Multi-Omics: Definition, Data Layers and Integration

Short answer. Multi-omics (also written multiomics, and sometimes “multiple omics”) means analysing two or more molecular layers, such as the genome, transcriptome, proteome, metabolome or epigenome, together rather than one at a time. The aim is to see how changes at one layer relate to changes at another. This page is general reference for researchers and lab staff; it is not clinical or medical advice.

Definition and scope: what multi-omics means

Illumina describes multiomics as an approach that “combines data from several omic layers” to show how molecular changes relate to development, cellular response and disease, naming genomics, transcriptomics, proteomics and epigenomics among the layers (Illumina, Multiomics, read 2026-11-07). A 2025 review in Molecular Biomedicine defines multi-omics as large-scale, high-throughput analysis of molecular layers and lists genomics, transcriptomics, proteomics, metabolomics and epigenomics, with single-cell and spatial methods extending them (Jiang et al., PMC12638490, read 2026-11-07).

The variants “multi omics”, “multiomics”, “multi-omic” and “multiple omics” all point to the same idea. “Multi-omic” is simply the adjective form. Single-layer fields are covered separately on CASRAI, for example genomics and proteomics.

What are the types of biological data in multi-omics?

The sources above name five molecular layers. The table lists only what those sources state.

Layer	What it describes	Source
Genomics	The genome and its variation	Illumina; Jiang et al.
Transcriptomics	The complete set of RNA transcripts	Illumina; Jiang et al.
Proteomics	Proteins	Illumina; Jiang et al.
Metabolomics	Metabolites	Jiang et al.
Epigenomics	Epigenetic modifications of the genome	Illumina; Jiang et al.

There is no single agreed list of “six types” of biological data; the layers a study uses depend on its question. See also the metabolome guide and metagenomics.

How multi-omics is done in practice

Illumina describes two routes. Single-readout assays capture more than one layer from the same sample, for example ATAC-Seq (chromatin accessibility), CITE-Seq (protein and transcript together), spatial transcriptomics and NGS-based proteomics. “In-silico multiomics” instead reuses separate omic datasets from earlier experiments. For sequencing layers the workflow runs from primary base calling to secondary alignment and variant calling to tertiary interpretation across omes. Illumina states that sample requirements depend on sample type, instrument and method, and lists bulk samples, tissue sections, single cells and intact tissue (spatial) as sample types.

What is multiplexing in sequencing?

Multiplexing means adding an index (barcode) tag to each library so several samples can be pooled and sequenced in the same flow cell lane; the tag is read in a separate index read and used to sort reads back to their sample (Illumina, Multiplexed Sequencing datasheet, read 2026-11-07). Illumina’s index hopping white paper explains that reads can be assigned to the wrong sample, which is why unique dual indexing is used. Related: multiplexed PCR and NGS sequencing.

How to integrate multi-omics data

Jiang et al. separate two directions and three timings.

Horizontal versus vertical integration

• Horizontal: the same omics type combined across batches, platforms or labs.
• Vertical: different omics layers measured on the same samples.

Early, intermediate and late integration

Strategy	What happens	Noted trade-off	Tools named
Early	Layers are concatenated into one matrix	Can lose information or add noise	–
Intermediate	Layers are modelled jointly	The dominant approach	MOFA/MOFA+, iCluster, SNF, DIABLO
Late	Separate models are built, then combined	Often misses relationships between omics	–

For batch correction the review names limma and ComBat for bulk data, and MNN, Seurat v5, LIGER, Harmony and BBKNN for single-cell data. MOFA is a frequent search term here: it falls under intermediate (joint-model) integration.

What is single-cell spatial multi-omics?

It combines layers measured at single-cell resolution with their position in tissue. The review treats single-cell and spatial methods as extensions of the omics layers, and Illumina lists spatial transcriptomics and intact tissue among its sample types. For tissue mapping tools see SpatialData.

Where it matters for labs: a design checklist

These points come from the failure modes Jiang et al. report. Use them as questions to settle before collecting samples.

• Matched samples: complete multi-omics for every individual is often infeasible, and the complete-case sample size can be far below the cohort size. The review notes that OmicsAnalyst requires at least 20 matched samples for data-driven integration.
• Batch design: batch effects are a named challenge; in single-cell data, over-correction can merge distinct populations.
• Missing values: common especially in proteomics and metabolomics; imputation carries risk.
• Platform heterogeneity: different platforms produce data that need harmonising before they are combined.
• Method and sample type: Illumina says requirements depend on them, so confirm the input needs of each assay with its manufacturer.

Common misunderstandings

• More layers is not automatically better. Concatenating layers (early integration) can add noise.
• Multi-omics is not only new data. Reusing existing datasets (in-silico multiomics) counts.
• Integration does not remove batch effects. Correction is a separate step with its own risks.

FAQ

What does multi omics mean?

Joint analysis of two or more molecular layers, such as genome, transcriptome, proteome, metabolome and epigenome.

What is multiplexing in sequencing?

Tagging libraries with indexes so multiple samples share one lane and are separated computationally afterwards.

What are the types of biological data in multi-omics?

Genomic, transcriptomic, proteomic, metabolomic and epigenomic data are the layers named by the sources above.

How do you integrate multi-omics data?

Choose horizontal or vertical integration, then early, intermediate or late combination; intermediate joint models such as MOFA are the dominant approach.

Sources

• Illumina, Multiomics, read 2026-11-07.
• Jiang et al., 2025, Multi-omics strategies for biomarker discovery and application in personalized oncology, Molecular Biomedicine, PMC12638490, read 2026-11-07.
• Illumina, Multiplexed Sequencing datasheet and index hopping white paper, searched 2026-11-07.