Contrastive self-supervised clustering of scRNA-seq data.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.

Results

We

SUBMITTER: Ciortan M 

PROVIDER: S-EPMC8157426 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature