RT Journal Article
SR Electronic
T1 Rarity: Discovering rare cell populations from single-cell imaging data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.07.15.500256
DO 10.1101/2022.07.15.500256
A1 Kaspar Märtens
A1 Michele Bortolomeazzi
A1 Lucia Montorsi
A1 Jo Spencer
A1 Francesca Ciccarelli
A1 Christopher Yau
YR 2022
UL http://biorxiv.org/content/early/2022/07/18/2022.07.15.500256.abstract
AB Background 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 are magnified when they are defined by differentially expressing a small number of genes. Typical unsupervised approaches fail to identify such rare subpopulations, and these cells tend to be absorbed into more prevalent cell types.Results In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries.Conclusions We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC data sets.Competing Interest StatementThe authors have declared no competing interest.