Clustering single-cell multi-omics data with MoClust.

ABSTRACT:

Motivation

Single-cell multi-omics sequencing techniques have rapidly developed in the past few years. Clustering analysis with single-cell multi-omics data may give us novel perspectives to dissect cellular heterogeneity. However, multi-omics data have the properties of inherited large dimension, high sparsity and existence of doublets. Moreover, representations of different omics from even the same cell follow diverse distributions. Without proper distribution alignment techniques, clustering methods will encounter less separable clusters easily affected by less informative omics data.

Results

We developed MoClust, a novel joint clustering framework that can be applied to several types of single-cell multi-omics data. A selective automatic doublet detection module that can

SUBMITTER: Yuan M 

PROVIDER: S-EPMC9805570 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature