Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer

TL;DR

This paper introduces TransGeneSelector, a deep learning pipeline that effectively mines key regulatory genes from small transcriptome datasets, outperforming traditional methods and revealing genes crucial for seed germination.

Contribution

The study presents a novel multi-network deep learning approach combining data augmentation, filtering, and Transformer classification for gene mining in small samples.

Findings

Successfully classified seed states with performance comparable to Random Forests.

Identified upstream regulatory genes involved in seed germination.

Validated key genes' roles through KEGG analysis and RT-qPCR.

Abstract

Gene mining is an important topic in the field of life sciences, but traditional machine learning methods cannot consider the regulatory relationships between genes. Deep learning methods perform poorly in small sample sizes. This study proposed a deep learning method, called TransGeneSelector, that can mine critical regulatory genes involved in certain life processes using a small-sample transcriptome dataset. The method combines a WGAN-GP data augmentation network, a sample filtering network, and a Transformer classifier network, which successfully classified the state (germinating or dry seeds) of Arabidopsis thaliana seed in a dataset of 79 samples, showing performance comparable to that of Random Forests. Further, through the use of SHapley Additive exPlanations method, TransGeneSelector successfully mined genes involved in seed germination. Through the construction of gene regulatory networks and the enrichment analysis of KEGG, as well as RT-qPCR quantitative analysis, it was confirmed that these genes are at a more upstream regulatory level than those Random Forests mined, and the top 11 genes that were uniquely mined by TransGeneSelector were found to be related to the KAI2 signaling pathway, which is of great regulatory importance for germination-related genes. This study provides a practical tool for life science researchers to mine key genes from transcriptome data.