1. Data input and cleaning: PDF document, R script
2. Network construction and module detection
   1. Automatic, one-step network construction and module detection: PDF document, R script
   2. Step-by-step network construction and module detection: PDF document, R script
   3. Dealing with large datasets: block-wise network construction and module detection: PDF document, R script
3. Relating modules to external clinical traits and identifying important genes: PDF document, R script
4. Interfacing network analysis with other data such as functional annotation and gene ontology PDF document, R script
5. Network visualization using WGCNA functions: PDF document, R script
6. Export of networks to external software: PDF document, R script

从代码的角度，就是上面的代码，我都在视频里面运行了，没有问题，都可以得到结果。

#3. 一步法网络构建：One-step network construction and module detection
net = blockwiseModules(datExpr, power = 6, maxBlockSize = 6000,
                       TOMType = "unsigned", minModuleSize = 30,
                       reassignThreshold = 0, mergeCutHeight = 0.25,
                       numericLabels = TRUE, pamRespectsDendro = FALSE,
                       saveTOMs = TRUE,
                       saveTOMFileBase = "AS-green-FPKM-TOM",
                       verbose = 3)

#1. 可视化全部基因网络
# Calculate topological overlap anew: this could be done more efficiently by saving the TOM
# calculated during module detection, but let us do it again here.
dissTOM = 1-TOMsimilarityFromExpr(datExpr, power = 6);
# Transform dissTOM with a power to make moderately strong connections more visible in the heatmap
plotTOM = dissTOM^7;
# Set diagonal to NA for a nicer plot
diag(plotTOM) = NA;
# Call the plot function
#sizeGrWindow(9,9)
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")