As spatial genomics technologies evolve, researchers face growing challenges in analyzing complex spatial transcriptomics data. This article provides a concise overview of key computational strategies—including data preprocessing, visualization, and machine learning (ML) applications—that are transforming how we interpret spatial gene expression patterns.

We explore common issues such as data sparsity, spatial autocorrelation, and high dimensionality, and highlight widely used tools like SpatialDE, TrendSEEK, and BISON. The role of ML in spatial domain detection, cell-type deconvolution, and multimodal integration is also discussed, along with best practices for reproducible workflows and model validation in spatial genomics.

Challenges in Spatial Transcriptomics Data: What Needs to Be Addressed Before Interpretation

Analyzing spatial transcriptomics data isn't just a matter of loading files and running pipelines—it requires a clear understanding of the data's limitations from the start. Several recurring issues often affect downstream interpretation and need to be carefully managed.

Design overview and core functionality of SpatialData. (Marconato, L.et al., Nature methods, 2025).

High Dimensionality

Spatial transcriptomics captures expression for thousands of genes across many spatial locations. That's a huge amount of data, and without some form of dimensionality reduction, the signal is difficult to interpret.

Practical approach: Use PCA for quick screening; UMAP or t-SNE for more nuanced visualizations of spatial clusters or gradients.

Spatial Dependency

Expression levels are often correlated between nearby spots due to tissue architecture. Ignoring this leads to misleading results.

Toolkits like SpatialDE or SpaNorm are designed to model this spatial structure, making it easier to pick out genes with true location-specific expression.

Sparsity and Dropout Effects

Especially in low-depth datasets, many spots contain only partial gene information—or none at all. This sparsity complicates analyses like clustering or differential expression.

Solutions: Apply QC tools such as SpatialQC or SpotSweeper to identify low-confidence regions. Some researchers also use imputation methods to fill in gaps, though these require careful validation.

Doublets and Noise

Capture spots can occasionally merge signals from more than one cell, especially when resolution is low. This distorts gene expression and skews downstream results.

Example: Doublet detection tools like SCNT help flag and remove these problematic data points.

Uneven Sequencing Depth

Some tissue regions simply get sequenced more deeply than others. Without normalization, this results in false expression differences.

Normalization methods like SpaNorm can adjust for these technical biases and make comparisons across tissue more reliable.

Visualizing Spatial Data

Once the data is cleaned and normalized, visualization becomes the key to understanding what's actually going on in the tissue.

• Spatial heatmaps let you map expression back onto the tissue structure—ideal for spotting regional patterns.
• UMAP and t-SNE plots help condense the high-dimensional space into something visually interpretable, often revealing biologically meaningful clusters.

Core Tools for Spatial Transcriptomics Analysis

Extracting biological meaning from spatial transcriptomics data often depends on choosing the right computational tools. Over the past few years, several methods have become widely used for detecting spatial gene patterns, co-expression modules, and spatial domains. Below are four representative tools that cover different aspects of spatial analysis.

SpatialDE – Finding Genes with Spatial Structure

SpatialDE is one of the first tools developed specifically to detect spatially variable genes (SVGs). It uses Gaussian process regression to test whether a gene's expression varies in a spatially dependent way across tissue. This is useful when you don't want to rely on pre-defined regions but want to let the data speak for itself.

• It works across the entire transcriptome, unsupervised.
• It can group genes into spatial expression modules ("automatic histology").
• It handles complex, non-linear patterns well.

If you're trying to identify genes driving spatial heterogeneity in tissue, this is a solid starting point.

Method schematic of SPARK-X and simulation results. (Zhu, J., et al.,Genome biology, 2021)

TrendSEEK – Tracking Gradients and Local Hotspots

TrendSEEK focuses on identifying gradual trends or expression hotspots across tissue. Unlike clustering-based approaches, it models spatial patterns as point processes, which is helpful when gene expression changes gradually rather than in discrete regions.

• Captures both smooth gradients and sharp local enrichments.
• Works at single-cell resolution.
• Especially useful for developmental or morphogenetic studies.

If you're studying processes like differentiation or zonation, TrendSEEK provides finer detail than global clustering methods.

stIHC – Identifying Spatial Gene Modules

Sometimes the goal isn't just to find individual SVGs, but to uncover groups of genes that behave similarly in space—this is where stIHC comes in. It uses an iterative clustering strategy to detect spatial co-expression modules.

• Clusters genes based on their spatial similarity.
• Helps define functional zones or tissue microenvironments.
• Compatible with multiple platforms (e.g., 10x Visium, Xenium).

stIHC is useful when you want to understand higher-order structure in the data, like regional specialization or coordinated gene programs.

BISON – Clustering While Selecting Informative Features

BISON takes a different approach. It combines clustering with feature selection using a Bayesian model. It doesn't just group similar spots or genes—it also tells you which genes are most responsible for defining those groups.

