Recent technological advancements have enabled the profiling of a large number of
genome-wide features in individual cells. However, single-cell data present unique …

Abstract scRNA-seq dataset integration occurs in different contexts, such as the identification
of cell type-specific differences in gene expression across conditions or species, or batch …

Motivation Rapid advances in single cell RNA sequencing (scRNA-seq) have produced
higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly …

The maintenance and transition of cellular states are controlled by biological processes.
Here we present a gene set-based transformation of single cell RNA-Seq data into …

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA
sequencing for many biological and medical applications as it allows users to measure gene …

Motivation: In single-cell RNA-sequencing (scRNA-seq) experiments, RNA transcripts are
extracted and measured from isolated cells to understand gene expression at the cellular …

Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in
recent years, bringing with it new challenges in data processing and analysis. This has led …

Background Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation
in transcription, suggesting that expression of many genes at the single-cell level are …

Single cell RNA-seq (scRNA-seq) over specified time periods has been widely used to
dissect the cell populations during mammalian embryogenesis. Integrating such scRNA-seq …

This dissertation presents a new framework for extracting signal from high-dimensional data
using a latent-space construction with applications in genomic data sets. This construction …