As single-cell RNA-seq is becoming increasingly widely used, the amount and variety of public data as well as the number of computational methods available for the analysis grow quickly. Unbiased benchmarking studies are vital in order to guide users and identify strengths and weaknesses of published methods. In a rapidly changing field, it is also important that benchmarks can easily be extended to include new methods, or variants of existing methods, as they become available. In this talk, I will describe several recent studies evaluating computational methods for clustering and differential expression analysis of single-cell RNA-seq data, and specifically discuss approaches to simplify exploration of the results and inclusion of new methods and data sets. I will also present the interactive SummarizedExperiment Explorer (iSEE) R/Bioconductor package, which allows straightforward, interactive exploratory analysis of single-cell RNA-seq as well as many other types of omics data.
Building: François Jacob
Address: Institut Pasteur, Rue du Docteur Roux, Paris, France