Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data.

ABSTRACT: Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms. However, the noise inherent in single-cell RNA-sequencing data severely disturbs the accuracy of cell clustering, marker identification and visualization. We propose that clustering based on feature density profiles can distinguish informative features from noise. We named such strategy as 'entropy subspace' separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the 'entropy subspace' separation strategy with a consensus clustering method. We demonstrate that ENCORE performs superiorly on cell clustering and generates high-resolution visual

SUBMITTER: Song J 

PROVIDER: S-EPMC7897472 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature