Download PDFOpen PDF in browserClique Selection and its Effect on Paraclique Enrichment: An Experimental Study10 pages•Published: March 11, 2020AbstractThe paraclique algorithm provides an effective means for biological data clustering. It satisfies the mathematical quest for density, while fulfilling the pragmatic need for noise abatement on real data. Given a finite, simple, edge-weighted and thresholded graph, the paraclique method first finds a maximum clique, then incorporates additional vertices in a controlled manner, and finally extracts the subgraph thereby defined. When more than one maximum clique is present, however, deciding which to employ is usually left unspecified. In practice, this frequently and quite naturally reduces to using the first maximum clique found. In this paper, maximum clique selection is studied in the context of well-annotated transcriptomic data, with ontological classification used as a proxy for cluster quality. Enrichment p-values are compared using maximum cliques chosen in a variety of ways. The most appealing and intuitive option is almost surely to start with the maximum clique having the highest average edge weight. Although there is of course no guarantee that such a strategy is any better than random choice, results derived from a large collection of experiments indicate that, in general, this approach produces a small but statistically significant improvement in overall cluster quality. Such an improvement, though modest, may be well worth pursuing in light of the time, expense and expertise often required to generate timely, high quality, high throughput biological data.Keyphrases: biological data clustering, graph theoretical algorithms, ontological enrichment, the paraclique algorithm, transcriptomic data analysis In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 99-108.
|
