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Contrastive self-supervised clustering of scRNA-seq data.

par Ciortan, Madalina ;Defrance, Matthieu
Référence BMC bioinformatics, 22, 1, page (280)
Publication Publié, 2021-05-01

Article révisé par les pairs

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Résumé :

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.