Dr. Frost graduated from the CQB COBRE in September of 2022 after receiving an R35 MIRA award (R35GM146586, Gene set analysis of single cell genomics). He is now working with the CQB as a Faculty Advisor to the Data Analytics Core.
Dr. Frost’s research focuses on the development of bioinformatics and biostatistical methods for analyzing high- dimensional genomic data. An important theme of this research has been the use of prior biomedical knowledge, formally encoded in an ontology, to improve statistical power, replication of results, visualization and interpretation. A specific focus of his doctoral studies, postdoctoral work and ongoing K01-funded research is gene set testing or pathway analysis. Gene set testing is an effective hypothesis aggregation method that evaluates hypotheses about biologically related groups of genes, as defined in a resource such as the Molecular Signatures Database (MSigDB). Relative to an approach that tests separate hypotheses for each gene, gene set testing can significantly improve interpretation, statistical power and replication for the analysis of high-dimensional genomic data.
Dr. Frost’s work in this area has included research on new gene set testing methods, research addressing the challenge of annotation quality, research on unsupervised gene set testing and research addressing statistical power. His current gene set testing research focuses on methods for tissue- specific gene set testing and techniques that support gene set analysis of single cell data. Other research interests include cancer genomics, tissue-specific gene function, single cell genomics, gene-environment and gene-gene interaction detection, p-value weighting and screening-testing methods, penalized regression, principal component analysis and random matrix theory.