This is part 1 of step-by-step tutorial of Weighted Gene Co-expression Network Analysis (WGCNA).
In this video I demonstrate how to perform Weighted Gene Co-expression Network Analysis (WGCNA) using a RNA-Seq dataset. I go over data manipulation, methods to detect outlier genes and samples in the dataset, normalization, picking soft threshold, identifying modules and visualizing modules as a dendrogram. I hope you find this video helpful! I look forward to your comments in the comment section below!
Part 2 of this tutorial:
• Weighted Gene Co-expre...
Data:
www.ncbi.nlm.n...
Code:
github.com/kpa...
WGCNA Tutorial:
horvath.geneti...
Chapters
0:00 Intro
0:40 WGCNA Workflow steps at a glance
1:09 Study Design
1:57 Fetch Data and read data in R
2:56 Get metadata using GEOquery package
5:00 Manipulate expression data
8:53 Quality Control - Remove outlier samples and genes; using goodSampleGenes()
11:27 Detecting outliers using hierarchical clustering
12:22 Detecting outliers using Principal Component Analysis (PCA)
17:16 Data Normalization using vst() from DESeq2 package
20:51 filtering out genes with low counts
22:38 Pick soft threshold
28:48 Identify Modules
31:15 maxBlockSize parameter
33:35 Get module eigengenes
34:34 Visualize modules as dendrogram
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To get in touch:
Website: bioinformagici...
Github: github.com/kpa...
Email: khushbu_p@hotmail.com
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Негізгі бет Weighted Gene Co-expression Network Analysis (WGCNA) Step-by-step Tutorial - Part 1
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