Please join us for GIDP Defense of Doctoral Dissertation
A. Grant Schissler, PhD Candidate
Lussier Research Group
Contributions to gene set analysis of correlated, paired-sample transcriptome data to enable precision medicine
Abstract: The defense will outline my contributions to gene set analysis of correlated, paired-sample transcriptome data to enable precision medicine. I will discuss three projects that involve the development of paired-sample gene set analytics. The major emphasis will be on single-subject transcriptome (i.e. gene expression data) methodology. Traditional statistical approaches are largely unavailable in this setting due to prohibitive sample size and lack of independent replication. This leads one to rely on informatic devices including knowledgebase integration (e.g., gene set annotations) and external data sources (e.g., estimation of inter-gene correlation). Common statistical themes include multivariate statistics (such as Mahalanobis distance and copulas) and large-scale significance testing. Briefly, the first work describes the development of clinically-relevant metrics of gene set (pathway) differential expression, N-of-1-pathways Mahalanobis distance (MD) scores. Next, the second article describes a testing procedure that accounts for non-trivial, inter-gene correlation. In the third article, the paired statistics are aggregated to analyze single-cell RNA-sequencing data derived from rare cells. Finally, I will demonstrate an R implementation of the statistics and visualizations developed on real patient data.