Single-cell gene expression analysis helps biologists and bioinformaticians reveal complex and rare cell populations, uncover regulatory relationships among genes and analyze and visualize gene expression differences among different cell types, or within a unique cell type. In this talk we will explore new tools for analyzing, interpreting and explore scRNA-seq data and the underlying biology. We will also show how to integrate ‘omics datasets from different platforms to gain insights into the biology and molecular drivers of specific cell populations.
1. How to analyze scRNA-saq data without a bioinformatician or learning code
2. How to leverage automatic cell annotation to streamline your workflow
3. How to quickly comb millions of cells to identify