Evaluating runtime in Binary clustering of Single-cell RNA Sequencing data

Evaluating runtime in Binary clustering of Single-cell RNA Sequencing data

de Koning, Milan (TU Delft Electrical Engineering, Mathematics and Computer Science)

Bouland, G.A. (mentor)
Reinders, M.J.T. (mentor)
Gerritsen, B.H.M. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

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.

Binary clustering
scRNAseq
runtime

http://resolver.tudelft.nl/uuid:9c11c1a3-fe0d-4be9-89dc-7519dba3e7de

Student theses

bachelor thesis

© 2023 Milan de Koning