A detailed walk-through of steps to merge and integrate single-cell RNA sequencing datasets to correct for batch effect in R using the #Seurat package. I hope you liked the video. I look forward to your comments under the comments section!

1) Paper:
Modeling Hepatoblastoma: Identification of Distinct Tumor Cell Populations and Key Genetic Mechanisms through Single Cell Sequencing (scRNA-seq): [ Ссылка ]

2) Data:
GSE180665: [ Ссылка ]

3) Link to code:
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4) Seurat Integration Vignette:
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5) Additional resources:
‣ CCA method: [ Ссылка ]30559-8?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867419305598%3Fshowall%3Dtrue#secsectitle0020
‣ MNN method: [ Ссылка ]
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Chapters:
0:00 Intro
0:21 Study design
1:45 When to integrate?
2:41 Types of integration
4:17 Batch correction methods
4:58 Downloading data
7:27 Read data in R
11:08 Merge Seurat objects
14:20 QC and filtering
20:26 Do we see batch effects in our data?
23:07 Visualize merged data (before integration)
25:26 Integration steps
30:34 Visualize integrated data (after integration)
32:03 Comparing UMAPs: before integration vs after integration

Show your support and encouragement by buying me a coffee:
[ Ссылка ]

To get in touch:
Website: [ Ссылка ]
Github: [ Ссылка ]
Email: khushbu_p@hotmail.com

#bioinformagician #bioinformatics #seurat #integration #cca #R #genomics #beginners #tutorial #howto #omics #research #biology #ncbi #GEO #rnaseq #ngs

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