Multi-Objective Evolutionary Clustering of Single-Cell RNA Sequencing Data
DescriptionCells are the basic building blocks of human organisms. Single-cell RNA sequencing is a technology for studying the heterogeneity of cells of different organs, tissues, subjects, conditions, and treatments. Identification of cell types and states in sequenced data is an important and challenging task, requiring computational approaches that are accurate, robust, and scalable. Existing approaches use cluster analysis as the first step of cell-types prediction. Their performance remains limited because they optimize only one objective function. In this study, two evolutionary clustering approaches were designed, implemented, and systematically validated, namely a single-objective evolutionary algorithm and a multi-objective evolutionary algorithm. The algorithms were evaluated on synthetic and real datasets. The results demonstrated that the performance and the accuracy of both evolutionary algorithms were consistent, stable, and on par with or better than baseline algorithms. Running time analysis of multi-processing on an HPC showed that evolutionary algorithms can efficiently handle large datasets.
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
TimeTuesday, 15 November 20228:30am - 5pm CST