Similarity metrics for binary cell clustering

Similarity metrics for binary cell clustering: How close can we get to state-of-the-art ?

Golik, Bartek (TU Delft Electrical Engineering, Mathematics and Computer Science)

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

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

Analysing single-cell RNA sequencing data is becoming an increasingly tedious task as the size of data sets grows. As a proposed solution, recent discoveries suggest that these data sets can be binarized without losing much information. This in turn should allow for memory and time efficient methods of storage and computation. Numerous analyses techniques require cell clustering as a preliminary procedure, which suggests the need to evaluate binary representation performance under that context. In this work we present a comparison between binary clustering results and the state-of-the-art, with a focus on similarity metric choice and the impact on intermediate steps of the procedure (i.e. similarity matrices and kNN graphs). The method was evaluated on single-cell transcriptomic data sets, utilizing a combination of R and C++ as an evaluation framework. Through these means we found that some of the similarity metrics operating on continuous input can possibly be reproduced with similarity metrics operating on binary input.

single-cell RNA sequencing
single-cell analysis
clustering
Similarity Metrics

http://resolver.tudelft.nl/uuid:744f8be7-37bc-4dd4-a66e-3003f72429ad

Student theses

bachelor thesis

© 2023 Bartek Golik