Figure 6
From: Merged consensus clustering to assess and improve class discovery with microarray data
Refining gene expression profiles with merge consensus clustering. We compared the cluster and membership robustness of consensus and merge consensus clustering matrices using the k-means clustering structure. (A) For the consensus clustering results, clusters 1 and 5 were highly robust (cr = 0.99 and 0.97), clusters 2-4 and 6 were moderately robust (cr = 0.81, 0.66, 0.76 and 0.74) and outliers (open black triangles) were evident for clusters 1, 5 and 6. Refinement of the robustness measures by merge consensus clustering broadly maintained or improved the overall cluster robustness (cr = 0.93, 0.87, 0.63, 0.85, 0.90 and 0.79, clusters 1-6 respectively), but re-segregated the outliers for clusters 1,2,5 and 6. For example, a striking outlier appears for the highly conserved cluster 1 as a result of merge consensus clustering (probe-set 1638314-at, mr = 0.99 → 0.66). (B) This outlier is confirmed by plotting the relative gene expression for all of the probe-sets in cluster 1 (probe-set 1638314-at black line, open black triangles).