Single-cell RNA sequencing (scRNA-seq) experiments enable gene expression measurement at a single-cell resolution, and provide an opportunity to characterize the molecular signatures of diverse cell types and states in tissue development and disease progression. However, it remains a challenge to construct a comprehensive view of single-cell transcriptomes in health and disease, due to the knowledge gap in properly modeling the high-dimensional, sparse, and noisy scRNA-seq data. In this talk, I will introduce two statistical methods we have developed for analyzing and comparing single-cell gene expression data. The first one is an integration method which enables joint analysis of single-cell samples from different biological conditions. This method can learn coordinated gene expression patterns that are common among, or specific to, different biological conditions, and identify cellular types across single-cell samples. I will also discuss the applicability of our method in diverse biomedical problems. The second one is a computational method for identifying, quantifying, and comparing RNA transcripts from scRNA-seq data. Accurate and sensitive profiling of RNA transcripts is of great importance in understanding the mechanisms and consequences of gene expression regulation and can have diagnostic values in clinical settings. We propose a method to address computational questions arising from this biological problem.