TY - JOUR
T1 - Best practices for single-cell analysis across modalities
AU - Single-cell Best Practices Consortium
AU - Heumos, Lukas
AU - Schaar, Anna C.
AU - Lance, Christopher
AU - Litinetskaya, Anastasia
AU - Drost, Felix
AU - Zappia, Luke
AU - Lücken, Malte D.
AU - Strobl, Daniel C.
AU - Henao, Juan
AU - Curion, Fabiola
AU - Aliee, Hananeh
AU - Ansari, Meshal
AU - Badia-i-Mompel, Pau
AU - Büttner, Maren
AU - Dann, Emma
AU - Dimitrov, Daniel
AU - Dony, Leander
AU - Frishberg, Amit
AU - He, Dongze
AU - Hediyeh-zadeh, Soroor
AU - Hetzel, Leon
AU - Ibarra, Ignacio L.
AU - Jones, Matthew G.
AU - Lotfollahi, Mohammad
AU - Martens, Laura D.
AU - Müller, Christian L.
AU - Nitzan, Mor
AU - Ostner, Johannes
AU - Palla, Giovanni
AU - Patro, Rob
AU - Piran, Zoe
AU - Ramírez-Suástegui, Ciro
AU - Saez-Rodriguez, Julio
AU - Sarkar, Hirak
AU - Schubert, Benjamin
AU - Sikkema, Lisa
AU - Srivastava, Avi
AU - Tanevski, Jovan
AU - Virshup, Isaac
AU - Weiler, Philipp
AU - Schiller, Herbert B.
AU - Theis, Fabian J.
N1 - Publisher Copyright:
© 2023, Springer Nature Limited.
PY - 2023/8
Y1 - 2023/8
N2 - Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
AB - Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
UR - http://www.scopus.com/inward/record.url?scp=85152429015&partnerID=8YFLogxK
U2 - 10.1038/s41576-023-00586-w
DO - 10.1038/s41576-023-00586-w
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C2 - 37002403
AN - SCOPUS:85152429015
SN - 1471-0056
VL - 24
SP - 550
EP - 572
JO - Nature Reviews Genetics
JF - Nature Reviews Genetics
IS - 8
ER -