Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

ABSTRACT:

Motivation

Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy.

Results

We propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using va

SUBMITTER: Yu L 

PROVIDER: S-EPMC10287920 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature