Randomized gates eliminate bias in sort-seq assays

Abstract

Sort-seq assays are a staple of the biological engineering toolkit, allowing researchers to profile many groups of cells based on any characteristic that can be tied to fluorescence. However, current approaches, which segregate cells into bins deterministically based on their measured fluorescence, introduce systematic bias. We describe a surprising result: one can obtain unbiased estimates by incorporating randomness into sorting. We validate this approach in simulation and experimentally, and describe extensions for both estimating group level variances and for using multi-bin sorters.