Biostatistics Events

Biostatistics Departmental Calendar

Event
Mon 6/1/2020 11:00AM - 1:00PM
Biostatistics Dissertation Defense
Jun Zhang-Interpretable Analysis of Multivariate Functional Data-ONLINE Biostatistics Dissertation Defense
Jun Zhang-Interpretable Analysis of Multivariate Functional Data-ONLINE
Mon 6/1/2020 11:00AM - 1:00PM
** Online/Virtual Event **

Jun Zhang of the Department of Biostatistics defends her dissertation on "Interpretable Analysis of Multivariate Functional Data". 


** Online/Virtual Event **
Sat 8/1/2020 to Thu 8/6/2020
Biostatistics Conference
Joint Statistical Meetings - - JSM 2020, Philadelphia, PA Biostatistics Conference
Joint Statistical Meetings - - JSM 2020, Philadelphia, PA
Sat 8/1/2020 to Thu 8/6/2020


The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the  Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.


Sun 3/14/2021 to Wed 3/17/2021
Biostatistics Conference
ENAR 2021 Spring Meeting of the International Biometric Society -- Baltimore Biostatistics Conference
ENAR 2021 Spring Meeting of the International Biometric Society -- Baltimore
Sun 3/14/2021 to Wed 3/17/2021


Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.


Sat 8/7/2021 to Thu 8/12/2021
Biostatistics Conference
Joint Statistical Meetings - - JSM 2021, Seattle, WA Biostatistics Conference
Joint Statistical Meetings - - JSM 2021, Seattle, WA
Sat 8/7/2021 to Thu 8/12/2021


The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the  Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.


Sun 3/27/2022 to Wed 3/30/2022
Biostatistics Conference
ENAR 2022 Spring Meeting of the International Biometric Society -- Houston Biostatistics Conference
ENAR 2022 Spring Meeting of the International Biometric Society -- Houston
Sun 3/27/2022 to Wed 3/30/2022


Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.


Sat 8/6/2022 to Thu 8/11/2022
Biostatistics Conference
Joint Statistical Meetings - - JSM 2022, Washington, DC Biostatistics Conference
Joint Statistical Meetings - - JSM 2022, Washington, DC
Sat 8/6/2022 to Thu 8/11/2022


The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the  Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.


Recent Events

Biostatistics Dissertation Defense

Zhe Sun - Novel Statistical Methods in Analyzing Single Cell 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 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 Wednesday, December 4, 2019 by Valenti, Renee Nerozzi
Created On Monday, July 8, 2019

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