From scATAC-seq to Gene Regulatory Networks: A Practical Guide to Inferring Regulatory Interactions

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-20

Key Takeaways

• scATAC-seq measures chromatin accessibility, providing insights into regulatory landscapes, but requires integration with other data types like gene expression (scRNA-seq/snRNA-seq) and transcription factor (TF) motif analysis to infer gene regulatory networks (GRNs).
• TF motif analysis, often using tools like chromVAR, quantifies TF activity by assessing the collective accessibility of motif-bearing regions across cells, offering a more robust measure than individual peak accessibility.
• Integrating scATAC-seq with scRNA-seq/snRNA-seq is crucial for resolving ambiguity, as it links TF activity to TF expression, with paired data (e.g., 10x Multiome) offering stronger evidence than unpaired data requiring computational alignment.
• GRN inference algorithms like SCENIC, GRNBoost2, SCRIP, ScReNI, and STREAM utilize TF activity scores, gene expression, and peak-to-gene linkages to predict regulatory edges with confidence scores, with validation often involving comparison to ChIP-seq references or perturbation experiments.
• The interpretation of inferred GRNs is limited by the correlational nature of the data; validation is essential, and understanding cell type specificity, developmental context, and disease state is critical for accurate biological interpretation.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) measures chromatin accessibility across thousands of individual cells, providing a direct window into the regulatory landscape that governs gene expression. Researchers use this data to infer gene regulatory networks (GRNs), which map how transcription factors control target genes. This guide covers the practical workflow from raw scATAC-seq data through network inference, with attention to quality control, tool selection, interpretation limits, and validation strategies. The intended reader is a biology student, researcher, or laboratory professional who has scATAC-seq data or is planning an experiment and needs to understand how to convert accessibility measurements into testable regulatory hypotheses.

What scATAC-seq Measures and Why It Matters for GRN Inference

Chromatin accessibility reflects the physical state of DNA packaging in the nucleus. Regions of open chromatin are generally permissive to transcription factor binding and regulatory activity, while closed regions are typically inaccessible to the protein complexes that drive transcription. scATAC-seq uses the Tn5 transposase enzyme to fragment and tag accessible DNA, and the resulting sequencing reads identify which genomic regions were open in each individual cell at the time of collection.

The connection between accessibility and gene regulation is not one-to-one. An accessible region near a gene may be a promoter, an enhancer, a repressor element, or a region with no regulatory function. Accessibility alone does not tell you which transcription factors are bound, whether the region is activating or silencing the target gene, or how strongly the regulatory interaction influences expression. This is why GRN inference from scATAC-seq data requires additional layers of information, including transcription factor motif analysis, integration with gene expression data, and often reference datasets from chromatin immunoprecipitation sequencing (ChIP-seq) experiments.

The core logic of GRN inference from scATAC-seq is straightforward. You identify accessible regions, scan those regions for known transcription factor binding motifs, link accessible regions to nearby genes, and then score the likelihood that a given transcription factor regulates a given gene based on the co-occurrence of motif matches, accessibility patterns, and expression correlations. The practical challenge is that each of these steps involves choices that materially affect the final network.

At a Glance: Key Decisions in the scATAC-seq to GRN Workflow

Core Principles of Regulatory Network Inference

Transcription Factor Motifs Are the Link Between Accessibility and Regulation

Transcription factors recognize specific DNA sequence patterns called motifs. When a region of chromatin is accessible and contains a motif for a particular transcription factor, that region is a candidate binding site. The presence of a motif is necessary but not sufficient for actual binding. Many accessible regions contain motif matches that are never occupied by the corresponding factor in the cell type being studied. This is because binding depends on factor abundance, cooperative interactions with other proteins, chromatin context, and the local epigenetic state.

Motif-based scoring methods such as chromVAR account for this uncertainty by comparing motif accessibility across cells. Instead of asking whether a single region is bound, these methods ask whether regions containing a given motif are systematically more accessible in some cells than in others. The resulting transcription factor activity score reflects the collective accessibility of all regions carrying that motif, which is more robust than looking at individual regions in isolation.

Integration with Gene Expression Resolves Ambiguity

Chromatin accessibility data alone cannot tell you whether a transcription factor is actually present in the cell. A factor may have extensive motif matches in accessible regions, but if the factor is not expressed, those regions are unlikely to be bound. Integrating scATAC-seq with single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) resolves this ambiguity by providing the expression state of each transcription factor in the same or matched cells.

