The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.

ABSTRACT: Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the existing literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider eight popular tools in mor

SUBMITTER: Brombacher E 

PROVIDER: S-EPMC9643784 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature