Clustering single-cell multimodal omics data with jrSiCKLSNMF.

ABSTRACT: Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent

SUBMITTER: Ellis D 

PROVIDER: S-EPMC10288154 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature