Joint variational autoencoders for multimodal imputation and embedding.

ABSTRACT: Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint Variational Autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. Variational autoencoders learn the latent embeddings of each modali

SUBMITTER: Kalafut NC 

PROVIDER: S-EPMC11340721 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature