Publication Date


Document Type


Committee Members

Keke Chen (Advisor), Krishnaprasad Thirunarayan (Committee Member), Tanvi Banerjee (Committee Member)

Degree Name

Master of Science (MS)


The development of the next-generation sequencing technology has enabled systems immunology researchers to conduct detailed immune repertoire analysis at the molecule level. Large sequence datasets (e.g., millions of sequences) are being collected to comprehensively understand how the immune system of a patient evolves over different stages of disease development. A recent study has shown that the hierarchical clustering (HC) algorithm gives the best results for B-cell clones analysis - an important type of immune repertoire sequencing (IR-Seq) analysis. However, due to the inherent complexity, the classical hierarchical clustering algorithm does not scale well to large sequence datasets. Surprisingly, no algorithms have been developed to address this scalability issue for immunology research. In this thesis, we study two different strategies, aiming at finding the best scalable methods that can preserve the quality of hierarchical clustering structure. The two strategies include (1) non-Euclidean indexing methods for speeding up the classical hierarchical clustering(HC), (2) a new tree-based sequence summarization approach - SCT that scans the large sequence dataset once and generates summaries for hierarchical clusters(HC). And we also experimented with the Spark based minimum-spanning-tree algorithm (SparkMST) that generates the equivalent result of single linkage hierarchical clustering (SLINK) for comparative analysis. We have implemented all these algorithms and experimented with real sequence datasets for B-cell clones analysis. The result shows that (1) the indexing-enhanced HC (e.g., using the Vantage-Point tree for indexing) preserves the clustering quality very well, while also significantly reducing the time complexity of the original HC; (2) SCT with HC is the fastest approximate HC method with slightly sacrificed quality; and (3) SparkMST scales out satisfactorily and gives significant performance gain with a large Spark cluster.

Page Count


Department or Program

Department of Computer Science and Engineering

Year Degree Awarded