[BioC] Seeking assistance on ROC

[Susan Bosco]

Dear friends,

I have been trying to perform ROC analysis with ROCR. Since there is not much support for the queries I have switched to ROC package of Bioconductor.

I'm trying to perform ROC analysis on Methylation data obtained from MeDiP experiment.The data set has values ranging from 0 to as large as 5. 

I have a couple of doubts with ROC as I had with ROCR.
1. When provided with different threshold values such as 0.6,0.7,0.8,0.9,each time I've got a plot which has the same curve as shown in the attached pdf.There's no change whatsoever in the curve with the different thresholds applied.Is the result
 what i'm getting on the data set appropriate?(I've come across research papers with ROC analysis being implemented on Methylation data)

2. As ROC provides knowldege about the cut-off value for micro array data,while assigning a cut-off value,should one take into account the value of threshold given in ROC or the accuracy value?

Following is my sessional info.

load("RGKma.RData")
state <-ifelse(RGKma$M[1:100,3] > 0.9, 1,0)
print("RGKma$M:");print(RGKma$M[1:100,3])
print("state:");print(state)
data<-RGKma$M[1:100,3]
R1<-rocdemo.sca(truth=state,data,dxrule.sca)
pdf("rocK.pdf")
plot(R1,col = "red")
dev.off()
print("ROC(R1):");print(ROC(R1,.3))

[1] "RGKma$M:"
  [1]  2.10538709 -0.07335174  2.13920582  0.18499421  3.30846203  1.69065450
  [7]  4.24969667  1.37415619  1.65769067  5.39253767  1.19349192  5.40321575
 [13]  3.06468274  1.34311072  0.68093156  4.03579639  2.91909842  3.36384055
 [19]  3.54968030  4.06977722  2.31968962  3.17237025  2.80040216  3.01874372
 [25]  1.89894809  4.17251372 -0.92690849  2.72505883  1.10609889  2.33584882
 [31]  0.09886450  3.30066347  2.66466248  1.39238431  2.38782229  4.19572478
 [37]  3.97185357  0.38627851 -0.09439237 -0.22948185  3.45955944  0.64538744
 [43]  1.02627932 -0.53789425  4.17758537  2.87612185  3.25867248  1.89058878
 [49]  2.71612450  3.06751911  2.63941028  1.03250743  2.07739372 -0.11727572
 [55]  3.66338130  2.52249841  0.05683122  1.90834958  4.25784185  1.87577855
 [61]  0.21814006  0.98911168  1.63475517  4.57600122  0.99326629  1.86706117
 [67]  1.27215099  2.23056201 -0.81404957  1.12010588  1.62733217  0.41223049
 [73]  3.43584658  3.78533569  2.33141286  3.15227631  1.51317488  3.37017353
 [79] -0.57605695  2.96351684  2.82082253  2.85149236  1.43692942 -0.49898928
 [85] -0.81504931 -0.75064053  1.11314716  2.51744122  2.49526189 -1.17086212
 [91]  1.11677841  0.51370382  3.24834409  0.40958307  0.39834589  1.28139084
 [97]  1.24613108  3.91323816  2.06097801  2.88980181
[1] "state:"
  [1] 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1
 [38] 0 0 0 1 0 1 0 1
 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1
 [75] 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0 0 1 1 1 1 1
[1] "ROC(R1):"
[1] 1

Thanking you in anticipation,
Susan,
M.Sc. in Molecular Biology and Human Genetics,
Manipal Life Sciences Centre,
Manipal,India.



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