Integrative Analysis of large single-cell RNA-seq collections
Integrative Analysis of Large Single-Cell RNA-Seq Collections
Harvard Medical School
Single-cell RNA-seq methods are being increasingly applied in complex study designs, which involve measurements of many samples, commonly 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. Here we describe a flexible approach, called Conos (Clustering On Network Of Samples), 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. In this way, it is possible to focus on the fine-grained clusters appearing within more similar subsets of samples, or analyze coarser clusters spanning broader sets of samples in the collection. Such multi-resolution joint clustering provides an important basis for downstream analysis and interpretation of sizeable multi-sample single-cell studies and atlas-scale collections.
Peter Kharchenko, Ph.D, is the Gilbert S. Omenn Associate Professor of Biomedical Informatics the Harvard Medical School. His lab specializes in developing statistical and computational methods for analysis of high-throughput assays, including transcriptional, epigenetic and genetic analysis. Dr. Kharchenko has received his Ph.D from the Biophysics program at Harvard University, under the mentorship of George Church. He then conducted his postdoctoral training with Peter Park at the Harvard Medical School, focusing on analysis of epigenetic regulation in model organisms and mammalian tissues.
Associate Professor of Biomedical Informatics
Harvard Medical School Boston, MA, USA
Domain 3 - U900 - CBIO - Bioinformatics, Biostatistics Epidemiology and Computational Systems