Statistical Bioinformatics Webinar: Zanini -- The Art of Generating Hypotheses from Single Cell Data

Single cell transcriptomic data are being ammassed by many laboratories and are
revealing an amazing and sometimes overwhelming degree of heterogeneity within
organisms, tissues, and even within each individual cell type. The cell similarity graph
or network is a mathematical object at the core of such data sets, encoding phenotypic
heterogeneity in a simplified yet powerful form. I will give an overview of my lab
operations, centered around cell graphs and aimed at generating sound and interesting
hypotheses for biomedicine via data exploration and deep experimental collaborations.
I will present northstar, a new cell clustering/classification approach that is
particularly well suited for cancer and developmental biology. I will then discuss two
of our recent adventures in biomedicine: (1) constructing a cell atlas of the neonatal
lung and (2) understanding the corruption of hematopoietic gene regulatory networks
during acute myeloid leukemia.