Weighted Gene Co-expression Network Analysis (WGCNA) Step-by-step Tutorial - Part 1

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-expression Network A...  

Data:
https://www.ncbi.nlm.nih.gov/geo/quer...

Code:
https://github.com/kpatel427/YouTubeT...


WGCNA Tutorial:
https://horvath.genetics.ucla.edu/htm...


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

You can show your support and encouragement by buying me a coffee:
https://www.buymeacoffee.com/bioinfor...

To get in touch:
Website: https://bioinformagician.org/
Github: https://github.com/kpatel427
Email: [email protected]

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