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BME Ph.D. Thesis Proposal Presentation
Hong Seo Lim
Date: April 26th, 2021
Time: 3-4 pm
Bluejeans link: https://bluejeans.com/532759351
Committee Members:
Peng Qiu, Ph.D. (Advisor)
Edward Botchwey, Ph.D.
Kavita Dhodapkar, M.D.
Eva Dyer, Ph.D.
Eberhard Voit, Ph.D.
Title: Developing Graph-based Computational Algorithms for Single-cell Data Science
Abstract:
Biological datasets are now generated more than ever as many data acquisition technologies have been developed over the years, especially single-cell technologies. Measurements of multiple parameters of single cells using flow and mass cytometry are now routinely conducted in various research areas including immunology, neuroscience, and cell signaling. Recent advancements of the next-generation sequencing technologies such as single- cell RNA sequencing now allow dramatically higher resolution of an individual cell which has led to new discoveries once unknown. With an increasing number of parameters and datasets available in the field of single-cell, various challenges arise. Some of the key aspects that need attention are (1) proper integration of single-cell datasets acquired from different technologies or affected by batch effect, (2) computational tool for flow and mass cytometry data that can perform automated manual gating and (3) an algorithm that could quantify to what extent the single-cell datasets express cluster- like or trajectory-like characteristics without prior knowledge about the data for both flow/mass cytometry and single-cell RNA sequencing data. Here we propose to provide computational tools to tackle each of the aforementioned single-cell challenges. More specifically, this project will utilize computational approaches based on graph theory to facilitate a single-cell data analysis pipeline which includes proper data integration/automated analysis, and quantification of characteristics of data.
