Unsupervised topological alignment for single-cell multi-omics integration.

ABSTRACT:

Motivation

Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging.

Results

In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally

SUBMITTER: Cao K 

PROVIDER: S-EPMC7355262 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature