模式
仿形(计算机编程)
标杆管理
计算机科学
工作流程
数据科学
计算生物学
生物
社会科学
营销
数据库
社会学
业务
操作系统
作者
Lukas Heumos,Anna C. Schaar,Christopher Lance,Anastasia Litinetskaya,Felix Drost,Luke Zappia,Malte Lücken,Daniel Strobl,Juan Henao,Fabiola Curion,Hananeh Aliee,Meshal Ansari,Pau Badia-i-Mompel,Maren Büttner,Emma Dann,Daniel Dimitrov,Leander Dony,Amit Frishberg,Dongze He,Soroor Hediyeh-zadeh
标识
DOI:10.1038/s41576-023-00586-w
摘要
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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