For example, regulatory T cells (Treg) have been characterised by comparing Treg (almost always) with conventional T cells (ie. CD25- or Foxp3- CD4+ T cells). However, this means that the characteristics of Treg, which we know, are partially the 'negative image' of conventional T cells. Therefore, the feature of Treg can be elucidated only through analysing their features in comparison to other T cell populations such as effector T cells, follicular helper T cells, memory T cells, and resting conventional T cells. Thus, multidimensional analysis is an essential tool for immunology.

Ono addresssed this problem by adapting to genomic data analysis the multidimensional method that was developed in sociology and ecology. In these disciplines, researchers deal with multiple phenotypes ('species' in ecology, 'social groups' in sociology), and Ono discovered Canonical Correspondence Analysis (CCA, ter Braak, 1986) as a potentially useful tool, and adapted CCA to genomic expression analysis (Ono et al, 2013; Ono et al, 2014).

CCA visualises the cross-level relationship between cells, genes, and biological process in a data-oriented manner.

Recently, using CCA-based multidimensional analysis methods, we have shown that Treg are more similar to memory-phenotype T cells and effector T cells than conventional T cells (Bradley et al, 2018).