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Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data

Figure 3

Performance of complex identification.

The proposed approach is compared to several competing methods of discovering protein complexes within genetic interaction networks: HCL implements hierarchical clustering with a distance measure computed from the genetic interaction profiles only (S-scores), while HCL-PE extends HCL by merging clusters only if there is a physical interaction between them (PE-score>1). For the modules defined by each method, accuracy versus coverage is plotted over a range of values for tuning the module size (see Methods). Accuracy is estimated as the fraction of protein pairs in a predicted module that are in a gold-standard set; coverage is estimated as the number of gold-standard pairs that fall in the same predicted module. Gold-standard sets are defined by protein pairs that are either (A) co-expressed, (B) functionally-related, or (C) assigned to the same complex in high-throughput data sets (as annotated in MIPS). The performance at the chosen parameter setting (α = 1.6) is indicated by the dotted vertical line. The performance of the method of Kelley et al. is reported for the same level of coverage as the present approach (asterisk). Since it operates on binary interaction data, we converted quantitative genetic and physical interaction scores to binary values based on a threshold of |S|>2.5 and PE>1.

Figure 3