Spatial omic analysis toolbox for multi-resolution and multi-omic integration using image registration
VoltRon is a spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is also capable of analyzing multiple types of spatially-aware data modalities.
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Coarse-to-fine Image Alignment incorporates OpenCV (Coarse) and SimpleITK (Fine) tools to align images and spatial omics datasets across same or adjacent tissue sections. Users may interact with built-in mini shiny apps to change alignment parameters and validate alignment accuracy.
- Automated Image Registration uses SIFT and ORB methods (OpenCV) to detect common features across images automatically whereas B-Spline interpolation is used to improve alignment accuracy of local structures.
- Manual Image Registration helps users to select common features across spatial datasets using reference images stored in VoltRon objects. In case automated image registration doesn't work, you can still align images by manually picking landmark points. Users can also employ B-Spline interpolation after manual coarse alignment.
- Non-rigid Image Registration is performed using both B-Spline (SimpleITK) and Thin-plate-spline (TPS, OpenCV) interpolation methods.
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Spatially Aware Analysis allows detecting spatial patterns across cells, spots, molecules and other entities.
- (Niche Clustering: Spots) VoltRon allows integration to single cell RNA datasets using Seurat, SingleCellExperiment and spacexr for spot deconvolution. Estimated cell type abundances are then used to cluster spots into groups of cell type niches which are defined as spots with distinct composition of cell types.
- (Niche Clustering: Cells) VoltRon creates spatial neighborhoods around cells to cluster local cellular compositions around all cells which in turn informs users on cell types that are likely within proximity to each other.
- (Hot Spot Detection) VoltRon detects region of locally spatial patterns of cells/molecules/spots that are abundant in biological events and/or features.
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Support for Larger-than-memory data enables storing large feature data matrices and large microscopic images of tissues on disk without overloading memory, thus allowing analysis on large datasets with ease. VoltRon stores large images as pyramid structures to speed up visualization and data retrieval.
You can install for Linux, Windows and MacOS setups via r-universe which you may require R version >= 4.5.0:
install.packages('VoltRon', repos = c('https://bimsbbioinfo.r-universe.dev',
'https://bioc.r-universe.dev',
'https://cloud.r-project.org'))The development version can also be installed from GitHub repository using devtools:
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("BIMSBbioinfo/VoltRon")Depending on the number of required dependencies, installation may be completed under a minute or may take a few minutes.
On Windows and MacOS, OpenCV will be downloaded automatically upon installation. However, Rtools may be required to be downloaded too, hence this may take some time!
On Ubuntu you may need libopencv-dev:
sudo apt-get install libopencv-devVoltRon incorporates VoltRonStore package to install dependencies necessary to run VoltRon objects on disk for large images and data matrices.
The script below will install dependencies such BPCells, ImageArray, HDF5DataFrame etc.
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("BIMSBbioinfo/VoltRonStore")or from R-universe:
install.packages('VoltRonStore', repos = c('https://bimsbbioinfo.r-universe.dev',
'https://bioc.r-universe.dev',
'https://bnprks.r-universe.dev',
'https://cloud.r-project.org'))See https://bimsbbioinfo.r-universe.dev/builds for more information.
VoltRon incorporates the SimpleITK package to execute non-rigid alignment across assays. You can download MacOS-arm (.tgz) or Windows (.zip) binaries from
https://github.com/BIMSBbioinfo/SimpleITKRInstaller/releases, and install as below:
install.packages("path/to/SimpleITK_2.5.3.tgz", repos = NULL, type = "binary")You can also install SimpleITK from GitHub using the following command. Depending on the number of processors the user has,
you can modify the MAKEJ=6 argument. We also need SimpleElastix module of SimpleITK to be installed,
hence we add -DSimpleITK_USE_ELASTIX=ON to the ADDITIONAL_SITK_MODULES argument.
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github(
repo = "SimpleITK/SimpleITKRInstaller",
configure.vars=c("MAKEJ=6",
"ADDITIONAL_SITK_MODULES=-DSimpleITK_USE_ELASTIX=ON"))For more information, plase visit the SimpleITK website.
VoltRon incorporates RBioformats package to import images from ome.tiff files, which requires Java JDK to be available in your system:
See https://cran.r-project.org/web/packages/rJava below for more information.
RCDT package has been archived as of 15.01.2026. Please install as below:
devtools::install_github('stla/RCDT')or
install.packages('RCDT', repos = c('https://bimsbbioinfo.r-universe.dev',
'https://cloud.r-project.org'))To ask questions please use VoltRon discussion forum on google groups.
You can also run VoltRon from a container already available in Docker Hub. The docker image is based on the Rocker Project and can be run from the terminal like below:
docker run --rm -ti -e PASSWORD=<yourpassword> -p 8787:8787 amanukyan1385/rstudio-voltron:main
Then, start the RStudio session from the browser at http://localhost:8787/ and enter rstudio as username and <yourpassword> as password.
See here for more instructions on how to run the container using Docker Desktop.
Manukyan, A., Bahry, E., Wyler, E., Becher, E., Pascual-Reguant, A., Plumbom, I., ... & Akalin, A. (2023). VoltRon: A Spatial Omics Analysis Platform for Multi-Resolution and Multi-omics Integration using Image Registration. bioRxiv, 2023-12.