Print Email Facebook Twitter Hierarchical progressive learning of cell identities in single-cell data Title Hierarchical progressive learning of cell identities in single-cell data Author Michielsen, L.C.M. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Mahfouz, A.M.E.T.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Date 2021 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 well as real datasets and show that scHPL can successfully learn known cellular hierarchies from multiple datasets while preserving the original annotations. scHPL is available at https://github.com/lcmmichielsen/scHPL. To reference this document use: http://resolver.tudelft.nl/uuid:a96876ee-f968-4128-9609-d3b4ff3cfef3 DOI https://doi.org/10.1038/s41467-021-23196-8 ISSN 2041-1723 Source Nature Communications, 12 (1) Part of collection Institutional Repository Document type journal article Rights © 2021 L.C.M. Michielsen, M.J.T. Reinders, A.M.E.T.A. Mahfouz Files PDF s41467_021_23196_8.pdf 1.86 MB Close viewer /islandora/object/uuid:a96876ee-f968-4128-9609-d3b4ff3cfef3/datastream/OBJ/view