Welcome!

We are a computational biology group at the Department of Biomedical Informatics of the Harvard Medical School. We are focused on the analysis of intratumoral heterogeneity in different cancer types, as well as the interactions between tumor cells and their microenvironment. We also study growth and maintenance of healthy tissues, to understand the statistical properties and feedback mechanisms that enable normal biological function.

We specialize in the development of methods for statistical and integrative analysis of the functional state of the cell from high-throughput genomic measurements, with an emphasis in the statistical modeling of the biological tissue from single-cell measurements. The wetlab component of the lab is aimed at automating and scaling up key functional genomics assays using microfluidics.

Wiring together scRNA-seq datasets

scRNA-seq is now applied in complex study designs, which can cover many samples, spanning multiple individuals, conditions, or tissue compartments. Joint analysis of such extensive, and often heterogeneous, sample collections requires a way of identifying and tracking recurrent cell subpopulations across the entire collection. Conos (Clustering On Network Of Samples) is a tool for joint analysis of such collections, that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph can then be used to propagate information between samples and to identify cell communities that show consistent grouping across broad subsets of the collected samples. Conos results enable investigators to balance between resolution and breadth of the detected subpopulations. Please see the publication for detailed description and analysis examples, as well as the github page for hands-on tutorials.