Statistical Methods for Single-Cell Omics and Integrative Genomics

Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level and holds great promise for deciphering tumor heterogeneity. The data are, however, extremely sparse and noisy because of the shallow depth of coverage and the biases and artifacts that are introduced. We propose SCOPE, a normalization and copy number estimation method for the noisy scDNA-seq data. SCOPE's main features include: (i) a Poisson latent factor model for normalization, which borrows information across cells and regions to estimate bias, using in silico identified negative control cells; (ii) an expectation-maximization algorithm embedded in the normalization step, which accounts for the aberrant copy number changes and allows direct ploidy estimation without the need for post hoc adjustment; and (iii) a cross-sample segmentation procedure to identify breakpoints that are shared across cells with the same genetic background. We evaluate SCOPE on a diverse set of scDNA-seq data in cancer genomics and show that SCOPE offers accurate copy number estimates and successfully reconstructs subclonal structure. More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results. Cancer is a disease driven by genetic and epigenetic alterations, where somatic mutations including point mutations and copy number aberrations occur in tumor progression. Multiple methods have been developed to reconstruct evolutionary history by bulk whole-exome sequencing (WES) of longitudinally/spatially separated tumor samples. However, they suffer from low resolution and deconvolution ambiguity. Here we devise a framework to relate bulk genomic profiles to single-cell transcriptomic variations for understanding the functional implications of intratumor heterogeneity. Jointly analyzing single-cell RNA sequencing (scRNA-seq) and bulk WES data can boost the power for phylogeny reconstruction. We apply the method to analyze bulk WES and scRNA-seq data from breast cancer and glioblastoma patients.Doctor of Philosoph