Computational methods for dissecting multicellular mechanisms of complex diseases

Abstract

Single-cell genomics technologies have enabled unbiased characterization of cell types and cellular states. However, the high-dimensional nature of this data necessitates computational and statistical methods to uncover the biological processes that shape it. In my thesis research, I developed three computational methods to explore genetic regulatory mechanisms underlying common diseases and the resulting multicellular patterns of dysfunction. In the first project, I developed a method called scITD to investigate how cellular processes across distinct cell types coordinate in disease contexts. scITD identifies sets of genes in one or more cell types that co-vary together across biological samples. Through the application of this tool to various immune-cell datasets, we uncovered highly reproducible gene expression patterns associated with autoimmune patient phenotypes. In the second project, I characterized technical artifacts prevalent in imaging-based spatial transcriptomics data. These artifacts arise from the misassignment of transcript molecules to incorrect cells. I further demonstrated how these artifacts confound downstream analyses, including differential expression and cell-cell interaction inference. To address this, I jointly developed a correction method that mitigates these artifacts, thereby uncovering novel biological insights in cancer datasets. In the third project, I introduced a computational method to unravel the mechanisms of genetic variants identified from genome-wide association study loci. This method tests whether these same genetic variants also underly changes to gene expression in specific cell types or states. Applying this tool to autoimmune and neurodegenerative datasets uncovered new SNP-gene-phenotype links and localized their effects to specific cell populations, helping to refine our understanding of these pathologies.