Statistical Bioinformatics Webinar: Ko -- Visualizing Trajectories from Single-Cell Time Course Data Using FLOW-MAP

Presented by Dr.  Melissa Ko (Stanford University) 

Multi-parameter single-cell measurement technologies give us an unprecedented view into
complex biological systems, but diving into this data can be a monumental task.  To
study a highly dynamic process like drug resistance in cancer, researchers may collect
this dense high-dimensional data at several timepoints that then need to be brought
together in the analysis step.  How can we extract important patterns across this time
course data? How can we spot interesting rare phenomena? To aid analysis of single-cell
time course datasets, we developed a graph-based analysis tool called FLOW-MAP.
FLOW-MAP enables researchers to visualize and then infer trajectories from data produced
in flow cytometry, mass cytometry or single-cell RNA sequencing experiments.  This
approach has been applied to investigate drug-induced apoptosis in multiple myeloma and
identify what factors may lead to a subset of cancer cells surviving our attempts to
treat this disease.  Through this example, we will explore how FLOW-MAP can be used to
gain an intuition for complex datasets, reveal patterns over time, and then communicate
these findings to our research audience.