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

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

bioinformagician via YouTube Direct link

Intro

1 of 16

1 of 16

Intro

Class Central Classrooms beta

YouTube playlists curated by Class Central.

Classroom Contents

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

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Intro
  2. 2 WGCNA Workflow steps at a glance
  3. 3 Study Design
  4. 4 Fetch Data and read data in R
  5. 5 Get metadata using GEOquery package
  6. 6 Manipulate expression data
  7. 7 Quality Control - Remove outlier samples and genes; using goodSampleGenes
  8. 8 Detecting outliers using hierarchical clustering
  9. 9 Detecting outliers using Principal Component Analysis PCA
  10. 10 Data Normalization using vst from DESeq2 package
  11. 11 filtering out genes with low counts
  12. 12 Pick soft threshold
  13. 13 Identify Modules
  14. 14 maxBlockSize parameter
  15. 15 Get module eigengenes
  16. 16 Visualize modules as dendrogram

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.