Single cell RNA-sequencing (scRNA-seq) allows massively parallel acquisition of gene expression profiles in heterogeneous cell populations such as dissociated tissues and organs. The measured single cell transcription profiles can be used to identify cell types, cell sub-types and continuous gene expression gradients e.g. during developmental or disease processes. However, a key challenge in the analysis of scRNA-seq data is the highly discrete, sparse and variable nature of single-cell mRNA molecule counts. Specifically, high levels of sampling noise and missing data can obscure transcriptional measures of cell type similarity and render identification of co-regulated groups of genes difficult. Moreover, it is currently not possible to systematically determine the origin of cell types in complex organisms based on single cell data. Extracting such information, however, might have a drastic impact, for example in preventing, diagnosing and treating a variety of physical and mental disorders. In brief, the focus of this project is on developing and adopting new analytical approaches to efficiently use single-cell data to solve the following problems: 1. Finding informative genes that allow clustering of cells and identification of cell types 2. Analysis of co-regulated gene modules 3. Integration of other data types including lineage barcodes that allow to trace cell origins.
1. Rachel Shahan, Che-Wei Hsu, Trevor M. Nolan, Benjamin J. Cole, Isaiah W. Taylor, Anna Hendrika Cornelia Vlot, Philip N. Benfey and Uwe Ohler(2020).A single cell Arabidopsis root atlas reveals developmental trajectories in wild type and cell identity mutants. bioRxiv 2020.06.29.178863; doi: doi.org/10.1101/2020.06.29.178863