Advancing Machine Learning
Single-cell atlas projects are rapidly generating single-cell/nuclei mRNA sequencing (sc/snRNA-seq) and single-cell assay for transposase-accessible chromatin (scATAC-seq) data. My lab’s work has focused on integrating single-cell transcriptomics with genomics to assess expressed mutations (e.g. Müller et al., 2016), an important topic relevant to profiling clinical cancer specimens. This project builds on that work to address 3 significant gaps in the clinical application of single-cell ‘omics: 1) methods that integrate scRNA-seq and/or scATAC-seq with DNA sequencing (DNA-seq) to assess a clinical sample’s composition and the lineage relationships of sequenced cells; 2) algorithms to project single-cell data across reference atlases; 3) predictive models of disease progression.