Holomics
A User-Friendly R 'shiny' Application for Multi-Omics Data Integration and Analysis
A 'shiny' application, which allows you to perform single- and multi-omics analyses using your own omics datasets. After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended 'Holomics' workflow. Methods used in the package were implemented in the package 'mixomics' by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752> and are described there in further detail.
README
<!-- README.md is generated from README.Rmd. Please edit that file -->
# Holomics
[](https://cran.r-project.org/package=Holomics)
[](https://cran.r-project.org/package=Holomics)
[](https://cran.r-project.org/web/licenses/GPL-3)
[](https://www.repostatus.org/#active)
[](https://github.com/MolinLab/Holomics/commits/main)
[](https://doi.org/10.1186/s12859-024-05719-4)
<img align="right" src="inst/app/www/img/logo.png" width=300>
<b>Holomics</b> is an R Shiny application that enables users to perform
single- and multi-omics analyses by providing a user-friendly interface
to upload different omics datasets, select and run the implemented
algorithms and finally visualize the generated results.
<b>Holomics</b> is primarily built on the R package mixOmics, which
offers numerous algorithms for the integrative analysis of omics
datasets. From this repertoire, the single-omics algorithms “Principal
Component Analysis” (PCA) and “Partial Least Squares Discriminant
Analysis” (PLS-DA), the pairwise-omics analysis “sparse Partial Least
Squares” (sPLS) and the multi-omics framework DIABLO (“Data Integration
Analysis for Biomarker discovery using Latent variable approaches for
Omics studies”) have been implemented in <b>Holomics</b>.
## Installation
### CRAN
install.packages("Holomics")
### Github
# Install devtools if it is not already installed
install.packages("devtools")
library(devtools)
# Install Holomics package
install_github("https://github.com/MolinLab/Holomics")
### Additional packages
You need to install the Bioconductor package separately.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("mixOmics")
BiocManager::install("BiocParallel")
## Start application
Either with
library(Holomics)
run_app()
or
Holomics::run_app()
## Workflow
To use all the features offered, the following workflow should be
followed. First, datasets are uploaded, during which any necessary
pre-filtering or transformation steps take place. Next, the user should
proceed to the single-omics analysis, where key features are identified
and the datasets are reduced accordingly. After completing the
single-omics analyses, the user can apply multi-omics analyses to
identify correlations between two or more datasets. NOTE: If
pre-filtered datasets (ideally generated earlier using Holomics) have
already been uploaded, it is possible to start directly with the
multi-omics analysis.
<img src="vignettes/images/workflow.png" width="100%" />
## Further information
For further information on how to use Holomics please have a look at our
<a href='https://CRAN.R-project.org/package=Holomics/vignettes/Holomics.html'>vignette</a>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2.1 |
rolling linux/jammy R-4.5 | Holomics_1.2.1.tar.gz |
4.5 MiB |
1.2.1 |
rolling linux/noble R-4.5 | Holomics_1.2.1.tar.gz |
4.5 MiB |
1.2.1 |
rolling source/ R- | Holomics_1.2.1.tar.gz |
4.5 MiB |
1.2.1 |
latest linux/jammy R-4.5 | Holomics_1.2.1.tar.gz |
4.5 MiB |
1.2.1 |
latest linux/noble R-4.5 | Holomics_1.2.1.tar.gz |
4.5 MiB |
1.2.1 |
latest source/ R- | Holomics_1.2.1.tar.gz |
4.5 MiB |
1.2.1 |
2026-04-26 source/ R- | Holomics_1.2.1.tar.gz |
4.5 MiB |
1.2.1 |
2026-04-23 source/ R- | Holomics_1.2.1.tar.gz |
4.5 MiB |
1.1.1 |
2025-04-20 source/ R- | Holomics_1.1.1.tar.gz |
4.5 MiB |