MUON: multimodal omics analysis framework

Danila Bredikhin,Oliver Stegle,Ilia Kats

doi:10.1186/s13059-021-02577-8

Abstract

Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. Here, we present a data standard and an analysis framework for multi-omics, MUON, designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible and interoperable data structure. MUON enables a versatile range of analyses, from data preprocessing to flexible multi-omics alignment.

Highlights

Multi-omics designs, that is the simultaneous profiling of multiple omics or other modalities for the same sample or cells, have recently gained traction across different biological domains
MuData provides a coherent structure for storing associated metadata and other side information, both at the level of samples and features
Application of MUON to single-cell multi-omics data To illustrate MUON, we considered data from simultaneous scRNA-seq and scATACseq profiling of peripheral blood mononuclear cells (PBMCs), which were generated using the Chromium Single Cell Multiome ATAC + Gene Expression protocol by 10x Genomics [22]

Summary

Introduction

Multi-omics designs, that is the simultaneous profiling of multiple omics or other modalities for the same sample or cells, have recently gained traction across different biological domains. Multi-omics approaches have been applied to enable new insights in basic biology and translational research [1, 2]. The emerging multi-omics datasets result in novel opportunities for advanced analysis and biological discovery [3]. Major challenges include efficient storage, indexing and seamless access of high-volume datasets from disk, the ability to keep track and link biological and technical metadata, and dealing with the dependencies between omics layers or individual features. Multi-omics datasets need to be converted into specific file formats to satisfy input requirements for different analysis and visualisation tools

Methods

Results

Discussion

Conclusion