Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona.

ABSTRACT:

Motivation

Single-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Existing manifold alignment methods demonstrated the state-of-the-art performance on single-cell multi-omics data integration, but they are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure.

Results

In this study, we present Pamona, a partial Gromov-Wasserstein distance-based manifold alignment framework that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures across modalities. We formulate this task as a partial manifold alig

SUBMITTER: Cao K 

PROVIDER: S-EPMC8696097 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature