Single-Cell Analysis: Best Practice Guidelines
For newcomers to the field of single-cell analysis as well as for experts, scientists led by Fabian Theis, Head of the Computational Health Center at Helmholtz Munich and Professor at the Technical University of Munich (TUM), developed best practice guidelines. With the increasing amount of single-cell data being produced in recent years the number of computational methods for analysis is also expanding leading to a difficult to navigate process for researchers. Therefore, the scientists reviewed a large amount of currently available tools and analysis and published the results with detailed description of available methods and how-to guidelines.
Progress in the single-cell field allows for extensive molecular profiling of cells through several distinct perspectives such as on the chromatin, RNA, protein or spatial level. The number of available computational tools is still growing at a rapid pace making it difficult to choose the best tools for the analysis at hand. A group of researchers around first co-authors Lukas Heumos and Anna Schaar, working at the Computational Health Center at Helmholtz Munich, composed an expert recommendation on the analysis of unimodal and multimodal single-cell data based on independent benchmarks that is complemented by a detailed free online book with code examples. Both contributions will guide newcomers to the field and update experts on the latest analysis best practices leading to more robust analyses.
Single-Cell Analysis Is a Fast-Evolving Field
The complexity of single-cell analysis is ever increasing. More complex experiments are being designed with more samples, more cells, more conditions, and combinations of different data modalities. To tackle this complexity, more than 1400 tools have been published for single-cell RNA analysis alone and additionally many more for further single-cell modalities such as chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling, and spatial information. Although computational analysis best practices have been formulated before, according to the authors, they are either outdated or incomplete and do not cover all modalities. The new recommendations for tooling based on independent benchmarks, the highlighting of analysis pitfalls, and the optimal code examples together allow analysts to make sense of challenging datasets.
An Extensive Online Book Version Guides Users in Their Day-To-Day Analysis
With upcoming new advances in the field, the current recommendations might change or need to be adapted. Therefore, the additional online book has a living nature allowing the researchers to continuously update the analysis best practices as more novel tools and benchmarks get published. In addition, the recommendations can be used as a foundation for clinical implications with upcoming advances in patient omics-analysis in the future. The living online book will guarantee that analysts are always up to date with the most recent best practices to ensure robust single-cell data analysis.
Original publication
Heumos et al. (2023): Best practices for single-cell analysis across modalities, Nature Reviews Genetics. DOI: 10.1038/s41576-023-00586-w
More information
Additional free online book: https://www.sc-best-practices.org
Acknowledgements
This work has been funded by the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IS18053A, by the Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association “ForInter” (Interaction of human brain cells), by the Wellcome Trust Grant 108413/A/15/D and by the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI [grant number: ZT-I-PF-5-01].