The integration can be performed on paired data, where both modalities are measured from the same cell, or on unpaired data, where the two assays are performed on separate cell suspensions from the same sample. Paired data from platforms such as the 10x Multiome assay provides the strongest evidence because the accessibility and expression measurements come from the same individual cell. Unpaired data requires computational alignment of the two cell populations, which introduces additional uncertainty but is often the only option when samples were processed separately.

Regulatory Potential Models Link Accessible Regions to Genes

Once you have identified candidate transcription factor binding sites, you need to assign those sites to target genes. The simplest approach is to assign each accessible region to the nearest gene, but this ignores the fact that enhancers can act over long genomic distances and often skip over intervening genes. Regulatory potential models address this by weighting the contribution of each accessible region based on its distance to the transcription start site and the correlation between accessibility and gene expression across cells.

The SCRIP method exemplifies this approach by integrating scATAC-seq data with a large-scale transcription factor ChIP-seq reference to infer transcription factor binding activity and target genes at single-cell resolution. Instead of relying solely on motif matches, SCRIP uses experimentally determined binding sites from ChIP-seq experiments to define candidate regulatory regions, which improves the accuracy of transcription factor activity estimates compared to motif-based methods alone.

Practical Workflow for Building GRNs from scATAC-seq Data

Step 1: Preprocess Raw Sequencing Data

The first stage of any scATAC-seq analysis is converting raw sequencing reads into a peak-by-cell matrix. This involves aligning reads to the reference genome, removing duplicates, identifying accessible regions called peaks, and counting the number of fragments in each peak for each cell. The choice of alignment tool, peak caller, and genome build affects all downstream analysis, so these decisions should be documented carefully.

Community-maintained pipelines provide a reproducible starting point. The nf-core project offers standardized bioinformatics pipelines with documented usage and configuration options, which helps ensure that preprocessing steps are consistent across samples and laboratories. The Galaxy Training Network provides accessible tutorials for scATAC-seq analysis that walk through each preprocessing step with example data, making it a useful resource for researchers who are new to the workflow.

The output of preprocessing is a peak-by-cell matrix where each entry represents the number of sequencing fragments from a given cell that fall within a given peak. This matrix is the raw material for all downstream analysis, including quality control, cell clustering, and network inference.

Step 2: Apply Quality Control Filters

Quality control for scATAC-seq data is more complex than for scRNA-seq because the data are extremely sparse. Each cell typically has only a small fraction of the genome accessible, and most peaks have zero counts in most cells. The key quality metrics are the total number of unique fragments per cell, the fraction of fragments in peaks, the transcription start site enrichment score, and the ratio of reads in promoter regions to reads in other genomic regions.

Cells with very low fragment counts may be empty droplets or damaged cells. Cells with very high fragment counts may be doublets, where two cells were captured in the same droplet. The fraction of fragments in peaks distinguishes cells with genuine accessible chromatin from background contamination. The transcription start site enrichment score measures whether accessible regions are concentrated around gene promoters, which is a hallmark of intact nuclei.

The MOCHA framework demonstrates advanced statistical modeling of scATAC-seq data for functional genomic inference in large human cohorts, addressing the challenges of analyzing data from many samples with varying quality. For smaller studies, the ArchR package provides a comprehensive set of quality control tools and is widely used in the field.

Step 3: Cluster Cells and Identify Cell Types

After quality filtering, the next step is to cluster cells based on their accessibility profiles. This identifies groups of cells that share similar regulatory landscapes, which typically correspond to cell types or cell states. Clustering scATAC-seq data requires dimension reduction methods that account for the sparsity of the data, such as latent semantic indexing or topic modeling, instead of the principal component analysis commonly used for scRNA-seq.

Cell type identification can be performed by comparing the accessibility of marker gene promoters across clusters, by integrating with scRNA-seq data from the same sample, or by using reference atlases of known cell types. The choice of clustering resolution affects the granularity of the resulting cell types, and this in turn affects the GRN inference. Networks inferred at a coarse resolution may miss cell-state-specific regulation, while networks inferred at a very fine resolution may be based on too few cells to be reliable.

Step 4: Compute Transcription Factor Activity

Transcription factor activity scores quantify the collective accessibility of motif-bearing regions in each cell. The chromVAR method is the most widely used approach. It computes the deviation of motif accessibility from the expected value based on the average accessibility of matched regions, accounting for technical biases such as GC content and sequencing depth.

The choice of motif database matters. Different databases contain different collections of position weight matrices, and the coverage of transcription factors varies. Some databases focus on human and mouse factors, while others include factors from additional species. The background model used to compute expected accessibility also affects the results, and different tools use different strategies for selecting background regions.

