Why Multi-Omics Data Integration Often Fails - And What We Do Differently

Introduction

Integrating RNA-seq, ATAC-seq, proteomics, and epigenomics data is no longer an experimental luxury. It is now the mainstream strategy to unravel complex regulatory networks, disease mechanisms, or cellular identities. Multi-omics gives us multiple layers of information - transcriptional, chromatin accessibility, protein abundance, DNA methylation, histone marks - all from the same system. But the integration itself is extremely challenging. And in real project work, we’ve seen that many multi-omics studies fall apart not because of wet-lab failure, but because the computational integration was poorly designed or executed.

We have helped many research teams - from academic centers to biopharma companies - to rescue multi-omics projects where results looked beautiful in isolation but contradicted each other when combined. The issue is rarely about software tools. The main problem lies in unrecognized biases, unaligned data structures, and misinterpretation across layers.

This article is not a tutorial for Seurat or MOFA. Instead, we summarize nine key challenges we encounter again and again in multi-omics data analysis projects. Each one is described in terms of the underlying problem, why it happens, a real project case (anonymized), and how we address it differently. If you are planning or analyzing a multi-omics study, we hope these insights help you avoid common pitfalls and save time.

Table of Contents

1. Unmatched Samples Across Omics Layers

The Problem

RNA, ATAC, proteomics, methylation - but from different sample sets. The integration produces confusing results because there’s no true pairing.

Why It Happens

In many studies, the different omics datasets were generated in different labs, or on different cohorts. People try to combine them based on group labels (e.g. "tumor" vs "normal") but without matched individuals or time points.

Real Example

One client had RNA-seq from 12 patients and proteomics from 8 of them - but only 4 overlapped. Their data integration showed poor correlation between gene expression and protein level. But the inconsistency was mostly due to mismatched subjects.

What We Do Differently

We start with a matching matrix - we plot what sample is available for which modality, and what overlaps exist. If necessary, we stratify analyses to avoid forcing unmatched data together. When true overlap is low, we use group-level summarization cautiously or switch to meta-analysis models.

2. Misaligned Resolution and Missing Cell Type Anchors

The Problem

Bulk RNA is compared with single-cell ATAC, or vice versa - and the integration fails because resolution is incompatible.

Why It Happens

People often believe that higher-resolution data (e.g., scATAC-seq) can be mapped to lower-resolution data (e.g., bulk RNA), but they don’t account for the missing cellular anchors or compositional differences.

Real Example

A study on brain tissue had bulk proteomics and scRNA-seq. The integration tried to map gene expression to protein levels. But many proteins were expressed in glial cells not captured well in the scRNA-seq clustering, leading to misleading correlations.

What We Do Differently

We evaluate the resolution of each omics dataset first. When integrating sc and bulk data, we use reference-based deconvolution, or infer cell type signatures. We explicitly define integration anchors - shared features that can bridge modalities - and assess resolution mismatch before going deeper.

3. Improper Normalization Across Modalities

The Problem

Normalization strategies differ across omics types. If not harmonized, integration becomes biased or even meaningless.

Why It Happens

RNA-seq is often normalized by library size or TPM; proteomics by TMT ratios or spectral counts; ATAC-seq by total peaks or binning; DNA methylation by β values. If analysts naively concatenate them, the dominant modality will skew clustering or PCA.

Real Example

One multi-omics study reported that ATAC-seq signal drove 90% of the variance in the integrated PCA. But we found that ATAC was not normalized at all - raw counts were used - while other layers were Z-scaled.

What We Do Differently

We bring each omics layer to comparable scale. This may involve quantile normalization, log transformation, CLR (centered log-ratio), or supervised scaling methods. We test effects using surrogate variable analysis and visualize modality contributions post-integration.

4. Blind Feature Selection Without Biological Guidance

The Problem

Thousands of features are selected per omics layer using variance or unsupervised metrics - without considering biological relevance or redundancy.

Why It Happens

Many integration pipelines automatically pick the “top variable genes,” “most enriched peaks,” or “top 2000 proteins.” But they don’t filter out mitochondrial genes, unannotated peaks, or proteins with missing values - nor do they consider which features are interpretable.

Real Example

A team presented an integrated heatmap showing “strong signals” in ATAC and proteomics layers. But many of the ATAC peaks were unannotated distal regions, and 40% of the proteins had >30% missing data imputed.

What We Do Differently

We apply biology-aware filters: remove mitochondrial or ribosomal genes, exclude blacklist peaks, and focus on features with known relevance to the system studied. For proteomics, we prefer high-confidence, consistently detected proteins and validate integration with pathway-level coherence.

