scROSHI: robust supervised hierarchical identification of single cells
Michael Prummer, Anne Bertolini, Lars Bosshard, Florian Barkmann, Josephine Yates, Valentina Boeva, Rudolf Aebersold, Melike Ak, Faisal Alquaddoomi, Jonas Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Marina Bacac, Daniel Baumhoer, Beatrice Beck‐Schimmer, Niko Beerenwinkel, Christian Beisel, Lara Bernasconi, Anne Bertolini, Bernd Bodenmiller, Ximena Bonilla, Lars Bosshard, Byron Calgua, Ruben Casanova, Stéphane Chevrier, Natalia Chicherova, Maya D’Costa, Esther Danenberg, Natalie J. Davidson, Monica-Andreea Drăgan, Reinhard Dummer, Stefanie Engler, Martin Erkens, Katja Eschbach, Cinzia Esposito, André Fedier, Pedro Ferreira, Joanna Ficek, Anja Frei, Bruno S. Frey, Sandra Goetze, Linda Grob, Gabriele Gut, Detlef Günther, Martina Haberecker, Pirmin Haeuptle, Viola Heinzelmann‐Schwarz, Sylvia Herter, René Holtackers, Tamara Huesser, Anja Irmisch, Francis Jacob, Andrea Jacobs, Tim M. Jaeger, Katharina Jahn, Alva Rani James, Philip Jermann, André Kahles, Abdullah Kahraman, Viktor H. Koelzer, Werner Kuebler, Jack Kuipers, Christian P. Kunze, Christian Kurzeder, Kjong-Van Lehmann, Mitchell Levesque, Sebastian Lugert, Gerd Maass, Markus G. Manz, Philipp Markolin, Julien Mena, Ulrike Menzel, Julian M. Metzler, Nicola Miglino, Emanuela S. Milani, Holger Moch, Simone Muenst, Riccardo Murri, Charlotte K.Y. Ng, Stefan Nicolet, Marta Nowak, Patrick G. A. Pedrioli, Lucas Pelkmans, Salvatore Piscuoglio, Michael Prummer, Mathilde Ritter, Christian Rommel, María L. Rosano-González, Gunnar Rätsch, Natascha Santacroce, Jacobo Sarabia del Castillo, Ramona Schlenker, Petra Schwalie, Severin Schwan, Tobias Schär, Gabriela Senti, Franziska Singer, Sujana Sivapatham, Berend Snijder
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 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.