Hierarchical progressive learning of cell identities in single-cell data.

ABSTRACT: Supervised methods are increasingly used to identify cell populations in single-cell data. Yet, current methods are limited in their ability to learn from multiple datasets simultaneously, are hampered by the annotation of datasets at different resolutions, and do not preserve annotations when retrained on new datasets. The latter point is especially important as researchers cannot rely on downstream analysis performed using earlier versions of the dataset. Here, we present scHPL, a hierarchical progressive learning method which allows continuous learning from single-cell data by leveraging the different resolutions of annotations across multiple datasets to learn and continuously update a classification tree. We evaluate the classification and tree learning performance using simulated as

SUBMITTER: Michielsen L 

PROVIDER: S-EPMC8121839 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature