Single-cell chromatin accessibility data - especially from scATAC-seq - gives us unprecedented resolution to study regulatory landscapes in heterogeneous tissues. But that doesn’t mean the results are always meaningful. Actually, we have seen many scATAC-seq projects go wrong, even when they look polished on the surface. Peaks are called, clusters are visualized, pseudo-bulk correlations are shown - but underneath, the interpretation is shaky. Sometimes, completely wrong.
In our experience, most failures are not due to careless mistakes. They happen because teams underestimate how noisy, sparse, and biased this data type is - and over-trust default analysis pipelines that were not designed to handle real-world complexity. In this article, we share ten problems we often encounter in single-cell chromatin accessibility analysis, and how we solve them in practice.
We don’t give basic tutorials. What follows are real diagnostic patterns - specific technical traps, subtle statistical artifacts, and solutions we’ve developed after rescuing flawed projects from many research teams. Some of these mistakes were made by very experienced scientists. We hope by reading this, you can avoid making the same.
Single-cell chromatin accessibility is powerful - but fragile. Our team identifies hidden pitfalls in your scATAC-seq analysis before they compromise your conclusions. Request a free consultation →
The Problem
Many teams filter cells with low transcription start site (TSS) enrichment assuming these are poor-quality nuclei. But sometimes low TSS score means something else - a cell-type with unusual chromatin organization, or sequencing chemistry bias.
Why It Happens
TSS enrichment was designed for bulk ATAC-seq to detect nucleosomal patterns. In single-cell context, especially with droplet-based platforms, it is influenced by DNA content, fragmentation bias, and even chromatin state itself. Certain immune or cancer cell types naturally show weak TSS patterns despite being biologically valid.
Real Example
In one tumor scATAC-seq dataset, myeloid cells were almost lost during quality filtering because their TSS enrichment score averaged below 4. But they had clear marker peaks, normal fragment sizes, and consistent clustering. These were not debris - just a cell population with atypical chromatin accessibility.
What We Do Differently
We use a flexible multi-metric approach: combine TSS enrichment, fragment size, and nucleosome pattern - but also cluster-level accessibility patterns. If a group of cells shows biological consistency, we retain them regardless of TSS score. Strict cutoffs often discard meaningful data.
The Problem
Some nuclei get assigned barcodes that partially overlap, leading to barcode collisions. These “merged” barcodes can resemble hybrid cells or multiplets - but standard doublet detection tools may miss them.
Why It Happens
Unlike scRNA-seq, scATAC-seq fragments span larger regions and are more susceptible to overlapping when barcode libraries are saturated. Additionally, lower unique molecular identifiers (UMIs) make it hard to confidently detect doublets.
Real Example
A B-cell and T-cell fusion cluster appeared in one scATAC-seq dataset. Standard doublet scores looked fine. But when we traced back, we found most barcodes had fragment pileups in both CD19 and CD3 loci - a strong sign of collision. The cluster vanished after reprocessing with improved demultiplexing.
What We Do Differently
We use collision-aware tools and perform fragment-overlap correlation checks across marker loci. Also, when possible, we reassign barcodes using raw FASTQ if barcode saturation or sequencing index bleed-through is suspected.
Single-cell chromatin pipelines can mislead - if unchecked. Our experts catch quality and interpretation traps before they derail your project. Request a free consultation →
The Problem
It’s common to create pseudo-bulk samples by summing up cells in a cluster. But this often conceals interesting differences between subtypes, especially if the cluster is heterogeneous or the read depth varies widely.
Why It Happens
In sparse scATAC-seq data, pseudo-bulk profiles can be dominated by a few high-depth cells or common peaks. This masks rare but meaningful cell-state–specific regulatory elements, especially enhancers with subtle accessibility.
Real Example
In an inflammation study, activated microglia showed up as a minor subcluster within the main microglia cluster. Pseudo-bulk analysis missed the NF-κB peaks that were present only in the small activated subset. These were only detected when we used per-cell differential accessibility analysis instead.
What We Do Differently
We apply stratified aggregation - weighing per-cell contribution - and run both pseudo-bulk and cell-level DA testing. We also dissect clusters hierarchically, checking whether biological signals are uniform or hidden in substructure.
The Problem
If peak calling is done only on aggregated data, it often misses open chromatin regions active only in rare cell types or transient states. These peaks simply don’t reach the global threshold.
Why It Happens
Most peak callers use minimum read count or enrichment over background. For rare subpopulations, even biologically relevant peaks may never exceed that threshold in the aggregated pileup.
Real Example
In one differentiation dataset, progenitor cells showed a known enhancer upstream of SOX9, but it was absent from the master peak list. We discovered that this region had low counts globally but was strongly open in a small subcluster. It only emerged after per-cluster peak calling.
What We Do Differently
We call peaks separately in major clusters and merge the union set. Then we refine using cell-level accessibility patterns. This way, we preserve important regions that are specific to rare but meaningful cell states.
The Problem
Dimensionality reduction (e.g., using Latent Semantic Indexing, LSI) is highly sensitive to preprocessing. Over-filtering removes signal; under-filtering amplifies noise. Bad parameter choice leads to distorted clusters.
Why It Happens
LSI relies on term frequency–inverse document frequency (TF-IDF) normalization. If peaks are too sparse or too abundant, it warps variance structure. Default thresholds often fail on datasets with high heterogeneity or varying sequencing depths.
