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Density plots by the color of the dye of the first 3 eigenarrays:
Consultation with Brig and Justin: don't split the data, keep all probes together.
Correlation with adjustment and biological variables:
PC | Batch | Center | Amount | Concentr. | Day | Month | Column | Row | Year | Grade | Stage | Age |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.1404 | 0.1263 | 0.004464 | 0.559545 | 0.120663 | 0.966426 | 0.423536 | 0.258863 | 0.036442 | 0.06361 | 0.35619 | 0.006236 |
2 | 2.2e-16 | 2.2e-16 | 2.447e-18 | 6.019e-03 | 2.198e-51 | 3.374e-32 | 1.389e-01 | 1.027e-01 | 2.397e-26 | 0.06419 | 0.12857 | 0.3884 |
3 | 8.057e-05 | 0.001862 | 0.0030439 | 0.5574784 | 0.0001560 | 0.0002149 | 0.7666292 | 0.5092963 | 0.0012852 | 0.6350 | 0.8488 | 0.1439 |
4 | 0.9221 | 0.2629 | 0.5526 | 0.3857 | 0.9925 | 0.7725 | 0.3971 | 0.8410 | 0.7211 | 0.0750 | 0.6507 | 0.0003187 |
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 |
|---|
> 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)
|
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.