Per-event Uncertainty Quantification for Flow Cytometry using Calibration Beads

TL;DR

This paper introduces a probabilistic model for flow cytometry that quantifies measurement uncertainty per event, improving the ability to distinguish true signals from background noise and variability.

Contribution

It presents a novel explicit probabilistic measurement model that accounts for multiple sources of uncertainty in flow cytometry data, enabling more accurate signal interpretation.

Findings

Model effectively separates sources of measurement variability.

Improves detection of small objects like extracellular vesicles.

Facilitates better instrument calibration and decision-making.

Abstract

Flow cytometry measurements are widely used in diagnostics and medical decision making. Incomplete understanding of sources of measurement uncertainty can make it difficult to distinguish autofluorescence and background sources from signals of interest. Moreover, established methods for modeling uncertainty overlook the fact that the apparent distribution of measurements is a convolution of the inherent the population variability (e.g., associated with calibration beads or cells) and instrument induced-effects. Such issues make it difficult, for example, to identify signals from small objects such as extracellular vesicles. To overcome such limitations, we formulate an explicit probabilistic measurement model that accounts for volume and labeling variation, background signals and fluorescence shot noise. Using raw data from routine per-event calibration measurements, we use this model to separate the aforementioned sources of uncertainty and demonstrate how such information can be used to facilitate decision-making and instrument characterization.