Introduction
We provide an R implementation of DIVAS (Prothero et al., 2024), a statistical method for multi-modal data integration. Via statistical analysis of subspaces, DIVAS identifies joint, partially shared, and individual variation across multiple data blocks in a completely data-driven way. In addition to translating the original MATLAB implementation into an accessible R package, we also provide visualization tools and worked examples for exploring DIVAS results in practice.
Documentation website: https://byronsyun.github.io/DIVAS/
Installation
Dependencies
The DIVAS package requires the current 1.x line of the CVXR package for compatibility with the SCS solver interface. The package has been tested with CVXR 1.0-15. In DESCRIPTION, DIVAS also declares CVXR (>= 1.0-15) as a package dependency.
# Install devtools (if not already installed)
install.packages("devtools")
# Install CVXR 1.x. DIVAS has been tested with CVXR 1.0-15.
install.packages("remotes")
remotes::install_version("CVXR", version = "1.0-15", repos = "https://cloud.r-project.org")
# Alternatively, install the latest CRAN release if it is compatible with your R version.
# install.packages("CVXR")Installing the DIVAS package
You can install the development version of DIVAS from GitHub using devtools:
# Install DIVAS package from the main branch on GitHub
devtools::install_github("ByronSyun/DIVAS/pkg", ref = "main")
# Or install from a local folder if you have cloned the repository
# devtools::install("path/to/DIVAS-main/pkg")Usage Examples
The DIVAS package supports analysis of various data formats. Here is a simple example using the built-in toy MATLAB dataset:
library(R.matlab)
library(DIVAS)
data_path <- system.file("extdata", "toyDataThreeWay.mat", package = "DIVAS")
data <- readMat(data_path)
datablock <- list(
X1 = data$datablock[1,1][[1]][[1]],
X2 = data$datablock[1,2][[1]][[1]],
X3 = data$datablock[1,3][[1]][[1]]
)
result <- DIVASmain(datablock)
dataname <- paste0("DataBlock_", 1:length(datablock))
plots <- DJIVEAngleDiagnosticJP(datablock, dataname, result, 566, "Demo")
print(plots)For more detailed tutorials, see the documentation website and linked case studies below.
Available Datasets
We provide the following examples to illustrate the use of DIVAS in different scenarios.
| Dataset | Brief Description | Vignette Link | Format | Primary Reference |
|---|---|---|---|---|
| toyDataThreeWay.mat | Synthetic 3-block data with known joint structures | Toy Dataset Example | .mat | Prothero et al. (2024) |
| gnp_imputed.qs | GNP economic time series data | GNP Dataset Example | .qs | Stock & Watson (2016) |
| COVID-19 Multi-Omics | 6-block integration: scRNA-seq (4 cell types), proteomics, metabolomics from 114 COVID-19 patient samples | COVID Case Study | .rds | Su et al. (2020) |
Case Study: COVID-19 Multi-Omics Analysis
This project serves as a comprehensive, real-world application of the DIVAS package on a complex multi-omics dataset from a COVID-19 patient cohort. It demonstrates the full data processing and analysis workflow, from raw data cleaning to final DIVAS results, showcasing the practical utility of the package.
Citation
If you use DIVAS, please cite the manuscript:
Sun, Y., Marron, J. S., Lê Cao, K.-A., & Mao, J. (2026). DIVAS: an R package for identifying shared and individual variations of multiomics data. bioRxiv, 2026.01.12.698985. https://doi.org/10.64898/2026.01.12.698985
The package citation is also available in R with citation("DIVAS") and on the Authors and Citation page.
Developers
- Jiadong Mao - Lead Developer, Maintainer
- Yinuo Sun - Package Developer, Maintainer
References
Prothero, J., et al. (2024). Data integration via analysis of subspaces (DIVAS). TEST.
Su, Y., Chen, D., Yuan, D., et al. (2020). Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19. Cell, 183(6), 1479-1495. https://doi.org/10.1016/j.cell.2020.10.037