TransGeneSelector: using a transformer approach to mine key genes from small transcriptomic datasets in plant responses to various environments.

Abstract

Gene mining is crucial for understanding the regulatory mechanisms underlying complex biological processes, particularly in plants responding to environmental conditions. Traditional machine learning methods, while useful, often overlook important gene relationships due to their reliance on manual feature selection and limited ability to capture complex inter-gene regulatory dynamics. Deep learning approaches, while powerful, are often unsuitable for small sample sizes. This study introduces TransGeneSelector, the first deep learning framework specifically designed for mining key genes from small transcriptomic datasets. By integrating a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for sample generation and a Transformer-based network for classification, TransGeneSelector efficiently addresses the challenges of small-sample transcriptomic data, capturing both global gene regulatory interactions and specific biological processes. Evaluated in Arabidopsis thaliana, the model achieved high classification accuracy in predicting seed germination and heat stress conditions, outperforming traditional methods like Random Forest and Support Vector Machines (SVM). Moreover, Shapley Additive Explanations (SHAP) analysis and gene regulatory network construction revealed that TransGeneSelector effectively identified genes that appear to have upstream regulatory functions based on our analyses, enriching them in multiple key pathways which are critical for seed germination and heat stress response. RT-qPCR validation further confirmed the model's gene selection accuracy, demonstrating consistent expression patterns across varying germination conditions. The findings underscore the potential of TransGeneSelector as a robust tool for gene mining, offering deeper insights into gene regulation and organism adaptation under diverse environmental conditions. This work provides a framework that leverages deep learning for key gene identification in small transcriptomic datasets.