Learn how to merge and integrate single-cell RNA sequencing datasets to correct for batch effects using the Seurat package in R. Follow a detailed workflow that covers study design, data integration types, batch correction methods, and step-by-step instructions for downloading, reading, merging, and analyzing scRNA-seq data. Explore quality control, filtering, and visualization techniques to identify and address batch effects. Compare UMAPs before and after integration to assess the effectiveness of the process. Gain practical insights into handling large-scale single-cell genomics data and applying advanced bioinformatics techniques for improved analysis and interpretation of complex biological datasets.
Integrate Single-Cell RNA-Seq Datasets in R Using Seurat - Detailed Seurat Workflow Tutorial
Overview
Syllabus
Intro
Study design
When to integrate?
Types of integration
Batch correction methods
Downloading data
Read data in R
Merge Seurat objects
QC and filtering
Do we see batch effects in our data?
Visualize merged data before integration
Integration steps
Visualize integrated data after integration
Comparing UMAPs: before integration vs after integration
Taught by
bioinformagician
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