Unfold High-Dimensional Clouds for Exhaustive Gating of Flow Cytometry Data

Abstract

Flow cytometry is able to measure the expressions of multiple proteins simultaneously at the single-cell level. A flow cytometry experiment on one biological sample provides measurements of several protein markers on or inside a large number 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 analytical approach in the flow cytometry community is manual gating on a sequence of nested biaxial plots, which is highly subjective, labor intensive, and not exhaustive. To address those issues, a number of methods have been developed to automate the gating analysis by clustering algorithms. However, completely removing the subjectivity can be quite challenging. This paper describes an alternative approach. Instead of automating the analysis, we develop novel visualizations to facilitate manual gating. The proposed method views single-cell 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 2D visualizations. We demonstrate the utility of the proposed visualization using real data, and provide quantitative comparison to visualizations generated from principal component analysis and multidimensional scaling.