https://doi.org/10.1038/s41467-021-22197-x.

ff98li / scRGNet