Subtype-DCGCN: an unsupervised approach for cancer subtype diagnosis based on multi-omics data

Yabin Kuang, Minzhu Xie

Abstract: Identifying cancer subtypes is an essential component of precision medicine, as it helps researchers develop more precise treatment methods and prevention strategies. Meanwhile, high-throughput sequencing technologies have produced a huge amount of multi-omics data for cancer patients and make it is practical to subtype cancers using multi-omics data. As existing cancer subtyping computational models based on multi-omics data could not effectively extended to weakly paired omics data, we proposed a novel unsupervised cancer subtyping model Subtype-DCGCN. Subtype-DCGCN uses Dual Contrast Graph Convolutional Networks guided by dual contrastive learning to lean low dimensional features for each type omics data, and with weighted average fusion Subtype-DCGCN could deal well with weakly paired multi-omics data. Extensive experiments on benchmark datasets showed that Subtype-DCGCN exhibited superior performance to other eight state-of-the-art similar methods in general to identify cancer subtypes. Moreover, tests on simulated datasets with varying missing rate showed that Subtype-DCGCN performed pretty well on weakly paired omics datasets.