PhD Defense by Xingyu Yang

Event Details
  • Date/Time:
    • Tuesday November 6, 2018
      2:00 pm - 4:00 pm
  • Location: Molecular Sciences and Engineering Building, room 3201a.
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Summaries

Summary Sentence: Computational approaches to intuitively analyze and visualize big data in biological research

Full Summary: No summary paragraph submitted.

In partial fulfillment of the requirements for the degree of 

Doctor of Philosophy in Bioinformatics

in the School of Biological Sciences


Xingyu Yang


Defends his thesis:
Computational approaches to intuitively analyze and visualize big data in biological research

 

Tuesday, November 6, 2018

2:00pm
Materials Science and Engineering Building 3201a

 

Thesis Advisor:

Dr. Peng Qiu
Department of Biomedical Engineering
Georgia Institute of Technology and Emory University


Committee Members:

Dr. Soojin Yi
School of Biological Sciences
Georgia Institute of Technology

 

Dr. Gregory Gibson

School of Biological Sciences
Georgia Institute of Technology

 

Dr. David Archer

Department of Pediatrics

Emory University School of Medicine

 

Dr. Ignacio Sanz
Department of Medicine

Emory University

 

Abstract:

To quantitatively understand the cell behavior in molecular level, scientists have developed technologies including high throughput sequencing and flow cytometry. High throughput sequencing can obtain the entire genome sequence and measure expression of large number of genes. Flow cytometry can measure multiple parameters of large number of cells. Both technologies generate large amount of data in high dimension. Therefore, efficient methods to analyze and interpret the data become in demand. In my thesis, I focus on developing computational methods that deliver intuitive and interpretable visualization of biological data. The first chapter describes a software named Cluster-to-Gate (C2G) that can visualize existing clustering results of flow/mass cytometry data in the format of 2D gating hierarchy. Though C2G presents a way to visualize and interpret clustering results, the visualization is still data-driven and no human-knowledge is incorporated. To overcome the limitation of C2G, the second chapter describes a framework that can learn gating approach from existing publications to build a knowledge-graph. This knowledge-graph can automatically suggest order of marker usage and gating hierarchy for new data set, which can be used to gate cell populations. The obtained cell populations are immediately matched to known cell types in the knowledge-graph, which makes them interpretable. The third chapter describe a novel algorithm (GLaMST) to reconstruct lineage tree of B cell receptor gene from high throughput sequencing data. This algorithm outperforms state-of-art in both accuracy and speed.

 

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Graduate Education and Faculty Development

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Public, Graduate students, Undergraduate students
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Phd Defense
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Oct 24, 2018 - 2:04pm
  • Last Updated: Oct 29, 2018 - 11:55am