A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data.

ABSTRACT:

Motivation

Single-cell multimodal assays allow us to simultaneously measure two different molecular features of the same cell, enabling new insights into cellular heterogeneity, cell development and diseases. However, most existing methods suffer from inaccurate dimensionality reduction for the joint-modality data, hindering their discovery of novel or rare cell subpopulations.

Results

Here, we present VIMCCA, a computational framework based on variational-assisted multi-view canonical correlation analysis to integrate paired multimodal single-cell data. Our statistical model uses a common latent variable to interpret the common source of variances in two different data modalities. Our approach jointly learns an inference model and two modality-specific non-linear models by

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9857983 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature