Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment

Abstract

Motivation: Despite the success of recent machine learning algorithms' applications to survival analysis, their black-box nature hinders interpretability, which is arguably the most important aspect. Similarly, multi-omics data integration for survival analysis is often constrained by the underlying relationships and correlations that are rarely well understood. The goal of this work is to alleviate the interpretability problem in machine learning approaches for survival analysis and also demonstrate how multi-omics data integration improves survival analysis and pathway enrichment. We use meta-learning, a machine-learning algorithm that is trained on a variety of related datasets and allows quick adaptations to new tasks, to perform survival analysis and pathway enrichment on pan-cancer datasets. In recent machine learning research, meta-learning has been effectively used for knowledge transfer among multiple related datasets.

Results: We use meta-learning with Cox hazard loss to show that the integration of TCGA pan-cancer data increases the performance of survival analysis. We also apply advanced model interpretability method called DeepLIFT (Deep Learning Important FeaTures) to show different sets of enriched pathways for multi-omics and transcriptomics data. Our results show that multi-omics cancer survival analysis enhances performance compared with using transcriptomics or clinical data alone. Additionally, we show a correlation between variable importance assignment from DeepLIFT and gene coenrichment, suggesting that genes with higher and similar contribution scores are more likely to be enriched together in the same enrichment sets.

Availability and implementation: https://github.com/berkuva/TCGA-omics-integration.

Figure 1.

Our meta-learning model’s parameters $\theta$ are trained over a distribution of source cancer types, and the corresponding loss L are generated. $\theta$ are optimized by gradient descent, or back-propagation of $\nabla L_{{\text{task}}_i}$

Figure 2.

The process of studying the correlation between genes with similarly high DeepLIFT contribution scores and their likelihood of being enriched together in the same STRING enrichment sets