Stochastic Regression and Peak Delineation with Flow Cytometry Data

Abstract

Many modern molecular analysis methods utilize DNA content values as part of the measurement process, and thus, the distribution of genome copies per cell within a population of cells is important. Genome copy distributions can be measured via flow cytometry by thresholding (or "gating") a subset of cells from which estimates of the targeted properties (e.g., genome copy number) can be calculated. This manuscript introduces a new approach that gives separate estimates of signal and noise, the former of which is used for gating and analysis, and the latter is used to quantify uncertainty. In this approach stochastic regression was used to quantify subpopulations of cells that have distinctly different genome copies per cell within a heterogenous population of Escherichia coli (E. coli) cells. By separating the signal and noise components, they can be used independently to evaluate measurement quality across different experimental conditions.