The SCRIP method offers an alternative to motif-based activity scoring by using ChIP-seq reference data to define transcription factor binding sites. This approach showed improved performance in evaluating transcription factor binding activity compared to motif-based methods and reached higher consistency with matched transcription factor expression in the published evaluation.

Step 5: Integrate with Gene Expression Data

The integration of scATAC-seq with scRNA-seq or snRNA-seq data is a critical step for GRN inference. The integration can be performed at the level of cell clusters, where you compare average accessibility and average expression across cell types, or at the level of individual cells, where you match each accessibility profile to an expression profile.

For paired data from the same cell, the integration is straightforward because each cell has both measurements. For unpaired data, you need to align the two cell populations using methods such as canonical correlation analysis or mutual nearest neighbors. The quality of this alignment directly affects the accuracy of the regulatory relationships you infer, because mismatched cells will produce spurious correlations between accessibility and expression.

The ScReNI method was designed to analyze both paired and unpaired datasets for scRNA-seq and scATAC-seq. It uses a nearest neighbors algorithm to establish neighboring cells for each cell and a modified random forest to infer nonlinear regulatory relationships between gene expression and chromatin accessibility. The published evaluation showed that ScReNI outperformed existing cell-specific network inference methods in network-based cell clustering and demonstrated superior performance in inferring cell type-specific regulatory networks.

Step 6: Run Network Inference Algorithms

Network inference algorithms take the transcription factor activity scores, gene expression data, and peak-to-gene linkages as input and produce a set of regulatory edges with confidence scores. Each edge connects a transcription factor to a target gene, and the confidence score reflects the strength of evidence for that regulatory relationship.

The SCENIC workflow is the most widely used framework for GRN inference from single-cell data. It first uses GRNBoost2 or GENIE3 to identify candidate regulatory modules based on expression correlations between transcription factors and target genes, then refines these modules using motif analysis to retain only those edges where the target gene has a motif for the transcription factor in its regulatory regions. The final output is a set of regulons, each consisting of a transcription factor and its predicted target genes.

Other methods take different approaches. STREAM uses a Steiner forest problem model and hybrid biclustering to infer enhancer-driven gene regulatory networks from jointly profiled transcriptome and chromatin accessibility data. KEGNI uses a knowledge graph enhanced framework with a graph autoencoder to capture gene regulatory relationships from scRNA-seq data. MultiCausGRN uses a graph attention network with directed prior-guided learning to integrate curated regulatory edges into the inference process.

Step 7: Validate and Interpret the Network

The final step is to assess whether the inferred network makes biological sense. This involves checking whether known regulators of the cell types under study appear as high-confidence nodes, whether the predicted target genes are consistent with published literature, and whether the network structure reflects known regulatory logic such as feedback loops and feed-forward motifs.

Experimental validation is the gold standard. The SC-MO-GRN-DB repository provides experimentally validated GRNs with over 22 million regulatory edges curated from high-confidence experimental datasets, along with tissue-matched single-cell multiomic datasets for benchmarking. Comparing your inferred network against these ground truth networks provides a quantitative measure of accuracy.

Perturbation experiments provide the strongest validation. The approach described in the BIBM 2024 paper uses genetically perturbed scATAC-seq data to infer GRNs, integrating pre- and post-perturbation chromatin accessibility data to construct networks that reflect the dynamic regulatory landscape. CRISPR interference can validate specific enhancer-gene relationships, as demonstrated in the kidney organoid study where an enhancer driving transcription of HNF1B was validated by CRISPR interference both in cultured proximal tubule cells and during organoid differentiation.

Tool Options and Tradeoffs

SCENIC and GRNBoost2

SCENIC is the most established framework for GRN inference from single-cell data. It combines expression-based correlation with motif analysis to produce regulons that are interpretable and testable. The main limitation is that it was designed primarily for scRNA-seq data, and the integration with scATAC-seq requires additional steps to incorporate accessibility information.

GRNBoost2 is the regression-based component of SCENIC that identifies candidate regulatory relationships. It is fast and scalable to large datasets, but it does not incorporate chromatin accessibility information directly. The motif refinement step in SCENIC partially addresses this by filtering edges based on motif presence, but the initial candidate edges are based on expression alone.

