scROSHI: robust supervised hierarchical identification of single cells

Prummer, Michael; Bertolini, Anne; Bosshard, Lars; Barkmann, Florian; Yates, Josephine; Boeva, Valentina; Tumor Profiler Consortium; Stekhoven, Daniel; Singer, Franziska; et al; Andani, Sonali; Frei, Anja L; Haberecker, Martina; Koelzer, Viktor H; Moch, Holger; Sobottka, Bettina; Wey, Norbert; Zoche, Martin

doi:10.1093/nargab/lqad058

Citations

Citation copied

Prummer, M., Bertolini, A., Bosshard, L., Barkmann, F., Yates, J., Boeva, V., Tumor Profiler Consortium, Stekhoven, D., Singer, F., et al, Andani, S., Frei, A. L., Haberecker, M., Koelzer, V. H., Moch, H., Sobottka, B., Wey, N., & Zoche, M. (2023). scROSHI: robust supervised hierarchical identification of single cells. NAR Genomics and Bioinformatics, 5(2), lqad058. https://doi.org/10.1093/nargab/lqad058

Abstract

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell