Best Practices for Single-Cell Analysis Across Modalities

Lukas Heumos,Anna C. Schaar,Christopher Lance,Anastasia Litinetskaya,Felix Drost,Luke Zappia,Malte D. Lücken,Daniel C. Strobl,Juan Henao,Fabiola Curion,Hananeh Aliee,Herbert B. Schiller,Fabian J. Theis

Nature Reviews Genetics（2023）SCI 1区

Institute of Computational Biology

Cited 221|Views45

Abstract

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.

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Key words

Functional genomics,Machine learning,RNA sequencing,Software,Biomedicine,general,Human Genetics,Cancer Research,Agriculture,Gene Function,Animal Genetics and Genomics

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