SCRIP

SCRIP integrates scATAC-seq with a large-scale transcription factor ChIP-seq reference to infer transcription factor binding activity and target genes. The use of experimentally determined binding sites instead of motif predictions improves accuracy, but the method depends on the availability of ChIP-seq data for the transcription factors and cell types of interest. For factors without ChIP-seq coverage, the method cannot provide binding information.

ScReNI

ScReNI infers cell-specific regulatory networks by integrating scRNA-seq and scATAC-seq data using a nearest neighbors algorithm and modified random forest. It handles both paired and unpaired datasets and provides the unique function of identifying cell-enriched regulators based on each cell-specific network. The method is newer than SCENIC and has been evaluated primarily on the datasets described in the original publication.

STREAM

STREAM infers enhancer-driven gene regulatory networks from jointly profiled transcriptome and chromatin accessibility data. It uses a Steiner forest problem model, hybrid biclustering, and submodular optimization to identify transcription factor-enhancer-gene relationships. The method demonstrated enhanced performance in transcription factor recovery, transcription factor-enhancer linkage prediction, and enhancer-gene relation discovery in the published evaluation.

KEGNI

KEGNI uses a knowledge graph enhanced framework with a graph autoencoder to infer GRNs from scRNA-seq data. The knowledge graph provides prior information about known regulatory relationships, which improves performance compared to methods that do not use prior knowledge. The modular design supports the integration of various knowledge graphs for context-specific tasks, and the method can also incorporate paired scRNA-seq and scATAC-seq data.

scMTNI

scMTNI is a multi-task learning framework that infers cell type-specific GRNs on cell lineages from scRNA-seq and scATAC-seq data. It is designed to model network dynamics along developmental trajectories, making it useful for studying differentiation and reprogramming. The method accurately infers GRN dynamics and identifies key regulators of fate transitions for linear and branching lineages.

Records and Measurements for Reproducible Analysis

Documenting the Analysis Pipeline

Reproducibility requires detailed documentation of every analysis step. This includes the software versions, parameter settings, reference genome build, motif database version, and quality control thresholds. Containerized workflows such as those provided by nf-core help ensure that the analysis environment is consistent across runs and across laboratories.

The Carpentries lessons provide foundational training in computing, data management, shell, Git, and programming that is directly applicable to building reproducible bioinformatics workflows. Version control of analysis scripts and parameter files is essential for tracking changes and reproducing results.

Key Metrics to Record

The following metrics should be recorded for each scATAC-seq dataset and each network inference run:

Common Failure Patterns and How to Address Them

Failure pattern 1: Low cell yield after quality control. If more than half of the cells are removed during QC, the remaining cells may not represent the full biological diversity of the sample. This can happen when the tissue dissociation protocol damages nuclei, when the sequencing depth is too low, or when the QC thresholds are too stringent. Address this by reviewing the QC metric distributions, adjusting thresholds based on the data instead of using default values, and considering whether the sample preparation protocol needs optimization.

Failure pattern 2: Transcription factor activity scores do not correlate with expression. If a transcription factor has high activity scores but low expression in the same cells, the motif analysis may be detecting binding sites for a different factor with a similar motif, or the factor may be regulated post-transcriptionally. Check whether the motif is specific to the factor of interest, whether the factor has known protein isoforms with different DNA binding specificities, and whether the expression data are reliable for that gene.

Failure pattern 3: The inferred network contains many edges that are not supported by literature. This is expected to some degree because GRN inference often identifies novel regulatory relationships. However, if the network contains edges that contradict known biology, such as a transcription factor regulating a gene that is not expressed in the same cell type, the inference parameters may need adjustment. Review the motif filtering thresholds, the peak-to-gene linkage distance, and the expression correlation cutoff.

Failure pattern 4: Networks differ dramatically between tools. Different inference algorithms make different assumptions about the relationship between accessibility and expression, so some variation is expected. If the networks are completely different, the input data may have issues, or the tools may be using different subsets of the data. Compare the transcription factor activity scores across tools to identify whether the discrepancy originates in the activity calculation or the network inference step.

Failure pattern 5: The network cannot be validated experimentally. If perturbation experiments do not confirm the predicted regulatory relationships, the network may contain many false positives. This is a common outcome because accessibility and expression correlations do not always reflect direct regulatory interactions. Consider using a more stringent confidence threshold, integrating additional data types such as chromatin conformation, or focusing on the highest-confidence edges for experimental validation.

Interpretation Limits and Biological Context

Correlation Does Not Equal Causation

The fundamental limitation of GRN inference from scATAC-seq data is that the resulting networks are correlational, not causal. An edge between a transcription factor and a target gene means that the accessibility of regions containing the factor's motif correlates with the expression of the target gene across cells. This correlation could arise from direct regulation, but it could also arise from indirect effects, shared upstream regulators, or technical artifacts.

