Evaluating runtime in Binary clustering of Single-cell RNA Sequencing data
M. de Koning (TU Delft - Electrical Engineering, Mathematics and Computer Science)
G.A. Bouland – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Marcel J. T. Reinders – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
BHM Gerritsen – Graduation committee member (TU Delft - Computer Science & Engineering-Teaching Team)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
As single-cell RNA sequencing techniques improve and more cells are measured in individual experiments, cell clustering procedures become increasingly more computationally intensive. This paper studies the runtime performance impact of a specialized clustering algorithm for data converted to a binary format, in order to reduce computational burden. We experimentally show that our specialized algorithm runs faster than the Seurat library on small datasets, and that with proper dimensionality reduction and approximation techniques, the algorithm could be more scalable than current methods. Optimizations for cluster quality and memory efficiency are not considered in this paper.