A benchmark study of deep learning-based multi-omics data fusion methods for cancer

作者：Dongjin Leng, Linyi Zheng, Yuqi Wen, Yunhao Zhang, Lianlian Wu, Jing Wang, Meihong Wang, Zhongnan Zhang, Song He, Xiaochen Bo · 发表于：Genome biology · 年份：2022 · DOI：10.1186/s13059-022-02739-2 · 被引用次数：161 · 研究领域：Bioinformatics and Genomic Networks、Ferroptosis and cancer prognosis、Single-cell and spatial transcriptomics

BACKGROUND: A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples. RESULTS: In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods' strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks. CONCLUSIONS: Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data ...