Defining the immune system at single cell resolution with Seurat
In this webcast, we will demonstrate how to use Seurat – an R toolkit for single cell RNA-seq – to discover, classify, and interpret cell types and states from large-scale scRNA-seq datasets. Seurat implements an unsupervised learning procedure to identify structure in cellular heterogeneity, and is tailored towards the sparse and low-coverage datasets that characterize scRNA-seq. Additionally, the package contains a suite of tools to help users visualize cellular subpopulations, define and interpret the markers which define them, and explore how they group together into broader cellular hierarchies.
We will apply Seurat to analyze a new dataset consisting of 68,000 PBMCs, sequenced using the 10x Genomics Chromium System. The software can sensitively identify dozens of both known and novel immune subpopulations, and classify them on the basis of canonical groupings, regulatory subtype, and activation state. We will walk through a suggested workflow, highlighting general key considerations for single cell analysis, and demonstrate Seurat’s broad applicability across different systems and profiling technologies (i.e. Drop-seq, Smart-Seq, etc.).
During this webcast you will learn:
- The standard workflow for unbiased cell type discovery and classification using Seurat.
- Methods to control for heterogeneity in sequencing depth and cell cycle.
- Strategies for efficiently processing large datasets, ranging up to hundreds of thousands of cells.
- Tools to visualize and interpret cellular subpopulations, and to assess the quality and reproducibility of single cell clustering.
You will also have the opportunity to ask questions of the speaker, live during the broadcast!
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