Understanding Cellular Dynamics
During normal development as well as different physiological and pathophysiological processes, cells undergo a number of functionally distinct molecular states and differentiation processes. Recent advances in single-cell sequencing technologies have enabled the generation of large-scale single-cell datasets that measure single or multiple modalities (chromatin accessibility, DNA sequence, gene expression, surface proteins, etc.) simultaneously from multitude of single cells (see figure below). Such datasets help to curate catalogs of cellular identities across tissues and organisms and provide the substrate for uncovering the relationships between biomolecules in single cells. New computational methods are needed for understanding these interactions and cellular states by handling the massive-scale, multi-modal, noisy single-cell measurements. We are interested in developing computational frameworks for handling the single-cell datasets for understanding the following problems
Detecting Cell Types from Multi-modal Datasets: Cell clusters can be detected from multi-modal single-cell datasets. Such datasets often couples single-cell RNA-sequencing with the measurement of another cellular feature - epigenomic state, surface proteins, etc.
Elucidating Differentiation Trajectory: The cellular differentiation dynamics can be elucidated by deriving the pseudotemporal order of cells on a manifold of cellular states. Our aim is to employ multi-modal datasets for this task.