The study of synovial fibroblasts in rheumatoid arthritis illustrates the value of integrating multiple data types to strengthen causal inference. The researchers combined scRNA-seq and scATAC-seq data with velocity and trajectory analysis to identify cellular differentiation lineages and infer gene regulatory networks using ArchR and custom algorithms. They then validated conserved gene regulatory networks using the SCENIC algorithm in a human arthritic scRNA-seq atlas. This multi-layered approach provides stronger evidence for regulatory relationships than any single analysis.

Cell Type Specificity Is Both a Strength and a Limitation

GRNs inferred from scATAC-seq data are cell type-specific by construction, because the accessibility and expression data are measured at single-cell resolution. This is a major advantage over bulk approaches, which average across heterogeneous cell populations. However, cell type-specific networks require sufficient cells per cell type to be reliable. Rare cell types may have too few cells for robust network inference, and the networks for these populations will be noisy.

The study of T helper 17 cell heterogeneity demonstrates the power of cell type-specific network inference. By integrating scATAC-seq and scRNA-seq data, the researchers inferred self-reinforcing and mutually exclusive regulatory networks controlling different cell states and predicted transcription factors regulating T helper 17 cell pathogenicity. They validated that BACH2 promotes immunomodulatory nonpathogenic T helper 17 programs and restrains proinflammatory T helper 1-like programs both in vitro and in vivo. This level of regulatory resolution is only possible with single-cell data.

Developmental and Disease Context Shapes Network Interpretation

The regulatory landscape changes dynamically during development and disease. A GRN inferred from a snapshot of cells at one time point reflects the regulatory state at that moment, not the full range of possible states. Trajectory analysis can partially address this by ordering cells along developmental paths, and methods such as scMTNI are designed to infer GRN dynamics along lineages.

The kidney organoid study provides an example of using GRN inference to benchmark differentiation progress. By comparing the cell-specific gene regulatory landscape during organoid differentiation with human adult kidney, the researchers identified more broadly open chromatin in organoid cell types and inferred enhancer dynamics by cis-coaccessibility analysis. This approach provides an experimental framework to judge the cell-specific maturation state of human kidney organoids and shows that organoids can be used to validate individual gene regulatory networks that regulate differentiation.

Emerging Methods for Multi-Omic Integration

Recent developments in generative modeling and knowledge-guided inference are expanding the options for GRN construction from multi-omic data. The scDiffusion-X model uses a latent diffusion approach with dual-cross-attention to integrate multi-omics data and can infer cell-type-specific heterogeneous GRNs through a gradient-based interpretation framework. The LAIOR framework applies hyperbolic geometry and neural ODE regularization to learn embeddings that preserve both local cell-state structure and global developmental trajectories across scRNA-seq and scATAC-seq datasets.

These methods are useful when you need to generate realistic multi-omics data for simulation studies or when you want to translate between modalities, such as predicting chromatin accessibility from gene expression. The SC-MO-GRN-DB repository provides standardized input datasets paired with experimentally supported ground-truth networks across six molecular modalities, including scRNA-seq, scATAC-seq, scChIP-seq, DNA methylation, chromatin conformation, and gene perturbation screens. This resource supports benchmarking of new inference methods against experimentally validated regulatory edges.

Quality Controls and Professional Escalation Criteria

When to Escalate to a Bioinformatics Specialist

Some analysis challenges require expertise beyond what a typical biology laboratory can provide. Escalate to a bioinformatics specialist or core facility when you encounter any of the following situations:

Situation 1: The data quality is poor and you cannot determine whether the problem is in the sample preparation or the sequencing. A bioinformatics specialist can help diagnose whether the issue is biological, technical, or computational by examining the raw data quality metrics and comparing them to expected distributions.

Situation 2: The integration of scRNA-seq and scATAC-seq data produces poor alignment between the modalities. This can happen when the two datasets come from different samples, when the cell type composition differs between the assays, or when the computational integration parameters are not appropriate for the data. A specialist can help troubleshoot the integration and determine whether the data are suitable for joint analysis.

Situation 3: The network inference results are highly unstable across parameter settings. If small changes in thresholds produce dramatically different networks, the data may not contain enough signal for reliable inference, or the inference algorithm may not be appropriate for the data structure. A specialist can help assess whether the data support network inference and recommend alternative approaches.