Real Example
A cell atlas project clustered all endothelial subtypes together, even though prior knowledge suggested three distinct types. After redoing LSI with peak sparsity filtering adjusted to exclude global housekeeper peaks, the clusters separated cleanly.
What We Do Differently
We tune binarization, peak filtering, and LSI components per dataset - not relying on fixed defaults. We also evaluate clustering robustness across different resolutions and peak subsets to ensure structure is real, not artifact.
scATAC-seq data is sparse - but insights should be clear. We ensure your analyses reveal true biology, not noise artifacts. Request a free consultation →
The Problem
Gene activity scores are often used as RNA-seq proxies - but they are not expression values. Misuse of these scores leads to wrong conclusions about gene regulation.
Why It Happens
Gene activity is inferred by summing accessibility within promoter + gene body or nearby peaks. This correlates only weakly with actual mRNA levels - especially in complex loci with distal regulation or overlapping enhancers.
Real Example
In one immune study, IL2RA gene appeared highly active in regulatory T-cells based on gene activity score. But mRNA was absent. Turns out, a nearby super-enhancer open in Tregs overlapped the gene body, inflating the accessibility estimate.
What We Do Differently
We use gene activity only as coarse guide. When interpreting regulation, we look directly at peak-level signals, linkage to distal regulatory elements, and correlation with TF motif accessibility - not just aggregate scores.
The Problem
Linking distal regulatory elements to promoters using co-accessibility or correlation often gives false signals - especially in sparse scATAC-seq data. Peaks may appear linked due to technical bias, not biology.
Why It Happens
Low depth and binary nature of data mean that many “co-accessible” peaks simply reflect shared coverage patterns from a few cells. Without normalization or replication, these links are hard to trust.
Real Example
A locus-level analysis in stem cells linked a distal enhancer to a gene based on Cicero co-accessibility. But when tested in CRISPR deletion, the enhancer had no effect. It was later found the co-accessibility was driven by barcode collisions in a subset of wells.
What We Do Differently
We filter co-accessible pairs by minimum shared fragment count, require replicate consistency, and use orthogonal validation (Hi-C or expression). We also prefer methods that model distance decay and correct for sparsity.
The Problem
TF motif footprints from scATAC-seq look attractive - but in reality, most are unreliable. Overinterpreting them can lead to misleading conclusions about transcription factor activity.
Why It Happens
True footprints require high read depth and fine resolution. In scATAC-seq, few reads cover each motif. Aggregation dilutes cell-type–specific signals. Footprint shape can also reflect Tn5 bias or sequence composition.
Real Example
In one cardiac dataset, a sharp footprint at GATA1 motif was claimed as TF binding. But this signal appeared even in control samples without GATA1 expression. It came from Tn5 sequence bias near the motif site.
What We Do Differently
We use motif enrichment over accessibility peaks, not footprinting, as the primary method. When footprinting is used, we normalize for GC content and sequence bias, and avoid interpreting weak or indirect patterns.
Even polished scATAC-seq results can be wrong. We stress-test peak calling, clustering, and gene activity to ensure your discoveries are reproducible. Request a free consultation →
The Problem
Many teams align scATAC-seq with scRNA-seq using label transfer - assuming direct correlation. But mismatches in resolution, timing, and signal type can make this misleading.
Why It Happens
Chromatin opens before gene is transcribed. Also, low-depth scRNA-seq misses many genes. Thus, label transfer may assign wrong identity if based on poor overlap between chromatin and expression signals.
Real Example
In a developing brain dataset, oligodendrocyte progenitors in scATAC-seq were labeled as astrocytes because of shared SOX9 accessibility. The correct label only emerged when using regulatory module-based integration, not gene activity alignment.
What We Do Differently
We integrate using joint topic models, cross-modal anchors, or multi-omics frameworks (e.g., MOFA+, Seurat v5) - not naive gene score mapping. We also verify key markers independently across modalities.
The Problem
scATAC-seq findings often lack follow-up validation. Peaks are called “novel” just because they appear in a figure - without replication, perturbation, or other evidence.
Why It Happens
scATAC-seq is expensive. Many teams stop at one replicate or skip RT-qPCR, CRISPR validation, or bulk ATAC for verification. This limits interpretability and can invite reviewer skepticism.
Real Example
A high-profile study reported enhancer reprogramming in immune cells based on scATAC-seq. But none of the key peaks were reproducible in bulk ATAC. The study was rejected and had to be redone with functional assays.
What We Do Differently
For any high-impact regulatory finding, we plan orthogonal validation - either with matched bulk ATAC-seq, CRISPRi, CUT&Tag, or reporter assays. We also require reproducibility across biological replicates and donors.
scATAC-seq data is extremely powerful - but also fragile. Missteps at any stage, from quality filtering to interpretation, can distort the whole story. Unfortunately, many teams don’t realize they’re doing something wrong until publication reviewers push back - or until the findings fail to replicate.
We’ve seen this happen many times. It’s painful and avoidable.
By applying robust QC, smarter peak calling, careful dimensionality reduction, and rigorous validation, we help researchers avoid these traps. And we don’t just run tools - we think through the biology, examine assumptions, and look at every result with expert eyes.
If your single-cell chromatin accessibility project is important, it deserves expert-level scrutiny. That’s what we do.
Don’t let technical artifacts mislead your biology. We specialize in rescuing scATAC-seq studies with noisy, sparse, or misleading outputs. Request a free consultation →