Benchmarking of scRNA-Seq to Improve Human Health

Single-cell sequencing data has enormous potential to improve our understanding of human health, with direct applications in the areas of diagnosis and therapeutic selection. Single-cell sequencing of mRNA expression levels (scRNA-Seq) historically focused on understanding fundamental biological systems at the single-cell level, but there is an increasing emphasis on using scRNA-Seq to understand the role of single-cell variability on human health outcomes. While the exploration of single-cell human variability and its relationship to disease is advancing, the corresponding statistical methodology to handle this type of data at the human population level lags behind. 

This project will advance a coherent methodological framework to analyze the effect of single-cell variability on patient phenotypes. This framework will be used to develop methods for evaluating the degree of biological or technical differences in scRNA-Seq data obtained from patient populations, particularly those which are the result of integrating data from multiple studies or sites. These methods will include visualization tools for exploring scRNA-Seq data from patients and statistical tests and metrics for quantifying these differences. These methodologies will facilitate the evaluation of batch-correction and integration methods for scRNA-Seq data from different patient populations. The final part of this project will develop and promulgate strategies for appropriately using the methodologies for such benchmarking tasks.