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