• Simultaneously clusters tissue regions and gene features.
• Reduces noise by filtering out uninformative genes.
• Useful for datasets where traditional clustering overfits.

BISON is well-suited for studies aiming to define distinct spatial domains and prioritize key marker genes that drive region-specific identity.

Machine Learning in Spatial Genomics: What It Can Do—and Where It Falls Short

Machine learning (ML) has quickly become a core component of spatial genomics analysis. It offers powerful ways to extract patterns from complex, high-dimensional datasets—but its applications require careful consideration. In this section, we look at where ML adds value, and what limitations still exist when applying these models to spatial data.

The workflow for spatial transcriptomics (ST) and hierarchical clustering results. (Lv, J., et al., Cell Death & Disease, 2021)

Where Machine Learning Helps

1. Detecting Spatial Expression Patterns

ML models—especially deep learning frameworks—can identify subtle spatial expression trends that traditional statistical methods might miss. Convolutional neural networks (CNNs), for example, are good at recognizing spatial hierarchies and detecting gene expression domains that align with tissue architecture.

The spatial distribution of stromal regions affects the gene expression of IMPC regions. (Lv, J., et al., Cell Death & Disease, 2021)

2. Cell Type Deconvolution

Most spatial transcriptomics platforms lack single-cell resolution, meaning each spot may contain a mixture of cell types. ML techniques such as autoencoders or graph neural networks (GNNs) help disentangle these mixtures by learning latent structures in the data. This enables more accurate mapping of cell populations across tissue.

3. Integrating Multimodal Data

Spatial genomics increasingly involves not just transcriptomic data, but also histology images, epigenetic marks, or proteomics. ML models—especially transfer learning frameworks—can integrate these modalities to improve resolution and interpretability. For instance, models like TransST use annotated single-cell data to refine spatial cluster definitions.

Interpretable deep dual-attention model for spatial multi-omics data analysis. (Long, Y., et al., Nature Methods, 2024).

Current Limitations and Challenges

Despite its promise, applying ML to spatial genomics still has practical hurdles.

1. Data Sparsity and Technical Noise

Spatial datasets often contain a high proportion of zeros—either due to dropout events or low capture efficiency. These gaps can mislead models or amplify noise unless preprocessing (e.g., imputation or denoising) is carefully applied.

2. High Computational Cost

Training deep learning models on spatial data can be resource-intensive, especially when working with full transcriptomes or large tissues. GPU-accelerated infrastructure is often required for timely analysis.

3. Lack of Interpretability

Many ML models, particularly deep networks, function as black boxes. For researchers looking to validate biological hypotheses, this can be frustrating. Interpretable ML (such as feature attribution methods or attention mechanisms) is gaining interest, but adoption in spatial analysis is still limited.

4. Poor Generalization Across Datasets

Models trained on one dataset often don't perform well on another, due to batch effects, platform differences, or biological variability. Domain adaptation and standardized benchmarks are needed to improve robustness.

Machine learning is a powerful addition to the spatial genomics toolkit—but it's not a plug-and-play solution. The best results come from combining domain knowledge, rigorous preprocessing, and validation strategies tailored to the research question.

Practical Strategies for Reliable Spatial Genomics Analysis

Machine learning can greatly enhance spatial genomics studies, but only when supported by robust workflows and data quality. Below are practical, research-tested strategies to ensure your analysis is both reliable and reproducible.

1. Match the Model to the Biological Question

Start by clearly defining your goal—classification, clustering, or data integration. Then, select an appropriate model based on that goal and the characteristics of your dataset.

• Supervised learning (e.g., Random Forest, SVM) is useful when labeled training data is available, such as known cell types or annotated tissue regions.
• Unsupervised methods (e.g., k-means, hierarchical clustering) help uncover new spatial patterns or unknown cell populations.
• Deep learning (e.g., CNNs, Transformers) is ideal for modeling complex spatial structures, but demands more data and computational resources.

Always balance model complexity with interpretability and data size.

2. Prioritize Quality Control (QC)

High-throughput spatial data is prone to noise, dropout, and coverage bias. Without QC, downstream insights may be misleading.

• Tools like SpatialQC and SpotSweeper help flag low-quality capture spots, artifacts, and coverage inconsistencies.
• Supplement automated QC with manual inspection—visualizing UMAPs, violin plots, or tissue maps often reveals issues algorithms miss.
• Normalize for depth and batch effects before modeling. Apply imputation techniques only if they clearly improve signal quality.

3. Build Reproducible Workflows

Reproducibility is essential for collaborative and scalable research.

• Containerization tools like Docker or Singularity preserve your computing environment for consistent results.
• Workflow engines such as Nextflow or Snakemake streamline multistep pipelines and make parameter tracking easier.
• Use version control (e.g., Git) to manage script changes and document every step of your pipeline for full transparency.

A reproducible analysis pipeline increases reliability and simplifies sharing and publication.

4. Integrate Data When Appropriate

Combining spatial data with other modalities can deepen biological insight, but also adds complexity.

