Hey Bioinformatics lovers,

Tommy here. Thanksgiving is around the corner.

If you are thankful to someone, it is a good time to express your appreciation!

We will discuss multi-omics integration methods and their pitfalls today.

You’ve finally got the data. RNA-seq data. Methylation arrays. Proteomics.

You’re ready to integrate everything and unlock biological insights that single-omics approaches miss.

Then you hit the wall: How do you actually combine these data types without destroying your signal?

Here’s what the textbooks don’t tell you.

The Question Comes Before the Method

Multi-omic integration sounds powerful, but it’s not magic.

The biggest mistake? Reaching for a fancy tool before knowing what you’re actually asking.

Start here: Do you want to find shared programs across omics, or unique signals from each modality?

That single choice determines everything.

Want unsupervised discovery of shared variation? Try MOFA2.

Need to predict disease outcomes or treatment response? DIABLO fits better.

Graph-based models can work too—but only if they actually outperform simpler approaches for your question.

Real example: A recent chronic kidney disease study used both MOFA2 and DIABLO.

Not because they were hedging bets, but because different questions needed complementary tools (see the paper).

Another new preprint takes a similar dual approach for a different disease (here).

Why Naive Integration Fails

Here’s the reality of multi-omics data:

- RNA-seq: 200 samples - Proteomics: 150 samples - Methylation: 180 samples

You can’t just concatenate these matrices. You have missing data.

Even if you have a full dataset, you’ll get one of two disasters:

1. Drowned signal: Real biological patterns vanish in the noise
2. Phantom clusters: Artificial groupings driven entirely by batch effects

Each data type has fundamentally different properties:

- scATAC-seq is sparse (mostly zeros)

- Proteomics is noisy (high technical variation)

- RNA-seq has 20,000+ features (high dimensionality)

- Methylation samples from ~50,000 regions covering over 9 million possible CpG sites

Treating them the same is like averaging marathon times with golf scores.

The numbers exist, but the combination is meaningless.

What Actually Works

Good integration methods handle these differences explicitly.

They normalize each modality separately, learn optimal weights for combining them, or use regularization to prevent any single data type from dominating.

MOFA2, DIABLO, and weighted PCA all do this—but in different ways for different goals.

Want to see how this fails in practice? This post on Visium spatial data integration shows what happens when normalization within modality gets skipped. The patterns looked compelling until validation time.

Biology > Black Boxes

Here’s the uncomfortable truth: These methods find correlations, not causes.

Your integration might reveal that certain genes, proteins, and methylation sites cluster together.

That’s interesting. But it doesn’t tell you which one drives the others, or whether they’re all responding to something else entirely.

Before you trust any multi-omics result:

- Map it back to known pathways and biology - Run orthogonal validation experiments
- Test if patterns generalize across independent cohorts - Ask if the finding makes biological sense

If you can’t trace your integrated result back to specific genes, CpGs, or proteins with testable hypotheses, you haven’t actually learned anything.

Your Multi-Omics Checklist

✓ Start with the biological question (not the method)
✓ Pick tools that match your goal (unsupervised discovery vs. prediction vs. networks)
✓ Normalize each modality separately before integration
✓ Validate everything with orthogonal data
✓ Prioritize biology over mathematical elegance

Resources to go deeper:

- Awesome Multi-Omics tools list - Methods review and benchmarking- Multi-omics integration strategies guide​

Multi-omics integration is messy.

The data never align perfectly, the methods make strong assumptions, and interpretation requires constant vigilance.

But when you ask the right question and use appropriate methods, multi-omics can reveal biology that single data types miss entirely. Just don’t expect it to be easy.

What’s been your biggest multi-omics integration challenge? Hit reply—I’d love to hear what’s working (or not working) in your analyses.

Happy Learning!

Tommy aka crazyhottommy

Other posts from my LinkedIn that you may find helpful https://www.linkedin.com/in/%F0%9F%8E%AF-ming-tommy-tang-40650014/recent-activity/all/​

PS:

If you want to learn Bioinformatics, there are other ways that I can help:

1. My free YouTube Chatomics channel, make sure you subscribe to it. I promise to make more :) I have been lazy recently.
2. I have many resources collected on my github here.
3. I have been writing blog posts for over 10 years https://divingintogeneticsandgenomics.com/​

Stay awesome!