e-learning
Clustering 3K PBMCs with Seurat
Abstract
Single cell RNA-seq analysis enables us to explore differences in gene expression between cells. It can reveal the heterogenity within cell populations and help us to identify cell types that could play roles in development, disease, or other processes. Single cell omics is a relatively young field, but there are a few commonly-used analysis pipelines that you will often see in the literature. In this tutorial, we will use one of these pipelines, Seurat, to cluster single cell data from a 10X Genomics experiment. You can follow the same analysis using the Scanpy pipeline in the Clustering 3K PBMCs with Scanpy tutorial.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- How can we identify cell types in single cell RNA-Seq data?
- What are the steps for clustering single cell data with Seurat?
Learning Objectives
- Explain the steps involved in clustering single cell data
- Evaluate the quality of single cell data and filter out low quality cells
- Prepare single cell data for analysis with Seurat
- Perform clustering with Seurat
- Be ready to apply the Seurat pipeline to new datasets
Licence: Creative Commons Attribution 4.0 International
Keywords: 10x, Single Cell
Target audience: Students
Resource type: e-learning
Version: 1
Status: Active
Prerequisites:
- Dealing with Cross-Contamination in Fixed Barcode Protocols
- Introduction to Galaxy Analyses
- Pre-processing of 10X Single-Cell RNA Datasets
- Pre-processing of Single-Cell RNA Data
Learning objectives:
- Explain the steps involved in clustering single cell data
- Evaluate the quality of single cell data and filter out low quality cells
- Prepare single cell data for analysis with Seurat
- Perform clustering with Seurat
- Be ready to apply the Seurat pipeline to new datasets
Date modified: 2025-01-23
Date published: 2025-01-23
Contributors: Pavankumar Videm, Saskia Hiltemann
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