• Use multimodal frameworks like Panpipes or MOSAIK to handle diverse inputs (e.g., histology, scRNA-seq, epigenomics).
• Apply harmonization techniques like canonical correlation analysis (CCA) or mutual nearest neighbors (MNN) to align datasets.
• Overlay results for interpretation—e.g., mapping gene expression onto tissue morphology to connect molecular and spatial features.

5. Validate All Results

Machine learning predictions need validation—statistically and biologically.

• Perform cross-validation (e.g., k-fold) to evaluate model stability and avoid overfitting.
• Use benchmark datasets to test tools and compare performance.
• Where possible, verify computational findings with biological assays (e.g., immunostaining or in situ hybridization) to confirm spatial localization.

Reliable spatial analysis depends on more than just choosing the right tool. It requires careful preparation, transparent workflows, and consistent validation to ensure that results hold up across datasets and biological systems.

Frequently Asked Questions: Tool Selection and Reproducible Analysis in Spatial Genomics

Researchers working with spatial genomics often face two recurring questions: "Which machine learning tool should I use?" and "How can I make my analysis reproducible?" Below, we address both with practical, lab-tested answers.

Q1: How do I choose the right machine learning tool for spatial transcriptomics data?

The right tool depends on the specific biological question you're asking. Here's a quick guide based on common goals:

• To detect spatially variable genes:

Use SpatialDE – it models gene expression variability across space and identifies genes with location-dependent patterns.

• To analyze spatial gradients or local hotspots:

Use TrendSEEK – it's designed to find smooth transitions or localized expression bursts in tissue sections.

• To identify spatial co-expression modules:

Use stIHC – it clusters genes based on shared spatial patterns, useful for defining functional gene programs.

• To uncover spatial domains and their defining features:

Use BISON – it combines biclustering and feature selection, helping you find meaningful regions and key marker genes.

When choosing tools, also consider factors such as:

• Dataset size and sparsity
• Compatibility with your platform (e.g., 10x Visium, Slide-seq)
• Computational requirements and interpretability
• Community adoption and documentation

There's no single best tool—let your biological question and data structure guide your decision.

Q2: How can I ensure that my spatial genomics analysis is reproducible?

Reproducibility is essential for credible spatial data analysis, especially when using machine learning. Here are some best practices:

• Standardize your workflow:

Use tools like Nextflow, Snakemake, or Galaxy to automate and document your pipeline. This makes it easy to track every processing step.

• Use version control:

Track all changes to scripts, tools, and parameters with Git. It helps you understand how your results evolved over time and supports collaboration.

• Encapsulate your environment:

Package your analysis using Docker or Singularity. This ensures your code runs identically across different systems and time points.

• Document everything:

Keep detailed records of tool versions, input files, QC thresholds, and parameter settings. Good documentation reduces ambiguity and supports long-term reproducibility.

Following these practices doesn't just help your own project—it makes your findings easier to share, publish, and validate by others.

Conclusion

Spatial genomics continues to push the boundaries of how we study gene regulation in tissue context. But extracting meaningful insights from these datasets requires more than advanced technology—it demands careful data processing, appropriate tool selection, and reproducible workflows. Whether you're identifying spatially variable genes, deconvoluting cell types, or integrating multimodal data, computational strategies and machine learning can dramatically expand the depth and precision of your analysis. By combining rigorous quality control with thoughtful model design and transparent practices, researchers can turn complex spatial data into reliable biological discoveries.

At CD Genomics, we offer end-to-end spatial transcriptomics and spatial epigenomics services—paired with expert bioinformatics support—to help you extract deeper insights from complex tissue data. Contact us today to discuss your project and explore customized analysis solutions.

References

1. Marconato, L., Palla, G., Yamauchi, K. A., Virshup, I., Heidari, E., Treis, T., ... & Stegle, O. (2025). SpatialData: an open and universal data framework for spatial omics. Nature methods, 22(1), 58-62.
2. Zhu, J., Sun, S., & Zhou, X. (2021). SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome biology, 22(1), 184.
3. Higgins, C., Li, J. J., & Carey, M. (2025). Spatial transcriptomics iterative hierarchical clustering (stIHC): A novel method for identifying spatial gene co‐expression modules. Quantitative Biology, 13(4), e70011.
4. Zhu, B., Cassese, A., Vannucci, M., Guindani, M., & Li, Q. (2025). BISON: Bi-clustering of spatial omics data with feature selection. arXiv preprint arXiv:2502.13453.
5. Lv, J., Shi, Q., Han, Y., Li, W., Liu, H., Zhang, J., ... & Fu, L. (2021). Spatial transcriptomics reveals gene expression characteristics in invasive micropapillary carcinoma of the breast. Cell Death & Disease, 12(12), 1095.
6. Long, Y., Ang, K. S., Sethi, R., Liao, S., Heng, Y., van Olst, L., ... & Chen, J. (2024). Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nature Methods, 21(9), 1658-1667.