Comparison of bias and resolvability in single-cell and single-transcript methods

• Published in: Communications biology Vol. 4; no. 1; p. 659
• Main Authors: Rammohan, Jayan, Lund, Steven P., Alperovich, Nina, Paralanov, Vanya, Strychalski, Elizabeth A., Ross, David
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02-06-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 13/31; 14/63; 38/32; 38/35; 631/1647/2017; 631/326/41/2532; 631/553/2706; 631/57/2265; Biology; Biomedical and Life Sciences; Life Sciences; Transcription
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-021-02138-6

Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark different methods for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods.
Rammohan et al. propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is defined and compared between methods. Their framework provides a practical solution for benchmarking and comparing the performance of any method, illustrated by analysing single-cell and single-transcript methods.