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Figure 3 | BMC Bioinformatics

Figure 3

From: Merged consensus clustering to assess and improve class discovery with microarray data

Figure 3

Patient class discovery using a consensus clustering approach. The leukaemia gene expression data set of Golub et al. [22] was used to assess the utility of consensus clustering to the segregation of patients into either an all (1-27) or aml (28-38) cluster. Consensus clustering was carried out with 500 iterations and the clustering algorithms agnes, k-means and pam and the membership robustness was calculated and plotted against patient number for both clusters. All three algorithms correctly segregated the aml patients into the same cluster ('consensus' panels, cluster 2, black filled circles), but only agnes (all) and k-means (all but 2 and 12) segregated the all patients reliably, whereas pam failed to correctly segregate 8/27 all patients. Merge consensus matrices were generated and membership robustness calculated for each of the three clustering structures ('merge' panels). Agnes and k-means produced almost identical results correctly segregating all patients apart from aml patients 2 and 12. Pam correctly segregated all aml patients, but could not segregate 19/27 all patients.

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