Identifying cell states in single-cell RNA-seq data at statistically maximal resolution.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because of the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measure

SUBMITTER: Grobecker P 

PROVIDER: S-EPMC11364423 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature