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LUSC lung squamous cell carcinoma

LUSC lung squamous cell carcinoma

Jan 20, 2012

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 ):

 

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.