Situation 4: You need to validate the network experimentally but do not know which edges to prioritize. A specialist can help rank the edges by confidence, identify which edges are supported by independent evidence, and design validation experiments that test the most important regulatory relationships.

Safety and Ethical Considerations

scATAC-seq experiments involve the use of human or animal tissues, and all work must comply with institutional review board approvals and animal care regulations. The computational analysis does not raise direct safety concerns, but the interpretation of results can have implications for human health, particularly when the research involves disease-associated cell types or genetic variants.

The study of BACH2 in T helper 17 cells illustrates the translational potential of GRN inference. Human genetics implicate BACH2 in multiple sclerosis, and the identification of BACH2 as a regulator of T helper 17 cell pathogenicity suggests potential therapeutic targets. When research has this type of translational potential, the interpretation of results should be careful and measured, and claims about therapeutic implications should be supported by direct experimental evidence.

A Decision Framework for Selecting GRN Inference Tools Based on Data Structure and Biological Question

The choice of GRN inference method is often presented as a matter of personal preference or familiarity, but the structure of your data and the specific biological question you are asking should drive the selection. Different methods make different assumptions about the relationship between chromatin accessibility and gene expression, and these assumptions determine which regulatory relationships the method can detect. This section provides a practical decision framework that connects data characteristics to method selection, along with a record system for tracking the choices that affect network interpretation.

Matching Method Selection to Data Structure

The first decision point is whether your scATAC-seq data is paired with scRNA-seq from the same cells or whether you have unpaired datasets. Paired data, where both modalities are measured from the same individual cell, supports methods that model regulatory relationships at single-cell resolution. Unpaired data requires methods that can align two cell populations computationally, which introduces uncertainty that some algorithms handle better than others.

ScReNI was explicitly designed to analyze both paired and unpaired datasets. The method uses a nearest neighbors algorithm to establish neighboring cells and a modified random forest to infer nonlinear regulatory relationships between gene expression and chromatin accessibility. If you have unpaired data, this method is a stronger candidate than methods that assume matched measurements.

The second decision point is whether you are studying a homogeneous cell population or a heterogeneous sample with multiple cell types and developmental trajectories. For homogeneous populations, simpler methods that focus on transcription factor activity and target gene prediction may be sufficient. For heterogeneous samples with lineage structure, methods that model network dynamics along trajectories are more appropriate. scMTNI is a multi-task learning framework designed to infer cell type-specific GRNs on cell lineages, making it suitable for differentiation and reprogramming studies where you need to understand how regulatory relationships change as cells transition between states.

The third decision point is whether you have prior knowledge about the regulatory relationships in your system. If you have curated regulatory edges from literature or databases, methods that incorporate this information can improve accuracy. KEGNI uses a knowledge graph enhanced framework with a graph autoencoder to capture gene regulatory relationships, and its modular design supports the integration of various knowledge graphs for context-specific tasks. MultiCausGRN incorporates directed prior-guided graph attention learning to integrate curated regulatory edges into graph representation learning, which improved predictive stability in the published evaluation on human PBMC multi-omics data.

A Practical Decision Matrix for Method Selection

Recording Method Selection and Rationale

Documenting why you chose a particular method is as important as documenting how you ran it. The following record fields should be completed for each network inference run:

Troubleshooting Method-Specific Failures

Different methods fail in characteristic ways, and recognizing these patterns helps you diagnose problems quickly.

Pattern 1: SCENIC produces regulons that are too large or too small. SCENIC first uses GRNBoost2 or GENIE3 to identify candidate regulatory modules based on expression correlations, then refines these modules using motif analysis. If the regulons are too large, the expression correlation threshold may be too permissive, or the motif refinement step may be retaining edges where the motif match is weak. If the regulons are too small, the correlation threshold may be too stringent, or the motif database may lack coverage for the transcription factors of interest. Review the distribution of regulon sizes across transcription factors and adjust the thresholds accordingly.

Pattern 2: SCRIP cannot provide binding information for transcription factors without ChIP-seq coverage. SCRIP relies on a large-scale transcription factor ChIP-seq reference to define binding sites. For factors without ChIP-seq data, the method cannot provide binding information. If your transcription factors of interest lack ChIP-seq coverage, consider using a motif-based method such as chromVAR for those factors, or check whether the ChIP-seq reference has been updated with new data.

