Statistical Methods and Software for the Study of Stem Cell Differentiation Using Single-Cell Transcriptome Sequencing
Single-cell transcriptome sequencing (scRNA-Seq), which combines high-throughput single-cell extraction and sequencing capabilities, enables the transcriptomes of large numbers of individual cells to be assayed efficiently. Profiling of gene expression at the single-cell level for a large sample of cells is crucial for addressing many biologically relevant questions, such as, the investigation of rare cell types or primary cells (e.g., stem cell differentiation) and the examination of subpopulations of cells from a larger heterogeneous population (e.g., classifying cells in brain tissues).
I will discuss some of the statistical and computational issues that have arisen in the context of a collaboration with the UC Berkeley Ngai Lab concerning the analysis of olfactory stem cell fate trajectories in mice. These issues, ranging from so-called low-level to high-level analysis, include: experimental design, exploratory data analysis (EDA) of scRNA-Seq reads, quality assessment/control (QA/QC), normalization to account for nuisance technical effects, cluster analysis to identify novel cell types, cell lineage and pseudotime inference, and differential expression analysis to identify genes involved in the differentiation process.
Our statistical methods are implemented in open-source R packages released through the Bioconductor Project (http://www.bioconductor.org).