Toward exhaustive gating of flow cytometry data

Abstract

Flow cytometry is a high-throughput technology that measures protein expressions at the single-cell level. A typical flow cytometry experiment on one biological sample provides measurements of several protein markers on or inside hundreds of thousands of individual cells in that sample. Analysis of such data often aims to identify subpopulations of cells with distinct phenotypes. Currently, the most widely used analysis in the flow cytometry community is manual gating on a sequence of biaxial plots, which is highly subjective and labor intensive. To address those issues, the majority of efforts in the literature have been devoted to automate the gating analysis using clustering algorithms. However, completely removing the subjectivity can be quite challenging. This paper describes an opposite approach. Instead of automating the analysis, we aim to develop novel visualizations to facilitate manual gating. The proposed method views a flow cytometry data of one biological sample as a high-dimensional point cloud of cells, derives the skeleton of the cloud, and unfolds the skeleton to generate a 2D visualization.