10X Genomics' Single Cell Multiome ATAC + Gene Expression assay makes it possible to measure chromatin accessibility and gene expression from the same nucleus. It sounds like the perfect solution - no more aligning different assays, no more worrying about cell matching. But actually, this kind of dataset is extremely complex. Getting it right is not only about running Cell Ranger ARC or making UMAPs. We have seen again and again that even experienced labs can make subtle mistakes that ruin biological interpretation later.
Multiome data is powerful because it links transcriptional programs and regulatory landscape at single-cell resolution. But that is also why it’s fragile. ATAC-seq and RNA-seq are very different types of signals - and combining them is not easy. Many pipelines pretend to “integrate” them, but actually just follow defaults without understanding what is being integrated.
In this article, we describe ten major problems we have encountered when analyzing 10X Genomics Single Cell Multiome datasets. These are not simple QC issues. These are real conceptual traps - where the results look fine, but conclusions are misleading. For each problem, we explain why it happens, show examples from real projects, and how we prevent it.
Single Cell Multiome analysis is powerful - but complex. We identify and correct conceptual traps before they derail your interpretation. Request a free consultation →
The Problem
Even though 10X Genomics Multiome data comes from the same cell, RNA and ATAC parts are sometimes not matched well. People think it's clean - but actually many barcodes are missing or filtered unevenly.
Why It Happens
In Cell Ranger ARC, the filtering thresholds for ATAC and RNA are applied separately. When data is loaded into Seurat or ArchR, analysts often use the intersection of barcodes that pass both filters. But what if a cell is good in RNA but borderline in ATAC? It gets dropped silently, and clusters lose important cells - distorting biology.
Real Example
In one developing cortex dataset, the neuroblast cluster had strong RNA signatures but seemed “flat” in ATAC. The pipeline had excluded many of these cells due to uneven ATAC filtering. Once we recovered matching barcodes manually, clear enhancer opening patterns emerged.
What We Do Differently
We review RNA and ATAC filtering separately, visualize barcode retention in both spaces, and decide whether to use union or intersection of barcodes. We also track population-specific losses and, if needed, re-run fragment filtering outside Cell Ranger ARC to recover biologically meaningful cells.
The Problem
Researchers often apply uniform QC cutoffs across RNA and ATAC, but these metrics differ drastically in sparsity and noise.
Why It Happens
Analysts use gene counts, UMIs, and mitochondrial percentage for RNA, but apply similar thresholds to ATAC fragment counts and TSS enrichment. Without modality-specific visualization, cells are over‑filtered or low-quality cells slip through.
Real Example
In a blood Multiome dataset, monocytes were lost due to low RNA gene counts despite strong ATAC signal. The generic filter removed nearly all monocytes, skewing downstream clustering.
What We Do Differently
We perform separate QC for RNA (genes per cell, mitochondrial rate) and ATAC (fragment distribution, TSS enrichment). We set thresholds based on biological expectations and visualize metrics before filtering to avoid unintended losses.
Silent barcode mismatches and unbalanced filters can skew your data. We ensure balanced integration and preserve all meaningful cells. Request a free consultation →
The Problem
Weighted nearest neighbor (WNN) clustering can be driven almost entirely by RNA or ATAC, masking complementary information.
Why It Happens
Seurat’s default modality weights favor the modality with higher variance or depth. If RNA has stronger signal, ATAC gets down‑weighted - even when regulatory information is crucial.
Real Example
In a tumor microenvironment sample, WNN clusters were identical to RNA-only clustering. Important chromatin transitions were completely missed because ATAC contribution was negligible.
What We Do Differently
We generate RNA-only and ATAC-only UMAPs and clusterings, manually tune WNN weights, or use ArchR’s iterative LSI for ATAC-driven grouping. We always check modality contributions before interpreting clusters.
The Problem
Inferring pseudotime on one modality can misrepresent the timing of chromatin versus transcriptional changes.
Why It Happens
Many rely on RNA trajectories and overlay ATAC scores afterward, but chromatin can open or close before RNA changes, leading to misinterpretation of regulatory dynamics.
Real Example
In an EMT Multiome study, RNA pseudotime suggested late repression of epithelial genes, but ATAC showed promoter closure early. Relying solely on RNA missed the priming event.
What We Do Differently
We infer pseudotime separately for RNA and ATAC using Monocle, Slingshot, or ArchR, and compare timings. We examine gene-specific chromatin-expression kinetics to capture true dynamics.
