Weighted Gene Co-expression Network Analysis - Step-by-Step Tutorial - Part 1

Embark on a comprehensive step-by-step tutorial exploring Weighted Gene Co-expression Network Analysis (WGCNA) using RNA-Seq data. Learn data manipulation techniques, outlier detection methods for genes and samples, normalization procedures, soft threshold selection, module identification, and dendrogram visualization. Follow along as the instructor demonstrates each step using R, covering topics such as data retrieval, metadata extraction with GEOquery, quality control measures, hierarchical clustering, Principal Component Analysis (PCA), data normalization with DESeq2, gene filtering, and module eigengene calculation. Gain practical insights into WGCNA workflow and its application in analyzing gene co-expression networks.

Intro
WGCNA Workflow steps at a glance
Study Design
Fetch Data and read data in R
Get metadata using GEOquery package
Manipulate expression data
Quality Control - Remove outlier samples and genes; using goodSampleGenes
Detecting outliers using hierarchical clustering
Detecting outliers using Principal Component Analysis PCA
Data Normalization using vst from DESeq2 package
filtering out genes with low counts
Pick soft threshold
Identify Modules
maxBlockSize parameter
Get module eigengenes
Visualize modules as dendrogram