1. Analysis of Clustering Algorithms
Cluster 0 (Color CC0000)
Analysis of Clustering Algorithms
Ethan Summers and Kathryn Cooper
College of Information Science and Technology
University of Nebraska at Omaha, Omaha, NE 68182
Results
ABSTRACT
Results
Algorithm
Clusters
Nodes in Clusters
Unclustered nodes
K-medoid 3
Cluster 0: 8
Cluster 1: 10
Cluster 2: 8
MCODE 2
Cluster 0: 5
Cluster 1: 4 17
MCL
Cluster 0: 19
Cluster 1: 3
Cluster 2: 2
K-medoid
Cluster 0: Red Cluster 1: Green Cluster 2: Purple
In Bioinformatics, choosing the right algorithm for a problem is very important. Choosing the wrong algorithm or one that is less efficient can make or break a project. Analyzing algorithms beforehand is key. The goal of this project is to analyze three clustering algorithms for protein protein interaction networks and compare their function and results. A clustering algorithm takes a dataset, in this case a simulated PPI (protein-protein interaction) network and groups together similar data points based on some similarity criteria. It is important to know the difference between these algorithms to get the desired results.
Methods
K-medoid
Even clusters
Forced cluster amount
No unclustered nodes
Seems to be good if there needs to be a specific amount of clusters, and all nodes must be used
MCODE
Even clusters
Fewer clusters
Many unclustered nodes
Seems to be good to pull specialized clusters out only if there is a connection
MCL
Skewed nodes
Very large single cluster
Few unclustered nodes
Seems to create a large cluster, this could be due to it being the first.
PPI data was simulated by random assignment of both values and edges
A number of network clustering algorithms were investigated for use. Many of these were not used but included WDCM, MSARC, e-CCC biclustering, and SPICI
Ultimately, the following algorithms were chosen and run using Cytoscape. Cytoscape is a UI that was used to run algorithms using a randomly generated list of nodes and edges. The algorithms used were:
K-medoids,
MCODE, and
MCL
These algorithms were chosen for their usability and ease of use with existing file formats.
MCODE
Cluster 0: Red Cluster 1: Green
Conclusion and Future Directions
Results
There is no set format to be used for this type of system, so finding a GUI is very helpful
There is a general sense of people making systems only for their own work, due to a lack of examples, guides, and standardization
For the future look for software, like Cytoscape, rather than individual tools
“Base” Network: Randomized set with 26 nodes and edges with edge weights betwen between 0-1 (chosen randomly)
MCL
Cluster 0: Red Cluster 1: Green Cluster 2: Purple
Node1
Node2
Edge A G
0.1 P
0.9 Q
0.7 B W
0.2 O
0.4
0.6 C E
0.3 I Z D R
0.5 U T
0.8 Y S F X H 1 J K L V M N
References
Bader, G. D., & Hogue, C. W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC bioinformatics, 4(1), 2.
Morris, J. H., Apeltsin, L., Newman, A. M., Baumbach, J., Wittkop, T., Su, G., ... & Ferrin, T. E. (2011). clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC bioinformatics, 12(1), 436.
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., ... & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13(11),