5. Overinterpreting Weak Correlations Across Omics

The Problem

People expect high correlation between RNA and protein, or between ATAC and RNA - but find only weak associations and still publish the network.

Why It Happens

mRNA and protein expression often diverge due to post-transcriptional regulation. ATAC peak signal doesn’t always mean the gene is expressed. Analysts misinterpret low correlations as meaningful - or worse, selectively report stronger pairs.

Real Example

One integrated plot showed correlation of 0.3 between ATAC peaks and RNA for a set of genes. But half the peaks were >50kb away from the gene body. The regulatory logic was absent.

What We Do Differently

We only analyze regulatory links when distance, enhancer maps, or TF binding motifs support the association. We report confidence levels for each link and build integration not from raw correlation, but from mechanistic logic whenever possible.

6. Ignoring Batch Effects That Compound Across Layers

The Problem

Each omics layer has its own batch effects - and when integrated, the noise adds up. Patterns seen in PCA or clustering are batch-driven, not biology-driven.

Why It Happens

People apply combat or Harmony to each modality, but forget that integration can still amplify residual batch noise - especially when omics layers were generated in different labs.

Real Example

One study on leukemia integrated RNA, ATAC, and proteomics. The first principal component separated samples by sequencing vendor - not by disease subtype. The batch correction was done individually but not jointly.

What We Do Differently

We inspect batch structure both within and across omics layers. We apply cross-modal batch correction only after alignment. We use multivariate linear modeling or canonical correlation with batch covariates, and always verify that biological signals dominate the integrated structure.

7. Using the Wrong Dimensionality Reduction for Integration

The Problem

Standard PCA, UMAP, or t-SNE is applied to concatenated omics data - but it distorts the relationships due to unequal feature types.

Why It Happens

Many analysis pipelines use single-layer reduction methods (like PCA or UMAP) without considering that one modality may dominate due to variance or scale.

Real Example

In a hepatocyte differentiation project, concatenated data from ATAC and RNA led to clustering that reflected ATAC signal alone. The UMAP completely ignored RNA input.

What We Do Differently

We use integration-aware tools like MOFA+, DIABLO, or LIGER that can weight modalities separately. We always test several methods and validate the integrated space using known cell types, time points, or perturbation labels.

8. Mixing Static and Dynamic Signals Without Temporal Context

The Problem

ATAC, RNA, and proteomics are measured at different time points - but are treated as synchronous in analysis. This leads to incorrect interpretation of dynamics.

Why It Happens

Many studies collect ATAC or methylation at early time points, but measure protein or RNA later. Analysts often average them or align them as if they are simultaneous.

Real Example

A stem cell reprogramming study tried to integrate ATAC-seq at Day 1 and proteomics at Day 5. The result showed “activation” of TF targets - but the open chromatin had already closed by Day 3.

What We Do Differently

We map all measurements to a temporal axis first. When time points differ, we use interpolation, trajectory alignment, or latent time modeling. We flag asynchronous comparisons and adjust interpretation accordingly.

9. Tools That Promise Integration - But Mask Biological Conflicts

The Problem

Many tools claim to integrate multiple omics layers - but they hide or discard modality-specific patterns that actually conflict with each other.

Why It Happens

Methods like canonical correlation analysis or joint matrix factorization tend to find the “shared space” - which can downplay differences. Biological conflicts - like increased ATAC but unchanged gene expression - are treated as noise.

Real Example

A tool reported “integrated clusters” of immune cells across methylation, RNA, and proteomics. But in fact, many key lineage markers were discordant - high RNA but low protein, or accessible but not expressed.

What We Do Differently

We present both shared and unshared signals. We highlight discordance explicitly and use it to suggest post-transcriptional regulation or chromatin remodeling without transcriptional output. Integration does not mean flattening. It means layered insight.

Final Remarks

Multi-omics integration is one of the most powerful strategies in modern biology - but also one of the most fragile. Each layer comes with its own biases, resolution, and temporal dynamics. Successful integration is not about stacking matrices or running a fancy tool. It’s about understanding what the data represent - and what they don’t.

If you’re integrating RNA, ATAC-seq, proteomics, and epigenomic data, we strongly advise you to think carefully about alignment, feature selection, scale, and biological logic. Many integration errors are subtle - they do not crash your pipeline, but they mislead your conclusion. We have seen “beautiful” figures that collapse under scrutiny because the biology didn’t match.

Avoiding these mistakes can save months of confusion and lead to more accurate, compelling biological stories. If you need help designing, troubleshooting, or defending a multi-omics analysis, our team can guide you through.

This blog article was authored by Justin T. Li, Ph.D., Lead Bioinformatician. To learn more about AccuraScience's Lead Bioinformaticians, visit https://www.accurascience.com/our_team.html.