Pipeline overview

The Omix pipeline offers an integration and analysis framework for multi-omics intended to pre-process, analyse, and v isualise multimodal data flexibly to address research questions. Omix is built on four consecutive blocks, (1) preparation of the multimodal container, (2) processing and quality control, (3) single omic analyses, and (4) multi-omics vertical integration

Pipeline outputs are standardised and include publication-quality plots, tables, and interactive reports.

Omix offers a range of state-of-the-art processing functions and integrative models, equipped with versatile parameters and quality control features. This empowers researchers to explore different integration strategies easily, enhancing the speed, scalability, and flexibility of multi-omics analyses.

While the current version focuses on bulk transcriptomics and proteomics, future iterations aim to encompass a broader range of omics types, expanding the software’s applicability and usefulness.

Installation

You can install the development version of Omix from GitHub with:

# install.packages("devtools")
devtools::install_github("eleonore-schneeg/Omix")

Overview of Omix features

• Multi-omics data container
  • The Omix multimodal container harmonises data management of multiple omics datasets. It enables the storage of raw and processed omics data slots, along with patient metadata, technical metadata, analysis parameters and outputs. The object structure relies on the MultiAssayExperiment library
• Data processing & Quality Control
  • Each omics layer is processed separately according to best practices. Given the wide range of processing functionalities, users decide which parameters and steps of the modular sequence are performed, which involves all or a combination of the folllowing steps:
  • Feature filtering
  • Normalisation/ transformation
  • Batch correction & denoising
  • Sample outlier removal
  • Formatting
• Single platform models
  • Omix provides a suite of analysis options including differential analysis (DE), a standard method to identify genes that are differentially expressed between certain disease states; Weighted Gene correlation Network (WGCNA), to identify modules of genes that associate to certain disease covariates; sparse Partial Least Square (sPLS) to define a sparse set of omics features, or molecular signature, that explains the response variable.
• Vertical integration for joint analysis
  • Omix provides a series of state-of-the-art integration models to perform patient-specific multi-omic integration, including:
  • MOFA
  • MEIFESTO
  • Sparse Multi-Block PLS (sMB-PLS)
  • DIABLO
  • Multi-omics clustering models such as iCluster
• Downstream analyses
  • Multi-omics integration is followed by a series of downstream analyses, including:
  • Multi-omics networks with iGraph
  • Community detection with the Louvain or Leiden clustering algorithms
  • Pseudotime inference with Slingshot
  • Functional enrichment with EnrichR
  • Cell-type enrichment with EWCE
  • Target validation based on the OpenTargets database
  • Publication quality plots and analysis reports

Omix implements these modular steps and displays results in an interactive reports.

Benchmark of multi-omics integration softwares

library(knitr)
kable(head(comparison_table))

…1	Supported omics	Pre-processing	Single omic analysis	Integrative models	Use case	Downstream analyses	Interactive visualisations	Language	Ref
Miodin	SNP, RNA, methylation, Proteins,	+	-	MOFA	BM	-	-	R	Ulfenborg, B. (2019).
MiBiOmics	miRNA, RNA, Proteins	-	-	Co-inertia analysis	BD	Multi-omics networks	+	Web	(Zoppi et al., 2021)
Muon	Single cell omics	-	-	MOFA, WNN	NA	-	-	Python	(Bredikhin et al., 2022)
Movics	SNP, RNA, methylation, Proteins	-	-	Range of clustering algorithm	STR	Survival analysis, enrichment	-	R	(Lu, 2020)
Omix	RNA, Proteins	+	+	MOFA,MEIFESTO, sMBPLS, MBPLS, DIABLO, iCluster	BD, BM, STR	Multi-omics signatures, networks, modules, functional/ cell type/ TF enrichment, pseudotime, etc	+	R	NA

Biomarker Discovery (BD), Biological Mechanisms (BM), Sample stratification (STR)

Getting started

The Getting Started section of the documentation contains downloadable examples on how to use Omix.

Data

The experiments described in our vignettes rely on in house data from the Multi-Omics Atlas Project, which may be obtained from the synapse portal for registered users.

• Project SynID: syn36812517
• Get started data: syn51533729
• Pseudo-temporal multi-omics integration: syn51516099

Docker image

For reproducibility purposes, we provide a Docker container here.

After installing Docker you can first pull the container via:

docker pull eleonoreschneeg/omix:latest

and then run the container:

docker run --rm -d -v $HOME:/home/rstudio/home -e ROOT=true -e PASSWORD=password -p 8787:8787 eleonoreschneeg/omix:1.0.0

An RStudio server session can be accessed via a browser at localhost:8787 using Username: rstudio and Password: password Navigate to Omix and open the Omix.Rproj file.

Citation

Please cite Omix as:

Eléonore Schneegans, Nurun Fancy, Michael Thomas, Nanet Willumsen, Paul M Matthews, Johanna Jackson (2023) Omix: A Multi-Omics Integration Pipeline

Contributing

For feature requests, please open an issue here.

Alternatively, you can fork the repository, add your change and issue a pull request.