DeepGSEA – explainable deep gene set enrichment analysis for single-cell transcriptomic data

Gene set enrichment (GSE) analysis is a crucial tool for interpreting gene expression data. It helps scientists understand different phenotypes—observable traits or characteristics—by comparing gene expression data to pre-defined gene sets (groups of genes known to be associated with specific functions or pathways).

The Challenge with Single-Cell RNA Sequencing

With the advent of single-cell RNA sequencing (scRNA-seq), which allows us to study gene expression at the individual cell level, GSE analysis has become even more important. This technology provides detailed insights into cellular diversity and complexity, but it also introduces challenges. The primary issue is cellular heterogeneity—each cell’s gene expression profile can be vastly different, making it difficult for traditional statistical methods to identify enriched gene sets accurately.

The Rise of Deep Learning in Gene Analysis

Deep learning has shown promise in various applications, such as clustering and trajectory inference in single-cell studies, because it can capture complex data patterns. However, using deep learning for GSE analysis has been limited due to challenges in interpretability—it’s hard to understand how these models make decisions.

Introducing DeepGSEA

Researchers at the University of Virginia have developed DeepGSEA, a new approach that leverages deep learning for GSE analysis while maintaining interpretability.

Here’s how it works:

  • Prototype-Based Neural Networks: DeepGSEA uses a special type of neural network that is designed to be interpretable. These networks use “prototypes” or representative examples from the data to make predictions, making it easier to understand the model’s reasoning.
  • Classification Tasks: The model learns to capture GSE information through designed classification tasks, helping it identify which gene sets are enriched in the data.
  • Significance Tests: After learning, DeepGSEA performs significance tests on each gene set to determine which ones are genuinely enriched.
  • Visualization: One of the key strengths of DeepGSEA is its ability to visualize the results. It provides explicit visualizations of the underlying distribution of a gene set using encoded cell and cellular prototype embeddings.

Performance and Benefits

DeepGSEA has been tested against commonly used GSE analysis methods through four simulation studies and three real scRNA-seq datasets. The results show that DeepGSEA is both sensitive (able to detect true positives) and specific (able to avoid false positives). Additionally, the interpretability of DeepGSEA allows researchers to explain its results, making it a powerful tool for GSE analysis in single-cell studies.

Conclusion

DeepGSEA represents a significant advancement in the field of gene set enrichment analysis, particularly for single-cell RNA sequencing data. By combining the strengths of deep learning with a focus on interpretability, DeepGSEA provides a robust and understandable approach to identifying enriched gene sets. This tool will enable scientists to gain deeper insights into cellular functions and phenotypes, potentially leading to new discoveries in genetics and molecular biology.

Availability – https://github.com/Teddy-XiongGZ/DeepGSEA

Xiong G, John LeRoy N, Bekiranov S, Sheffield N, Zhang A. DeepGSEA: (2024) Explainable Deep Gene Set Enrichment Analysis for Single-cell Transcriptomic Data. Bioinformatics [Epub ahead of print]. [abstract]

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