SPICE: Exploration and analysis of post‐cytometric complex multivariate datasets

TLDR

Polychromatic flow cytometry generates complex, multivariate datasets that are typically explored with pie or bar charts, yet existing tools lack optimized aggregate analysis across specimens grouped by categorical variables. The authors developed algorithms and a graphical interface to enable aggregate analysis of such datasets. They implemented thresholding for accurate pie chart representation, addressed normalization effects and averaging with background noise, and introduced a nonparametric statistic for comparing complex distributions between sample groups. Although initially designed for T cell functional profiling, the methods are broadly applicable to diverse data types. Published 2011 Wiley‑Liss, Inc.

Abstract

Abstract Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. Often, the exploration of such datasets is accomplished by visualization of patterns with pie charts or bar charts, without easy access to statistical comparisons of measurements that comprise multiple components. Here we report on algorithms and a graphical interface we developed for these purposes. In particular, we discuss thresholding necessary for accurate representation of data in pie charts, the implications for display and comparison of normalized versus unnormalized data, and the effects of averaging when samples with significant background noise are present. Finally, we define a statistic for the nonparametric comparison of complex distributions to test for difference between groups of samples based on multi‐component measurements. While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes. Published 2011 Wiley‐Liss, Inc.