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

ABSTRACT: 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 prep

SUBMITTER: Rammohan J 

PROVIDER: S-EPMC8172639 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature