Print Email Facebook Twitter Binarized single cell RNA sequencing data clustering Title Binarized single cell RNA sequencing data clustering: The impact of binarized scRNA-seq data on clustering through community detection algorithms Author Theunisz, Jurriën (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Reinders, M.J.T. (mentor) Bouland, G.A. (mentor) Gerritsen, B.H.M. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 Abstract Single-cell RNA sequencing data clustering is a valuable technique for demonstrating cell-to-cell heterogeneity and revealing cell dynamics within and amongst groups. Large up-scaling of scRNA-seq datasets in recent years pose computational challenges for existing state-of-the-art clustering techniques. A possible solution to tackle these challenges is to binarize the scRNA-seq data and perform clustering using optimized binary methods. Using a binary clustering pipeline we demonstrate that binary clustering solutions resemble conventional clustering solutions for large clusters, but show less resemblance for smaller clusters. We also show that the Leiden community detection algorithm can achieve higher cluster quality compared to the Louvain algorithm for the binarized data. Subject scRNA-seqbinary clusteringcommunity detection To reference this document use: http://resolver.tudelft.nl/uuid:02fc7829-b616-4a7b-9aae-63a730cedbcc Part of collection Student theses Document type bachelor thesis Rights © 2023 Jurriën Theunisz Files PDF Final_Paper.pdf 630.87 KB Close viewer /islandora/object/uuid:02fc7829-b616-4a7b-9aae-63a730cedbcc/datastream/OBJ/view