I present a biological study of differentiating stem cells along with statistical and computational methods for the analysis of data from such studies. The motivating biological example comes from a study of steady-state tissue maintenance in the mouse olfactory epithelium, where we are interested in characterizing the transcriptional changes that drive cell fate decisions. This study uses single-cell RNA sequencing (scRNA-Seq) to quantify the transcriptional activity of hundreds of cells across thousands of genes. Analysis of such data requires an intensive computational pipeline. I present two novel tools designed to fit into such a pipeline: first, I present Slingshot, a method for modeling the developmental process and ordering cellular profiles along it; second, I present tradeR, a method for analyzing patterns of gene expression along such a developmental process and testing for meaningful differences. Together with existing tools, these methods can help us understand the molecular mechanics underlying such fundamental biological processes as organism development, tissue maintenance, and regeneration.