Integration of multiple types of single-cell data with Seurat v3
Large datasets, in particular single cell datasets, pose a challenge for integration across different samples and multiple data types (gene expression, chromatin accessibility, spatial). We invite you to a webinar to learn about new updates for the R package Seurat (version 3) to address these challenges, with a focus on new features for diverse biological disciplines.

The webinar will be presented by our guest speaker, Dr. Rahul Satija of the NYGC and NYU, followed by a live Q&A session.

In this webinar you will learn about:

- Improved and expanded methods for single-cell RNA-seq integration, including datasets collected across different donors and species for assembly into a reference ‘atlas’

- New methods for harmonizing and classifying cells based on chromatin accessibility (scATAC-seq) and scRNA-seq profiles, as well as transferring information between spatial and sequencing-based profiles

- An efficiently restructured Seurat object, with an emphasis on the analysis of multi-modal data, for example, from CITE-seq

- A new normalization procedure that effectively mitigates the effect of technical variation while preserving biological heterogeneity

Can't make it live? No problem, register here and we'll send you an email when the recording is available.

Jul 17, 2019 09:00 AM in Pacific Time (US and Canada)

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