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Weighted Gene Co-expression Network Analysis (WGCNA) Detailed Workflow Steps | Bioinformatics 101

In this video I continue discussing Weighted Gene Co-expression Network Analysis (WGCNA) by going into the details of workflow steps and providing intuition behind each step. I hope you find this video helpful! I look forward to your comments in the comment section below! WGCNA resources: 1. https://bmcbioinformatics.biomedcentr... 2. https://horvath.genetics.ucla.edu/htm... 3. https://academic.oup.com/bib/article/... Chapters: 0:00 Intro 0:58 Quick recap on basic idea behind WGCNA 1:54 WGCNA Workflow Steps 2:49 Basic Idea behind constructing networks 4:55 Step 1: Construct a weighted correlation matrix 6:40 Hard Thresholding (Unweighted Networks) 7:59 Soft Thresholding (Weighted Networks) 9:10 What is a scale-free network? 11:47 Step 2: Identify modules 12:38 Merge similar modules 13:41 Step 3a: Correlate modules with phenotypes/traits 14:58 Step 3b: Identify driver genes 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] #bioinformagician #bioinformatics #wgcna #coexpressionnetworks #geneexpression #scalefreenetworks #proteinproteininteractionnetworks #sequencing #coverage #samtools #depthofsequencing #samflag #sam #bam #alignment #phred #fasta #fastq #singlecell #10X #ensembl #biomart #annotationdbi #annotables #affymetrix #microarray #affy #ncbi #genomics #beginners #tutorial #howto #omics #research #biology #GEO #rnaseq #ngs