Important update (January 20th, 2011): the data below have been corrected for the BCR batch which is not necessarily the processing batch. The dataset needs to be reanalyzed.

Correlation between BCR batch and the processing batch for 27k arrays(January 20, 2012)

Batch vs clinical traits

Number of batches is 12. Correlation between batch and center:

table(two,batchID)
    batchID
two  0689 0848 0979 1096 1198 1440 1551 1633 1818 1871 1947 2043
  18    0    0   12    2    0    2    0    2    0    0    0    0
  21   13    0    0    0    0    0    0    4    0    0    0    0
  22    9    0    0    0    6    3    0   12    2    0    2    0
  33    0    0    0    0    4    5    0    0    1    0    1    0
  34    0    6    0    0    0    2    0    1    7    1    1    0
  37    0    0    2    6    1    0    0    1    0    0    0    0
  39    0    0    0    0    0   12    0    0    4    0    0    0
  43    0    3    2    0    0    0    2    1    4    0    1    0
  46    0    0    5    0    0    0    0    0    2    0    0    0
  51    0    0    0    3    0    0    0    0    0    0    0    1
  56    1    0    0    0    0    0    0    2    2    0    0    6
  60    0   20    0    0    1    0    0    0    1    2    0    2
  63    0    0    0    0    0    2    0    0    1    0    4    0
  66    0   18   15    0    6    0    0    0    0    0    0    0
  70    0    0    0    0    0    0    0    0    2    0    0    0
  77    0    0    0    0    0    0    0    0    0    0    4   10
  79    0    0    0    0    0    0    0    0    0    0    1    0
  85    0    0    0    0    0    0    0    0    3    0    1    0
  90    0    0    0    0    0    0    0    0    0    0    1    0
  92    0    0    0    0    0    0    0    0    0    0    0    2
  94    0    0    0    0    0    0    0    0    0    0    1    0
  96    0    0    0    0    0    0    0    0    0    0    0    2
  98    0    0    0    0    0    0    0    0    0    0    0    1

Significant batch/clinical traits correlations (complete list can be found here):

Batch vs survival

Again, for this type of cancer clinical traits file contains days to last known alive but it has more NAs than days to the last follow up so I will use the latter for construction of the survival object.

Call:
coxph(formula = survivalObject ~ batchVector)

  n= 223, number of events= 92

                    coef exp(coef) se(coef)      z Pr(>|z|)
batchVector0848 -0.14217   0.86748  0.37975 -0.374  0.70813
batchVector0979 -0.23685   0.78911  0.42661 -0.555  0.57877
batchVector1096  1.66699   5.29619  0.60925  2.736  0.00622 **
batchVector1198 -0.13837   0.87077  0.42245 -0.328  0.74325
batchVector1440 -0.25689   0.77345  0.38754 -0.663  0.50741
batchVector1633  0.27021   1.31025  0.37760  0.716  0.47423
batchVector1818 -0.21253   0.80853  0.46395 -0.458  0.64688
batchVector1947 -0.05172   0.94959  0.48598 -0.106  0.91524
batchVector2043  0.06161   1.06355  1.03684  0.059  0.95261
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
batchVector0848    0.8675     1.1528    0.4121     1.826
batchVector0979    0.7891     1.2672    0.3420     1.821
batchVector1096    5.2962     0.1888    1.6046    17.481
batchVector1198    0.8708     1.1484    0.3805     1.993
batchVector1440    0.7735     1.2929    0.3619     1.653
batchVector1633    1.3102     0.7632    0.6251     2.746
batchVector1818    0.8085     1.2368    0.3257     2.007
batchVector1947    0.9496     1.0531    0.3663     2.462
batchVector2043    1.0636     0.9402    0.1394     8.116

Rsquare= 0.041   (max possible= 0.973 )
Likelihood ratio test= 9.36  on 9 df,   p=0.4044
Wald test            = 12.53  on 9 df,   p=0.1849
Score (logrank) test = 15.53  on 9 df,   p=0.07747

On overall, correlation of batch with survival is not significant. There is one batch (1096) that seems to be somewhat more involved and it has only 11 patients. When I removed all patients from that batch no other batches showed completely insignificant correlation with survival.

DNA methylation

27k (normal Level 1 format: methylated intensities are in the first column, unmethylated intensities are in the forth column), convert to M value, didn't slit into the red and green. 134 patients, matched to the technical clinical information 133 patients. Work with them. SVD:

After fixing the inconsistencies in the concentration column (I had 0.13 ug/uL and .13 ug/uL and like for all other concentrations), converting concentration and plate_column to factors here is the summary of the technical variables:

> summary(tech)
 batchID       amount      concentration plate_column   plate_row
 0689:22   13.3 uL:97   0.13 ug/uL:11    1:37         A      :19
 0848:46   26.7 uL:36   0.14 ug/uL:35    2:35         B      :19
 0979:36                0.15 ug/uL:59    3:26         C      :18
 1096:11                0.16 ug/uL:23    4:11         D      :16
 1198:18                0.17 ug/uL: 5    5:16         G      :16
                                         6: 8         E      :15
                                                      (Other):30
       shortDay
 10-3-2010 :46
 14-7-2010 :11
 18-11-2009:22
 30-8-2010 :18
 5-5-2010  :36

Correlation of the first 6 PCs with the tech variables:

> x
        batchID       amount concentration plate_column plate_row     shortDay
V1 1.219915e-14 1.512302e-15    0.22695068  0.004764525 0.5279767 1.219915e-14
V2 1.906561e-01 4.091184e-01    0.96296059  0.292069252 0.1656324 1.906561e-01
V3 7.464626e-02 6.984062e-02    0.22476713  0.467718779 0.5705236 7.464626e-02
V4 2.670836e-01 7.040891e-01    0.78009461  0.638345228 0.2242532 2.670836e-01
V5 5.721394e-01 7.689742e-01    0.42172107  0.348612502 0.6017037 5.721394e-01
V6 2.122370e-13 9.075951e-02    0.07250977  0.015916359 0.7132870 2.122370e-13

Start by removing the batch:

This looks strange. I never looked at the data distribution after normalization. Is it ok?

Correlation with the technical variables:

> x
     batchID    amount concentration plate_column  plate_row  shortDay
V1 0.9882080 0.9838389     0.9665557   0.45086339 0.08627734 0.9882080
V2 0.9975700 0.8632969     0.7730067   0.60414536 0.57298681 0.9975700
V3 0.9998216 0.9394499     0.3675916   0.02054405 0.88934477 0.9998216
V4 0.9994017 0.9313953     0.8917591   0.77577312 0.15250595 0.9994017
V5 0.9993497 0.9959595     0.2796044   0.45763689 0.62038204 0.9993497
V6 0.9766655 0.6485570     0.8141023   0.62257929 0.25956284 0.9766655

Removing batch fixed correlations with other technical variables. Consider data to be normalized?
ExpressionSet object is available.

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