ScMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data.

ABSTRACT: The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it o

SUBMITTER: Jeong Y 

PROVIDER: S-EPMC11304929 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature