Learning from Data in Single-Cell Transcriptomics

Music City Center 

Novel measurement platforms are allowing researchers to investigate biological processes at ever-increasing scale and ever-finer resolution, with transformative impact on both fundamental research and personalized health. Single-cell spatially-resolved transcriptomics allows the high-throughput measurement of gene expression levels for entire genomes at the resolution of single cells (vs. pools of cells), while simultaneously recording the spatial location of cells and molecules within tissues. Such resolution is crucial to address many important biological and medical questions, such as the study of stem cell differentiation, the detection of rare mutations in cancer, or the discovery of cellular subtypes in the brain.

Transcriptomic studies provide a great example of the range of questions one encounters in a data science workflow, where the data are complex in a variety of ways, questions are not always clearly formulated, there are multiple analysis steps, and drawing on rigorous statistical principles and methods is essential to derive meaningful and reliable biological results.

In this lecture, I will provide a survey of statistical questions related to the analysis of single-cell transcriptome sequencing data to investigate the differentiation of stem cells in the brain, including, exploratory data analysis, expression quantitation, cluster analysis, and the inference of cellular lineages. I will also address differential expression analysis in spatial transcriptomics.