Pattern 3: STREAM identifies enhancer-gene relationships that do not match known biology. STREAM uses a Steiner forest problem model and hybrid biclustering to infer enhancer-driven networks. If the enhancer-gene relationships do not match known biology, the peak-to-gene linkage distance may be too permissive, or the biclustering parameters may be grouping cells in ways that do not reflect biological states. Review the biclustering output to check whether the cell groupings correspond to known cell types.

Pattern 4: Knowledge-guided methods produce networks that are heavily biased toward prior knowledge. Methods like KEGNI and MultiCausGRN incorporate curated regulatory edges, which improves accuracy but can also bias the network toward known relationships. If the network contains few novel edges, the prior knowledge weight may be too high. Reduce the influence of the knowledge graph or curated edges and check whether the network still identifies known regulators.

Benchmarking Against Ground Truth Networks

The SC-MO-GRN-DB repository provides experimentally validated GRNs with over 22 million regulatory edges curated from high-confidence experimental datasets, along with tissue-matched single-cell multiomic datasets across human and mouse tissues. This resource supports benchmarking of inference methods against experimentally supported ground truth networks.

When benchmarking, use the same input data for all methods you are comparing. The repository provides standardized input datasets paired with experimentally supported ground-truth networks across six molecular modalities, including scRNA-seq, scATAC-seq, scChIP-seq, DNA methylation, chromatin conformation, and gene perturbation screens. This standardization ensures that differences in performance reflect method capabilities instead of differences in data preprocessing.

Calculate precision and recall for each method by comparing inferred edges against the ground truth network. Precision measures the fraction of inferred edges that are supported by experimental evidence, while recall measures the fraction of experimentally validated edges that the method recovers. The optimal method for your application depends on whether you prioritize precision or recall. If you are planning experimental validation, prioritize precision to reduce the number of false positives you need to test. If you are exploring novel regulatory relationships, prioritize recall to maximize the number of candidate edges for follow-up.

Escalation Criteria for Method Selection and Interpretation

Escalate to a bioinformatics specialist when you encounter any of the following situations:

Situation 1: Multiple methods produce networks with very low overlap. If the overlap between networks from different methods is below 20 percent of the total edges, the data may not contain enough signal for reliable inference, or the methods may be using fundamentally different subsets of the data. A specialist can help diagnose whether the issue is in the input data or the method assumptions.

Situation 2: The network inference results are highly sensitive to small changes in parameter settings. If changing a threshold by 10 percent produces a network with substantially different topology, the data may not support robust inference. A specialist can help assess whether the data quality is sufficient and recommend alternative approaches.

Situation 3: You need to integrate more than two modalities. If you have additional data types such as scChIP-seq, DNA methylation, or chromatin conformation data, the integration becomes more complex. The SC-MO-GRN-DB repository provides resources for multi-modal integration, but a specialist can help design an analysis plan that appropriately weights each data type.

Situation 4: The biological question requires causal inference. GRN inference from scATAC-seq data produces correlational networks. If you need to establish causal regulatory relationships, you will need perturbation experiments or methods that integrate gene perturbation data. The BIBM 2024 approach uses genetically perturbed scATAC-seq data to infer GRNs, integrating pre- and post-perturbation chromatin accessibility data. A specialist can help design the perturbation experiment and select appropriate analysis methods.

Frequently Asked Questions

What is the minimum number of cells needed for reliable GRN inference from scATAC-seq data?

The minimum number of cells depends on the number of cell types in the sample and the complexity of the regulatory landscape. For a homogeneous cell population, a few thousand cells may be sufficient. For heterogeneous samples with multiple cell types, you need enough cells per cell type to support robust correlation analysis, typically at least a few hundred cells per cluster. The sparsity of scATAC-seq data means that more cells are generally better, and methods that borrow information across cells, such as chromVAR activity scoring, are more robust with larger cell numbers.

Can I infer GRNs from scATAC-seq data alone without scRNA-seq data?

Yes, but the networks will be less reliable because you cannot confirm that the transcription factors are expressed in the cells where their motifs are accessible. Motif-based activity scores provide indirect evidence of transcription factor activity, but expression data provide direct confirmation. If you only have scATAC-seq data, you can use reference expression atlases to check whether the transcription factors of interest are expressed in the relevant cell types, but this is less reliable than measuring expression in the same cells.

How do I choose between paired and unpaired scRNA-seq and scATAC-seq data?

Paired data, where both modalities are measured from the same cell, provides the strongest evidence for regulatory relationships because the accessibility and expression measurements come from the same individual cell. Unpaired data requires computational alignment of the two cell populations, which introduces uncertainty. If you are designing a new experiment, paired data from platforms such as the 10x Multiome assay is the preferred choice. If you are working with existing data, unpaired integration is feasible but requires careful validation of the alignment.

