Omix: A Transcriptomics-Proteomics Integration Pipeline

The Omix pipeline is a specialised framework for pre-processing, analysing, integrating, and visualising bulk transcriptomics and proteomics data. By embedding state of the art bioinformatics tools with novel features into a user-friendly wrapper, Omix streamlines the integrative analysis of bulk transcriptomics and proteomics data, thereby facilitating complex biological investigations, from biomarker discovery to patient stratification.

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

The Omix pipeline offers an integration and analysis framework for multi-omics intended to pre-process, analyse, and visualise multimodal data flexibly to address research questions. Omix is a wrapper tool built on five consecutive blocks, (1) preparation of the multimodal container, (2) data processing and quality control, (3) single omic analyses, and (4) Transcriptomics-Proteomics vertical integration, (5) Joint Transcriptomics-Proteomics post-integration downstream analyses

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 interactive reports.

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)

Datasets:

• Get started data: syn51533729
• Pseudo-temporal multi-omics integration data: syn51516099 https://doi.org/10.7303/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:latest

An RStudio server session can be accessed via a browser at localhost:8787 using Username: rstudio and Password: password

Citation

Please cite Omix as:

Eléonore Schneegans, Nurun Fancy, Michael Thomas, Nanet Willumsen, Paul M Matthews, Johanna Jackson (2023) Omix: A Transcriptomics-Proteomics 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.