scROSHI: robust supervised hierarchical identification of single cells
Authors: Prummer Michael , Bertolini Anne , Bosshard Lars , Barkmann Florian , Yates Josephine , Boeva Valentina ,   The Tumor Profiler Consortium, 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 , Casanova Ruben , Chevrier Stéphane , Chicherova Natalia , D’Costa Maya , Danenberg Esther , Davidson Natalie , Drăgan Monica-Andreea , Dummer Reinhard , Engler Stefanie , Erkens Martin , Eschbach Katja , Esposito Cinzia , Fedier André , Ferreira Pedro , Ficek Joanna , Frei Anja L , Frey Bruno , Goetze Sandra , Grob Linda , Gut Gabriele , Günther Detlef , Haberecker Martina , Haeuptle Pirmin , Heinzelmann-Schwarz Viola , Herter Sylvia , Holtackers Rene , Huesser Tamara , Irmisch Anja , Jacob Francis , Jacobs Andrea , Jaeger Tim M , Jahn Katharina , James Alva R , Jermann Philip M , Kahles André , Kahraman Abdullah , Koelzer Viktor H , Kuebler Werner , Kuipers Jack , Kunze Christian P , Kurzeder Christian , Lehmann Kjong-Van , Levesque Mitchell , Lugert Sebastian , Maass Gerd , Manz Markus G , Markolin Philipp , Mena Julien , Menzel Ulrike , Metzler Julian M , Miglino Nicola , Milani Emanuela S , Moch Holger , Muenst Simone , Murri Riccardo , Ng Charlotte K Y , Nicolet Stefan , Nowak Marta , Pedrioli Patrick G A , Pelkmans Lucas , Piscuoglio Salvatore , Prummer Michael , Ritter Mathilde , Rommel Christian , Rosano-González María L , Rätsch Gunnar , Santacroce Natascha , del Castillo Jacobo Sarabia , Schlenker Ramona , Schwalie Petra C , Schwan Severin , Schär Tobias , Senti Gabriela , Singer Franziska , Sivapatham Sujana , Snijder Berend , Sobottka Bettina , Sreedharan Vipin T , Stark Stefan , Stekhoven Daniel J , Theocharides Alexandre P A , Thomas Tinu M , Tolnay Markus , Tosevski Vinko , Toussaint Nora C , Tuncel Mustafa A , Tusup Marina , Van Drogen Audrey , Vetter Marcus , Vlajnic Tatjana , Weber Sandra , Weber Walter P , Wegmann Rebekka , Weller Michael , Wendt Fabian , Wey Norbert , Wicki Andreas , Wildschut Mattheus H E , Wollscheid Bernd , Yu Shuqing , Ziegler Johanna , Zimmermann Marc , Zoche Martin , Zuend Gregor , Stekhoven Daniel , Singer Franziska 
Published: June 2023

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.