Best practices for single-cell analysis across modalities
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.
Citation
@article{heumos2023,
author = {Heumos, Lukas and C. Schaar, Anna and Lance, Christopher and
Litinetskaya, Anastasia and Drost, Felix and Zappia, Luke and D.
Lücken, Malte and C. Strobl, Daniel and Henao, Juan and Curion,
Fabiola and Best Practices Consortium, Single-cell and B. Schiller,
Herbert and J. Theis, Fabian},
title = {Best Practices for Single-Cell Analysis Across Modalities},
journal = {Nature reviews genetics},
pages = {1-23},
date = {2023-03-31},
url = {https://lazappi.id.au/publications/2023-heumos-best-practices},
doi = {10.1038/s41576-023-00586-w},
issn = {1471-0056},
langid = {en},
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.}
}