Cluster analysis for the impact of sickle cell disease on the human erythrocyte protein interactome.
Protein interaction networks provide useful information to assess impacts of disease on cell functions. Statistical clustering methods applied to these networks can reveal impacts on particular functional groups of proteins. In addition, clustering methods can identify subsets of proteins that require additional study to provide updated information regarding their position within an interaction network, and hence increase our understanding of their relationships with other proteins in the network. These ideas are illustrated here by considering the impacts of sickle cell disease on the human erythrocyte interaction network. Statistical cluster analyses are performed based on a measure of similarity for nodes within a network called the Generalized Topological Overlap Measure. These analyses identify clusters of proteins that are similar in terms of shared interaction partners. Identification of clusters that contain proteins whose relative abundances have been significantly altered in sickle cell patients provides specific information about the impact of these proteins on erythrocyte functions.
University of Texas at Dallas, Department of Mathematical Sciences, 800 West Campbell Road, Richardson, TX 75080-3021, USA. ammann@utdallas.edu.
Larry P
LP
Ammann
Steven R
SR
Goodman
D000755:Anemia, Sickle Cell / Q000378:metabolism*; D016000:Cluster Analysis; D004912:Erythrocytes / Q000378:metabolism*; D006801:Humans; D011506:Proteins / Q000378:metabolism*
D011506:Proteins