Biostatistics Dissertation Defenses

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Events that appear on this page are in the Sponsoring Department of "Biostatistics" with "Dissertation Defenses" as its Category.

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Upcoming Biostatistice Dissertation Defenses

Biostatistics Dissertation Defense

Zhe Sun - Novel Statistical Methods in Analyzing Single Cell RNA Sequencing Data

Thursday 7/25 2:00PM - 4:00PM
7139 Public Health, Peterson Seminar Room

Zhe Sun of the Department of Biostatistics defends her dissertation on “Novel Statistical Methods in Analyzing Single Cell RNA Sequencing Data”.

 Committee Chairpersons:  Ying Ding, PhD, Department of Biostatistics and Wei Chen, PhD, Department of Pediatrics

Committee Members:

Kong Chen, PhD, Department of Medicine

Ming Hu, PhD, Department of Quantitative Health Sciences, Cleveland Clinic

Yongseok Park, PhD, Department of Biostatistics

 Graduate faculty of the University and all other interested parties are invited to attend


ABSTRACT:

Understanding of biological systems requires the knowledge of their individual components. Single cell RNA sequencing (scRNA-Seq) becomes a revolutionary tool to investigate cell-to-cell transcriptomic heterogeneity, which cannot be obtained in population-averaged measurements such as the bulk RNA-Seq. The newly developed droplet-based system enables parallel processing with digital counting of thousands of single cells in a single experiment, leading to the discovery of novel cell types which facilitates newly biological discoveries. This dissertation focuses on developing novel statistical methods for analyzing droplet-based scRNA-Seq data, which includes clustering methods to identify cell types from single or multiple individuals, and a joint clustering approach to simultaneously analyze paired data from scRNA-Seq and Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-Seq), a state-of-art technology that allows the detection of cell surface proteins and transcriptome profiling within the same cell simultaneously.

In the first part of this dissertation, I developed DIMM-SC, a Dirichlet mixture model which explicitly models the raw UMI count for clustering droplet-based scRNA-Seq data and produces cluster membership with uncertainties. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. In the second part, I developed BAMM-SC, a novel Bayesian hierarchical Dirichlet mixture model to cluster droplet-based scRNA-Seq data from population studies. BAMM-SC takes raw count data as input and accounts for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Extensive simulation studies and applications to multiple in house experimental scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with considerable improved clustering accuracy, particularly in the presence of heterogeneity among individuals. In the third part, I developed RE-DIMM-SC, a novel random effects model that jointly cluster the paired data from scRNA-seq and CITE-Seq simultaneously. Simulations and analysis of in-house real data sets were performed, which successfully demonstrated the validity and advantages of our method in helping people understand the heterogeneity and dynamics of various cell populations in complex multicellular tissue or organs.  

PUBLIC HEALTH SIGNIFICANCE: Recent droplet-based single cell sequencing technology and its extensions have brought revolutionary insights to the understanding of cell heterogeneity and molecular processes at single cell resolution. I believe the proposed statistical approaches in this thesis for single cell data will improve the identification and characterization of cell subtypes from heterogeneous tissues, which is essential to fully understand cell identity and cell function.

Last Updated On Monday, July 08, 2019 by Valenti, Renee Nerozzi
Created On Monday, July 08, 2019

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Biostatistics Dissertation Defense
Zhe Sun - Novel Statistical Methods in Analyzing Single Cell RNA Sequencing Data Biostatistics Dissertation Defense
Zhe Sun - Novel Statistical Methods in Analyzing Single Cell RNA Sequencing Data
Thu 7/25/2019 2:00PM - 4:00PM
7139 Public Health, Peterson Seminar Room

Zhe Sun of the Department of Biostatistics defends her dissertation on “Novel Statistical Methods in Analyzing Single Cell RNA Sequencing Data”.


Biostatistics Dissertation Defense
Md Tanbin Rahman - Classification and Clustering for RNA-seq Data with Variable Selection Biostatistics Dissertation Defense
Md Tanbin Rahman - Classification and Clustering for RNA-seq Data with Variable Selection
Fri 6/7/2019 9:00AM - 11:00AM
7139 Public Health, Peterson Seminar Room

Md Tanbin Rahman of the Department of Biostatistics defends his dissertation on "Classification and Clustering for RNA-seq Data with Variable Selection". 


