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Table 1 Simulation studies of consensus module detection.

From: Eigengene networks for studying the relationships between co-expression modules

Noise level Branch cut Consensus module detection
   Sensitivity Specificity Fidelity
1 0.965 1 1 0.989
1 0.975 1 1 0.988
1 0.985 1 1 0.985
1 0.995 1 1 0.965
2 0.965 0.966 1 0.964
2 0.975 0.984 1 0.958
2 0.985 0.998 1 0.949
2 0.995 1 1 0.935
3 0.965 0.717 1 0.871
3 0.975 0.823 1 0.838
3 0.985 0.929 1 0.824
3 0.995 0.997 0.999 0.822
4 0.965 0.457 1 0.823
4 0.975 0.589 1 0.744
4 0.985 0.739 0.997 0.713
4 0.995 0.928 0.995 0.675
5 0.965 0.0753 1 0.636
5 0.975 0.16 1 0.421
5 0.985 0.296 0.992 0.415
5 0.995 0.643 0.966 0.363
6 0.965 0.00345 1 0.667
6 0.975 0.0138 1 0.333
6 0.985 0.077 0.971 0.209
6 0.995 0.355 0.954 0.168
  1. Using simulated data to assess the performance of the consensus module detection method. The column 'noise level' reflects the amount of noise added to the simulated data (details can be found in Additional File 7). The modules were defined as branches of an average linkage hierarchical cluster tree. The column 'branch cut' reports the heights used for cutting branches of the cluster tree. Sensitivity, specificity and fidelity are defined in the text.