Wednesday, December 9, 2020 - 11:00 to 12:30
SCHOOL OF COMPUTER SCIENCE
The School of Computer Science is pleased to present...
MSc Thesis Proposal by: Saiteja Danda
Date: Wednesday December 9, 2020
Time: 11:00 am to 12:30 pm
ZOOM URL: https://zoom.us/j/94179583413?
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Rapid progress in single-cell technologies has brought new insights into complex biological systems. Single-cell experiments at the cellular level and gene level allow researchers to uncover potential information such as gene regulatory relationships, differential expressions, trajectories of distinct cell lineages in development. In addition, identifying rare cell populations is one of the prominent research areas because knowledge of cell types allows finding markers for specific cells or sub-types and understanding the disease's heterogeneity. Computational techniques such as clustering and unsupervised learning methods are the most suitable approaches for the pre-processing step in Single-cell RNA sequencing (scRNA-seq) data analysis. They can identify a group of genes that belong to a specific cell type based on similar gene expression patterns. However, due to the sparsity and high-dimensional nature of the data, classical clustering methods are inefficient. Therefore, the use of dimensionality reduction techniques to improve clustering results is crucial. We use a pipeline-based analysis that includes pre-processing of the data, followed by Modified Locally Linear Embedding (MLLE) as dimensionality reduction, clustering, and then identifying different cell types and marker genes.
Keywords: Single-cell RNA sequencing, markers, Modified Locally Linear Embedding, clustering, marker genes.
Internal Reader: Dr. Ahmad Biniaz
External Reader: Dr. Phillip Karpowicz
Advisor: Dr. Luis Rueda
MSc Thesis Proposal Announcement
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