As a first step in the analysis, we applied cluster analysis to obtain a global overview of cytokine expression in the AH. Contrary to conventional analysis, this analysis method does not divide the samples into a priori groups but aggregates them on the basis of similarity to each other (i.e., nonsupervised analysis;
Fig. 1A). Application of this method to our data shows that the samples can be divided into three principal groups: one consisting primarily of ARN (
Fig. 1A, cluster A) and two mixed groups (
Fig. 1A, clusters B and C). The noninflammatory controls fall primarily in a subgroup of the bottom compartment in cluster C, while OT and RV-FU samples do not separate clearly from each other or from the controls. This analysis was confirmed by using PCA (
Fig. 1B), a technique that uses all AH measurements to evaluate the extent to which samples are alike and projects this into a 2D plot. Principal component analysis data showed that ARN samples appear to contain different cytokines when compared to the other disease categories, as these segregated from the other samples on the first principal component that accounts for two-thirds of all variability in the data (66%) (
Fig. 1B).
Cluster analysis further showed that the three clusters segregate primarily by the expression of a subset of cytokines, including IP-10, sVCAM-1, sICAM-1, MCP-1, Eotaxin, MIF, IL-8, IL-6, RANTES, IL-10, and IL-18, ranging from high, intermediate, and low levels in clusters A, B, and C, respectively. The control subgroup in cluster C segregated primarily on cytokines IL-12p70, MIP1α, TNF-α, and IL-13; a similar subgroup based on these cytokines was observed in the ARN-dominated cluster A.