...
Finally, the center effect needs to go. Variables to adjust for: batch, center, plate row, plate column. Percent Variance explained after the adjustment:
Under 14%! Still large effect, look at the variables:
...
Percent Variance explained:
PCs | Batch | Center | Day | Month | Year | Amount | Concentr. | Row | Column | Stage | Grade | Age |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2.2e-16 | 2.2e-16 | 1.6e-61 | 5.7e-39 | 3.8e-31 | 1.6e-19 | 6.9e-04 | 3.2e-02 | 2.2e-01 | 0.36 | 0.17 | 0.2778 |
Remove the batch effectbatch, center, plate row, plate column (also mask that one batch), looks at the first eigengene and the eigenarray:
P values after the adjustment:
PCs | Batch | Center | Day | Month | Year | Amount | Concentr. | Row | Column | Stage | Grade | Age |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.6857 999 | 3 0.984e-059999 | 0.4e-01995 | 0.9e-01991 | 0.7e-01956 | 0.6e-01863 | 0.4e-02074 | 0.9e-08997 | 0.7e-02626 | 0.64960 | 0.03326 | 0.881 |
...
6206 |
Look at the outliers: 
Weird!
Do one more test and remove the first eigengene together with the variables above:
Conclusion
Now scale (center=TRUE, scale=TRUE) bot datasets (red and green probes), combine them together for network construction.