What is the difference between a regulon and a gene regulatory network?

A regulon is a set of genes that are predicted to be regulated by a single transcription factor. A gene regulatory network is the collection of all regulons and the interactions between them. SCENIC produces regulons as its primary output, and these can be combined to form a network. The distinction matters for interpretation because regulons focus on the targets of individual factors, while networks emphasize the connections between factors and the overall regulatory architecture.

How do I validate a predicted regulatory interaction experimentally?

The strongest validation is a perturbation experiment where you disrupt the transcription factor and measure the effect on the predicted target genes. CRISPR knockout or knockdown of the transcription factor followed by RNA sequencing or quantitative PCR of the target genes provides direct evidence of regulation. CRISPR interference can validate specific enhancer-gene relationships by disrupting the enhancer and measuring the effect on the target gene. Chromatin immunoprecipitation followed by sequencing can confirm that the transcription factor binds the predicted regulatory region.

What causes false positive edges in inferred GRNs?

False positives arise from several sources. Motif matches in accessible regions do not guarantee transcription factor binding. Expression correlations can reflect indirect regulation or shared upstream regulators. Peak-to-gene linkages based on distance can assign enhancers to the wrong genes. Technical artifacts such as batch effects and sequencing depth variation can create spurious correlations. Using multiple lines of evidence, including motif analysis, expression correlation, and prior knowledge, reduces but does not eliminate false positives.

How do I compare my inferred network with published networks?

The SC-MO-GRN-DB repository provides experimentally validated GRNs with over 22 million regulatory edges curated from high-confidence experimental datasets, along with tissue-matched single-cell multiomic datasets. You can compare your inferred edges against these ground truth networks to calculate precision and recall. You can also check whether your network recovers known regulators of the cell types under study by comparing against published literature and databases of transcription factor targets.

What should I do if my network inference results are not reproducible across runs?

Non-reproducible results usually indicate that the analysis pipeline is not fully documented or that the inference algorithm has stochastic components. Ensure that all random seeds are fixed, all software versions are recorded, and all parameter settings are documented. Containerized workflows such as those provided by nf-core help ensure reproducibility. If the results are still not reproducible, the data may not contain enough signal for reliable inference, and you should consider whether the sample size is adequate.

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• RNA-Seq vs Microarray: Choosing the Right Gene Expression Profiling Platform
• Gene Set Enrichment Analysis in R: A Practical Tutorial for Interpreting Omics Data
• Metabolomics Data Analysis in R: A Practical Workflow
• Microbiome Data Analysis in R: A Practical Guide for Compositional Data

Related Clinical & Scientific Guides

• A Practical Guide to Detecting Antimicrobial Resistance Genes in Shotgun Metagenomic Data
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References and Further Reading

• NCBI Data Resources. National Center for Biotechnology Information.
• EMBL-EBI Training. European Bioinformatics Institute.
• Bioconductor. Bioconductor Project.
• Galaxy Training Network. Galaxy Project.
• nf-core Documentation. nf-core.
• The Carpentries Lessons. The Carpentries.
• A single-cell multiomic analysis of kidney organoid differentiation.. Proceedings of the National Academy of Sciences of the United States of America, 2023.
• ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.. Genomics, proteomics & bioinformatics, 2025.
• BACH2 regulates diversification of regulatory and proinflammatory chromatin states in T(H)17 cells.. Nature immunology, 2024.
• Single-cell multimodal analysis identifies common regulatory programs in synovial fibroblasts of rheumatoid arthritis patients and modeled TNF-driven arthritis.. Genome medicine, 2022.
• KEGNI: knowledge graph enhanced framework for gene regulatory network inference.. Genome biology, 2025.
• Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.. Briefings in bioinformatics, 2024.
• Single-cell gene regulation network inference by large-scale data integration.. Nucleic acids research, 2022.
• LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.. 2026.
• A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.. 2026.
• Single-Cell Multi-Omics Reveal Gene Regulatory Mechanisms Underlying Cardiac Embryonic Development.. 2026.
• SC-MO-GRN-DB: A comprehensive repository for single-cell multiomic gene regulatory networks.. 2026.
• Inferring Gene Regulatory Network Based on scATAC-seq Data with Gene Perturbation. bioRxiv, 2024.
• MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference. Frontiers in Bioinformatics, 2026.
• Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. bioRxiv, 2023.
• MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts. Nature Communications, 2024.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.