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A Novel Mediation Method for Metagenomic Studies and Deep Learning Methods for Single-cell RNA-Seq Studies

Ph.D. Final Oral Dissertation Defense

A Novel Mediation Method for Metagenomic Studies and Deep Learning Methods for Single-cell RNA-Seq Studies
Ph.D. Final Oral Dissertation Defense
Location: Zoom - link in group email
Presenter: Meng Lu, Statistics & Data Science

A Novel Mediation Method for Metagenomic Studies and Deep Learning Methods for Single-cell RNA-Seq Studies 

Meng Lu 

 

A comprehensive understanding of human health and diseases requires interpretation of molecular complexity and variation at multiple levels, such as the genome, transcriptome, metagenome, etc. In this research, I focus on developing novel methods in integrative analysis on multi-omic data and imputation methods for single-cell transcriptomic data. In the first project, a new mediation analysis procedure is proposed to integrate the high dimensional microbial data and host transcriptomic data coupled with clinical outcomes. The second and third projects focus on imputing single-cell RNA sequencing data that are large-scale and highly sparse (i.e., with excessive zeros). Adapting the generative adversarial networks, I have developed two new imputation methods in recovering gene expression, which then improves the accuracy in constructing relationships among cells. Through a series of comprehensive simulation studies and real data analyses, the proposed mediation approach has been demonstrated the best performance, compared to the existing approaches; the proposed deep-learning-based imputation methods have been shown outperforming the available imputation methods in cell type visualization, cell type identification, and lineage reconstruction.