research overview
Cancer treatments lead to favorable outcomes only in a subset of patients partly due to significant heterogeneity in tumor cells. Towards developing personalized treatments tailored to each patient and the composition of cell types in each tumor, our research aims to characterize the heterogeneous populations of interacting cells in the tumor microenvironment and their dysregulated circuitry with an unsupervised approach.
Recent advances in single-cell genomic technologies have empowered us to profile heterogeneous phenotypes at the resolution of individual cells. However, analysis of single-cell data involves major statistical challenges. Our interdisciplinary approach involves developing novel machine learning methods to address the statistical and computational challenges inherent to high-dimensional single-cell data.
By integrating single-cell and multi-omics data using probabilistic modeling approaches, we aim to infer dysregulated programs driving cancer stem cells as well as the reprogramming of immune cells leading to immune dysfunction. We are also interested in developing statistical models for studying the temporal dynamics of cell populations and their underlying circuitry in response or resistance to immunotherapies.
Ultimately, these interpretable frameworks applied to various cancer systems can guide and improve therapies by accounting for diversity and plasticity of cells.
By integrating single-cell and multi-omics data using probabilistic modeling approaches, we aim to infer dysregulated programs driving cancer stem cells as well as the reprogramming of immune cells leading to immune dysfunction. We are also interested in developing statistical models for studying the temporal dynamics of cell populations and their underlying circuitry in response or resistance to immunotherapies.
Ultimately, these interpretable frameworks applied to various cancer systems can guide and improve therapies by accounting for diversity and plasticity of cells.
