Developing methods to analyze and model high-dimensional multi-omics data, including longitudinal omics data, to support clinical research
As scientists collect increasingly complex datasets on the molecular profiles of humans, our methods to analyze and model these datasets should advance as well. To address this need, we develop models and computational tools to analyze and integrate different types of molecular data from clinical research. Our work strives to make the modeling and analysis of high-dimensional molecular data more accessible to the clinicians and researchers who study childhood cancer and catastrophic disease.
The modeling and analysis of high-dimensional molecular data—known as omics data—is an important aspect of clinical research studies. Our group focuses on methods development and clinical collaborations to support the collection and analysis of omics data in clinical research.
Method and model development
Our latest methods development strives to build models and tools to analyze omics and/or multi-omics data collected from pediatric participants enrolled in The Environmental Determinants of Diabetes in the Young (TEDDY) international consortium study. A major focus of our work in this area is the development of models and computational tools to analyze longitudinal microbiome data, and then integrate microbiome and metabolomics (multi-omics) data collected in this large-cohort pediatric study, which will allow researchers to analyze the data simultaneously.
A project in the preliminary stages is the development of a methodology to identify longitudinal cell-type-related markers from the deconvolution of bulk gene expression data in the TEDDY study or the St. Jude LIFE cohort. Our goal is to analyze multi-layered molecular information from participants in this study as we seek to understand the effects of pediatric cancer treatments on survivors in adulthood.
Clinical research support
Our group supports the analysis of data from clinical trials focused on pediatric brain tumors, and we currently provide statistical analysis and collaboration in three ongoing phase I clinical trials. Since St. Jude is one of the collaborating institutes in the Pediatric Brain Tumor Consortium (PBTC), our group offers biostatistical services to two clinical studies conducted within that consortium. The third study we support is a current phase I/II clinical trial in pediatric low-grade glioma at St. Jude.
Our work in methodology development and clinical collaborations seeks to advance the use and analysis of omics and multi-omics data in clinical research.
Dr. Qian Li joined St. Jude as an Assistant Member of the St. Jude faculty after completing two postdoctoral fellowships at the University of Kansas Medical Center and the Moffitt Cancer Center in Florida. Dr. Li received her PhD in Statistics from the University of Missouri and now leads her group in the integrative analysis of multi-omics studies, the development of longitudinal and time series models, and building statistical methods for assessing high-dimensional omics data.