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Ph.D. Defense: Tianyu Wang

July 8 @ 2:00 pm - 3:30 pm EDT

Title: Developing Computational Methods for Single-cell RNA Sequencing Data Analysis

Student: Tianyu Wang

Major Advisor: Prof. Sheida Nabavi 

Associate Advisors: Prof. Ion Mandoiu and Prof. Derek Aguiar

Date/Time: Thursday, July 8th, 2021, 2:00 PM

Meeting link:

Meeting number: 120 671 8966

Password: JwDsxAY2p32   


Single-cell RNA sequencing (scRNAseq) enables to uncover the cell-specific changes in transcriptome which are missed by bulk sequencing. This emerging and fast-growing sequencing technology has had a major impact on several fields, including microbiology, neurobiology, immunology, developmental biology, cancer, and stem cell. With rapid advances in single-cell technologies, the data continues to grow in size and complexity. The scalable, flexible, and robust computational methods for analyzing these data become an urgent need. Due to the low amount of RNAs from a single cell and low capture efficiency of sequencing technologies, single-cell sequencing posts new challenges in data analysis including data multimodality, extensive noise, and dropout (missing data). To address the new challenges of single-cell data analysis, novel computational methods need to be developed.

In this study, we developed the new methods that employ non-parametric approaches and incorporate prior biological knowledge about gene interactions for four main applications in scRNAseq data: 1-Differential gene expression analysis, 2-cell clustering analysis, 3-imputation for drop-out zero expressions, and 4-cell classification analysis. For differential gene expression analysis in the multimodal scRNAseq data, we developed a new method based on the Earth Mover’s Distance (EMD). The EMD is a non-parametric method to measure the distance between two distributions by solving the transportation optimization problem. For cell clustering analysis, we developed a new distance metric for pairs of cells that synthesizes the prior knowledge of gene relationship and gene expression values. We used a word embedding approach to obtain vector representations for genes and used the vectors to compute the distance between genes. For estimating the zero expression values in scRNAseq data, we employed a method based on the low rank matrix completion with side information approach that taking advantage of gene associations as prior knowledge for better imputation. For classifying the cells, we proposed an end-to-end deep learning model that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks.


July 8
2:00 pm - 3:30 pm EDT

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