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since we already know from the analyses of the M value that batch, center, plate row and plate column have effect on the data, I will skip the preliminary steps and remove these factors. Also, the dataset will exclude batch number 0652.
| Code Block |
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> X<-model.matrix(~factor(batch[mask]) + adj$plate_row[mask] + adj$plate_column[mask] + factor(center[mask]))
> Xmod<-solve(t(X) %*% X) %*% t(X) %*% t(beta[,mask])
> betaRes<- beta[,mask] - t(X %*% Xmod)
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Correlation with adjustment and biological variables:
PC | Batch | Center | Amount | Concentr. | Day | Month | Column | Row | Year | Grade | Stage | Age |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 0.9830 | 0.6760 | 0.9999 | 1.0000 | 0.4995 | 0.9871 | 0.9133 | 0.3422 | 0.2915 | 0.01044 |
2 | 1 | 1 | 0.9750 | 0.7878 | 1.0000 | 1.0000 | 0.8328 | 1.0000 | 0.8487 | 0.5115 | 0.6704 | 0.4074 |
3 | 0.99 | 1 | 0.9402 | 0.7498 | 0.9994 | 1.0000 | 0.8415 | 0.9993 | 0.9304 | 0.11282 | 0.02903 | 0.0001028 |
4 | 1 | 1 | 0.9521 | 0.1648 | 1.0000 | 0.9998 | 0.5466 | 1.0000 | 0.9786 | 0.4573 | 0.6384 | 0.8565 |
It seems that with beta value I see a stronger correlation with age. First eigengene, first and second eigenarrays and the outliers:
We can proceed with building a comethylation network although the outliers look terrible.