Biostatistics Dissertation Defense
Shu Wang - Clustering Methods with Variable Selection for Data with Mixed Variable Types or... Biostatistics Dissertation Defense
Shu Wang - Clustering Methods with Variable Selection for Data with Mixed Variable Types or...
Mon 4/15/2019 11:00AM - 1:00PM
7139 Public Health, Peterson Seminar Room

Shu Wang of the Department of Biostatistics defends her dissertation on "Clustering Methods with Variable Selection for Data with Mixed Variable Types of Limits of Detection". 


Biostatistics Dissertation Defense
Di Zhang - Inference on Win Ratio for Clustered Semi-Competing Risk Data Biostatistics Dissertation Defense
Di Zhang - Inference on Win Ratio for Clustered Semi-Competing Risk Data
Thu 4/11/2019 10:00AM - 12:00PM
1149 Public Health, Foster Conference Room

Di Zhang of the Department of Biostatistics defends her dissertation on “Inference on Win Ratio for Clustered Semi-Competing Risk Data”.


Biostatistics Dissertation Defense
Li Zhu - Bayesian variable selection model and differential co-expression network analysis for... Biostatistics Dissertation Defense
Li Zhu - Bayesian variable selection model and differential co-expression network analysis for...
Tue 4/9/2019 12:00PM - 2:00PM
A216 Public Health

Li Zhu of the Department of Biostatistics defends her dissertation on "Bayesian variable selection model and differential co-expression network analysis for multi-omics data integration".


Biostatistics Dissertation Defense
Xingyuan Li - Modeling Exposure-Time-Response Association in the Presence of Competing Risks Biostatistics Dissertation Defense
Xingyuan Li - Modeling Exposure-Time-Response Association in the Presence of Competing Risks
Fri 1/18/2019 1:00PM - 3:00PM
7139 Public Health, Peterson Seminar Room

Xingyuan Li of the Department of Biostatistics defends her dissertation on "Modeling Exposure-Time-Response Association in the Presence of Competing Risks". 


Biostatistics Dissertation Defense
Christopher Keener - Power and Sample Size Determination for Stepped Wedge Cluster Randomized Trials Biostatistics Dissertation Defense
Christopher Keener - Power and Sample Size Determination for Stepped Wedge Cluster Randomized Trials
Wed 7/18/2018 10:00AM - 12:00PM
7139 Public Health, Peterson Seminar Room

Christopher Keener of the Department of Biostatistics defends his dissertation on "Power and Sample Size Determination for Stepped Wedge Cluster Randomized Trials".


Biostatistics Dissertation Defense
Joanne Beer - Predicting Clinical Variables from Neuroimages Using Fused Sparse Group Lasso Biostatistics Dissertation Defense
Joanne Beer - Predicting Clinical Variables from Neuroimages Using Fused Sparse Group Lasso
Tue 7/17/2018 1:00PM - 3:00PM
7139 Public Health, Peterson Seminar Room

Joanne Beer of the Department of Biostatistics defends her dissertation on "Predicting Clinical Variables from Neuroimages Using Fused Sparse Group Lasso".


Biostatistics Dissertation Defense
John Pleis - Mixtures of Discrete and Continuous Variables: Considerations for Dimension Reduction Biostatistics Dissertation Defense
John Pleis - Mixtures of Discrete and Continuous Variables: Considerations for Dimension Reduction
Wed 5/30/2018 1:00PM - 3:00PM
7139 Public Health, Peterson Seminar Room

John Pleis of the Department of Biostatistics defends his dissertation on "Mixtures of Discrete and Continuous Variables: Considerations for Dimension Reduction".


Biostatistics Dissertation Defense
Zhou Fang - Integration and Missing Data Handling in Multiple Omics Studies Biostatistics Dissertation Defense
Zhou Fang - Integration and Missing Data Handling in Multiple Omics Studies
Thu 5/3/2018 9:00AM - 11:00AM
7139 Public Health, Peterson Seminar Room

Zhou "Ark" Fang of the Department of Biostatistics defends his dissertation on "Integration and Missing Data Handling in Multiple Omics Studies".


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Questions and Submissions

The schedule of dissertation defenses in the Department of Biostatistics is maintained by...

Renee Valenti

412-624-3023

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