Integrating different molecular layers, i.e., multiomics data, is crucial for unraveling the complexity of diseases; yet, most deep generative models either prioritize predictive performance at the expense of interpretability or enforce interpretability by linearizing the decoder, thereby weakening the network's nonlinear expressiveness. To overcome this tradeoff, we introduce POEMS: Product Of Experts for Interpretable Multiomics Integration using Sparse Decoding, an unsupervised probabilistic framework that preserves predictive performance while providing interpretability. POEMS provides interpretability without linearizing any part of the network by 1) mapping features to latent factors using sparse connections, which directly translates to biomarker discovery, 2) allowing for cross-omic associations through a shared latent space using product of experts model, and 3) reporting contributions of each omic by a gating network that adaptively computes their influence in the representation learning. Additionally, we present an efficient sparse decoder. In a cancer subtyping case study, POEMS achieves competitive clustering and classification performance while offering our novel set of interpretations, demonstrating that biomarker based insight and predictive accuracy can coexist in multiomics representation learning.

翻译：整合不同分子层面的多组学数据对于揭示疾病复杂性至关重要；然而，大多数深度生成模型要么以牺牲可解释性为代价优先考虑预测性能，要么通过线性化解码器来强制实现可解释性，从而削弱了网络的非线性表达能力。为克服这一权衡，我们提出了POEMS：基于稀疏解码的可解释多组学整合专家乘积模型，这是一种无监督概率框架，在保持预测性能的同时提供可解释性。POEMS通过以下方式实现可解释性而无需对网络任何部分进行线性化：1）利用稀疏连接将特征映射到潜在因子，这直接转化为生物标志物发现；2）通过专家乘积模型在共享潜在空间中实现跨组学关联；3）通过门控网络自适应计算各组学在表示学习中的贡献度来报告其影响。此外，我们提出了一种高效的稀疏解码器。在癌症亚型分型的案例研究中，POEMS实现了具有竞争力的聚类和分类性能，同时提供了我们新颖的可解释性分析，证明了基于生物标志物的洞察力与预测准确性可以在多组学表示学习中并存。