Evaluation of Clustering Algorithms for Protein Complex and Protein Interaction Network Assembly

Assembling protein complexes and protein interaction networks from affinity purification-based proteomics data sets remains a challenge. When little a priori knowledge of the complexes exists, it is difficult to place proteins in the proper locations and evaluate the results of clustering approaches. Here we have systematically compared multiple hierarchical and partitioning clustering approaches using a well-characterized but highly complex human protein interaction network data set centered around the conserved AAA+ ATPases Tip49a and Tip49b. This network provides a challenge to clustering algorithms because Tip49a and Tip49b are present in four distinct complexes, the network contains modules, and the network has multiple attachments. We compared the use of binary data, quantitative proteomics data in the form of normalized spectral abundance factors, and the Z-score normalization. In our analysis, a partitioning approach indicated the major modules in a network. Next, while Euclidian distance was sensitive to scaling, with data transformation, all the attachments in a data set were recovered in one branch of a dendrogram. Finally, when Pearson correlation and hierarchical clustering were used, complexes were well separated and their attachments were placed in the proper locations. Each of these three approaches provided distinct information useful for assembly of a network of multiple protein complexes.