Classifying flow cytometry data using Bayesian analysis helps to distinguish ALS patients from healthy controls

ABSTRACT:

Introduction

Given its wide availability and cost-effectiveness, multidimensional flow cytometry (mFC) became a core method in the field of immunology allowing for the analysis of a broad range of individual cells providing insights into cell subset composition, cellular behavior, and cell-to-cell interactions. Formerly, the analysis of mFC data solely relied on manual gating strategies. With the advent of novel computational approaches, (semi-)automated gating strategies and analysis tools complemented manual approaches.

Methods

Using Bayesian network analysis, we developed a mathematical model for the dependencies of different obtained mFC markers. The algorithm creates a Bayesian network that is a HC tree when including raw, ungated mFC data of a randomly selected heal

SUBMITTER: Rauber S 

PROVIDER: S-EPMC10434536 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature