Ó³»­´«Ã½

Department of Biomedical Informatics, Harvard Medical School
From one to millions of cells: computational challenges in single-cell analysis

Abstract:  Over the last five years, our ability to isolate and analyze detailed molecular features of individual cells has expanded greatly. In particular, the number of cells measured by single-cell RNA-seq (scRNA-seq) experiments has gone from dozens to over a million cells, thanks to improved protocols and fluidic handling. Analysis of such data can provide detailed information on the composition of heterogeneous biological samples, and variety of cellular processes that altogether comprise the cellular state. Such inferences, however, require careful statistical treatment, to take into account measurement noise as well as inherent biological stochasticity. I will discuss several approaches we have developed to address such problems, including error modeling techniques, statistical interrogation of heterogeneity using gene sets, and visualization of complex heterogeneity patterns, implemented in PAGODA package. I will discuss how these approaches have been modified to enable fast analysis of very large datasets in PAGODA2, and how the flow of typical scRNA-seq analysis can be adapted to take advantage of potentially extensive repositories of scRNA-seq measurements. Finally, I will illustrate how such approaches can be used to study transcriptional and epigenetic heterogeneity in human brains.

Harvard Medical School, Kharchenko Lab
Primer: Linking genetic and transcriptional intratumoral heterogeneity at the single cell level