Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment

Abstract

The tumor microenvironment (TME) profoundly influences tumor progression and affects immunotherapy responses and resistance. Understanding its heterogeneity is the key for developing immunotherapy. However, the available methods can only partially portray the TME heterogeneity with a small number of cell types. Here, we developed a deep learning-based frame with a design visible, DCNet, that embeds the relationships between cells and their marker genes in the neural network, and can infer the cell landscape with more than 400 cell types based on bulk RNA-seq data. DCNet accurately recapitulated the cell landscape of multiple single cell RNA-seq datasets, which showed better robustness and stability. Based on the cell landscape of TCGA patients, which was built with DCNet, the patients were divided into two groups with significant differences in survival time and distinct cell-type populations. DCNet provides a foundation for decoding TME heterogeneity. The source code of DCNet can be found on GitHub: https://github.com/xindd/DCNet.

Keywords

• Tumor microenvironment
• deep learning
• cell landscape