Thursday, April 22, 2021 - 13:30 to 15:30
SCHOOL OF COMPUTER SCIENCE
The School of Computer Science is pleased to present…
MSc Thesis Defense by: Saiteja Danda
Date: Thursday, April 22nd, 2021
Time: 1:30pm – 3:30pm
Meeting URL: https://zoom.us/j/97947372841?from=addon
Passcode: If interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca
Abstract:
Identifying relevant disease modules such as target cell types is a significant step for studying diseases and consequently leading to better diagnosis, drug discovery, and prognosis. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, which are categorized in the form of unsupervised learning methods, are the most suitable approach in scRNA-seq data analysis when the cell types have not been characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensional nature of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a pipeline to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques such as modified locally linear embedding (MLLE) and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We evaluate the intra- and inter-cluster performance based on the Silhouette score before performing a biological assessment. We further performed gene enrichment analysis across biological databases to evaluate the proposed method's performance. As such, our results show that MLLE combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different experiments.
Keywords: non-linear dimensionality reduction, clustering, single-cell RNA sequencing
MSc Thesis Committee:
Internal Reader: Dr. Ahmad Biniaz
External Reader: Dr. Phillip Karpowicz
Advisor: Dr. Luis Rueda
Chair: Dr. Hossein Fani
MSc Thesis Defense Announcement
5113 Lambton Tower 401 Sunset Ave. Windsor ON, N9B 3P4 (519) 253-3000 Ext. 3716 csgradinfo@uwindsor.ca