Comparison of Bias and Resolvability in Single-Cell and Single-Transcript Methods

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