Rare cell types often lose their peaks in global calling. We perform cluster-level peak analysis to preserve rare biology. Request a free consultation →
The Problem
Global peak calling from aggregated fragments often misses rare or cluster-specific accessible regions.
Why It Happens
When certain cell types are underrepresented, their regulatory peaks are diluted in the aggregate, and default algorithms omit these important sites.
Real Example
In a hematopoietic stem cell dataset, progenitor-specific enhancers (3% of cells) were absent from the global peak set. Cluster-wise MACS2 calling recovered dozens of lineage‑specific peaks.
What We Do Differently
We perform cluster-level peak calling via ArchR’s addGroupCoverages() + addReproduciblePeakSet(), or export fragments for manual MACS2 per cluster. This reveals rare but real regulatory elements.
The Problem
Gene activity scores from ATAC are often interpreted as direct transcriptional proxies, but they can diverge from true expression.
Why It Happens
Activity is inferred from promoter and gene-body accessibility, but open chromatin does not guarantee transcription. Compact or GC-rich promoters may show low ATAC signal despite high expression.
Real Example
In adrenal gland cells, TH expression was high in RNA but had low activity score due to a compact, GC-rich promoter. Interpreting activity as expression would have falsely labeled these cells inactive.
What We Do Differently
We treat gene activity as a proxy, directly compare it with RNA, and adjust models (e.g., include distal peaks) when necessary. Discrepancies become insights into regulatory mechanisms.
Gene activity ≠ gene expression. We interpret ATAC signals carefully to avoid misleading conclusions. Request a free consultation →
The Problem
TF motif enrichment or chromVAR scores can be driven by GC content or peak size biases, producing false positives.
Why It Happens
Without matched-background correction for GC content, mappability, and peak length, motif analysis tools report spurious enrichments.
Real Example
In a neurogenesis dataset, SOX2 and POU5F1 motifs appeared enriched in glial cells despite no expression. After chromVAR with GC-matched background and reproducible peak filtering, the enrichments vanished.
What We Do Differently
We use chromVAR or HOMER with strict background matching and validate motif enrichments against TF expression to avoid reporting silent factors.
The Problem
Peak-to-gene linkages based on correlation can be unstable and overfitted without reproducibility checks.
Why It Happens
Tools like ArchR and Cicero infer links from accessibility-expression correlations, but noise, depth variation, and distance effects inflate false positives.
Real Example
In a lung fibrosis study, only 12% of the 17,000 inferred links were reproducible across replicates. Many high-scoring links lacked biological support or linked enhancers to unrelated genes.
What We Do Differently
We apply reproducibility filters, distance constraints, and cross-reference with enhancer databases (ENCODE, GeneHancer). We report confidence levels and recommend orthogonal validation when possible.
Spurious correlations can mislead regulatory inferences. We apply rigorous filtering and validation to ensure robust cis-regulatory insights. Request a free consultation →
The Problem
Technical differences across samples can create spurious clusters or trajectories mistaken for biological states.
Why It Happens
Variations in sequencing depth or signal quality across donors or batches drive separation if integration is not handled carefully.
Real Example
In a PBMC Multiome from two donors, a donor-specific cluster was misinterpreted as a novel cell type. It was actually low-quality cells from donor B with reduced RNA complexity.
What We Do Differently
We use Harmony or CCA with balanced anchors, inspect metadata-driven separations, and perform diagnostic plots to ensure true biological signals drive clustering.
The Problem
Visible UMAPs, links, and motifs entice strong conclusions, but without validation these claims are fragile.
Why It Happens
Pressure to publish or present novel insights leads teams to overinterpret noisy or sparse signals, inviting reviewer pushback.
Real Example
In a cancer immunology study, claims of new regulatory programs in exhausted CD8+ T cells were undermined by low ATAC depth and lack of qPCR validation. Reviewers delayed resubmission by eight months.
What We Do Differently
We deliver reviewer-ready outputs: UCSC track hubs, QC reports, confidence-scored networks, and advise on necessary orthogonal validations (qPCR, CRISPR, atlas comparisons) early in the project.
10X Genomics Single Cell Multiome opens unprecedented views into regulatory and transcriptional programs. Yet its power comes with complexity-subtle assumptions and defaults can quietly derail your conclusions. With rigorous QC, modality-aware analyses, and validation-ready outputs, you can harness Multiome data to drive robust biological discovery.
If you’re analyzing Multiome datasets and want confidence in your results, our experts can help you catch conceptual traps early, recover hidden signals, and prepare outputs that stand up to reviewer scrutiny.
Want confidence in your Multiome analysis? We identify fragile steps and support your work through publication and review. Request a free consultation →