Rarity: discovering rare cell populations from single-cell imaging data.

ABSTRACT:

Motivation

Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that is magnified when they are defined by differentially expressing a s

SUBMITTER: Martens K 

PROVIDER: S-EPMC10751233 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature