TY - JOUR
T1 - Hierarchical progressive learning of cell identities in single-cell data
AU - Michielsen, Lieke
AU - Reinders, Marcel J.T.
AU - Mahfouz, Ahmed
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85105926214&partnerID=8YFLogxK
U2 - 10.1038/s41467-021-23196-8
DO - 10.1038/s41467-021-23196-8
M3 - Article
C2 - 33990598
AN - SCOPUS:85105926214
SN - 2041-1723
VL - 12
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 2799
ER -