> Details

Prummer, Michael; Bertolini, Anne; Bosshard, Lars; Barkmann, Florian; Yates, Josephine; Boeva, Valentina; Aebersold, Rudolf; Ak, Melike; Al-Quaddoomi, Faisal S; Albinus, Jonas; Alborelli, Ilaria; Andani, Sonali; Attinger, Per-Olof; Bacac, Marina; Baumhoer, Daniel; Beck-Schimmer, Beatrice; Beerenwinkel, Niko; Beisel, Christian; Bernasconi, Lara; Bertolini, Anne; Bodenmiller, Bernd; Bonilla, Ximena; Bosshard, Lars; Calgua, Byron; [...]

scROSHI: robust supervised hierarchical identification of single cells

Reference
management

Bookmarks

Remove from
bookmarks

Share this on Whatsapp

Close

Bookmarks



You can manage bookmarks using lists, please log in to your user account for this.
  • Media type: E-Article
  • Title: scROSHI: robust supervised hierarchical identification of single cells
  • Contributor: Prummer, Michael; Bertolini, Anne; Bosshard, Lars; Barkmann, Florian; Yates, Josephine; Boeva, Valentina; Aebersold, Rudolf; Ak, Melike; Al-Quaddoomi, Faisal S; Albinus, Jonas; Alborelli, Ilaria; Andani, Sonali; Attinger, Per-Olof; Bacac, Marina; Baumhoer, Daniel; Beck-Schimmer, Beatrice; Beerenwinkel, Niko; Beisel, Christian; Bernasconi, Lara; Bertolini, Anne; Bodenmiller, Bernd; Bonilla, Ximena; Bosshard, Lars; Calgua, Byron; [...]
  • Published: Oxford University Press (OUP), 2023
  • Published in: NAR Genomics and Bioinformatics, 5 (2023) 2
  • Language: English
  • DOI: 10.1093/nargab/lqad058
  • ISSN: 2631-9268
  • Origination:
  • Footnote:
  • Description: AbstractIdentifying 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 type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.
  • Access State: Open Access