Partial label learning for automated classification of single-cell transcriptomic profiles.

ABSTRACT: Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learnin

SUBMITTER: Senoussi M 

PROVIDER: S-EPMC11023635 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature