An R package for the study design and power calculation issues of Methyl-Seq experiments.
You can install this package by copying and paste the following code into R
install.packages("devtools") ## In case you didn't install devtools
devtools::install_github("gitlongor/pi0") # install the dependency pi0
devtools::install_github("epifluidlab/MethylSeqDesign")
- Call the example data. The toy example contains the mouse methylation data from 6 sample in 2 groups, each group has 3 samples.
library(MethylSeqDesign)
data(Mouse)
- Perform differential methylation analysis. prop specifies the proportion of subsampling. By subsampling, we vary the sequencing depth of pilot data.
pilot.R=1/4
R=c(1/15, 1/10, 1/8, 1/6, 1/4)
dmr.result<-DMR.analysis(N0=3,cov.matrix=Mouse$cov.matrix, methyl.matrix=Mouse$methyl.matrix, pilot.R=pilot.R, R=R)
- Predict expected discovery rate(EDR) when there are N=2,6,12,30,40,50 samples per group. EDR is the genome wide power we are interested in. EDR=the number of detected DMR/The total number of DMR. This step may take a few minutes
N=seq(4,50,2)
predict.result<-Estimate.EDR.from.pilot(dmr.result, N0=3, target.N=N)
- Interpret the results
$`EDR`
4 vs 4 6 vs 6 8 vs 8 10 vs 10 12 vs 12 14 vs 14 16 vs 16 18 vs 18 20 vs 20 22 vs 22 24 vs 24 26 vs 26 28 vs 28 30 vs 30
0.067 0.1731 0.4776 0.6715 0.7725 0.8455 0.8853 0.9156 0.9361 0.9511 0.9606 0.9682 0.9745 0.9790 0.9832
0.1 0.2602 0.5674 0.7258 0.8279 0.8792 0.9149 0.9374 0.9536 0.9637 0.9702 0.9742 0.9781 0.9827 0.9858
0.125 0.2777 0.5875 0.7377 0.8331 0.8907 0.9233 0.9460 0.9601 0.9676 0.9738 0.9790 0.9830 0.9871 0.9895
0.167 0.3248 0.6164 0.7613 0.8460 0.8927 0.9244 0.9453 0.9581 0.9677 0.9745 0.9798 0.9845 0.9881 0.9898
0.25 0.3709 0.6426 0.7874 0.8641 0.9049 0.9362 0.9535 0.9655 0.9723 0.9793 0.9838 0.9860 0.9891 0.9911
32 vs 32 34 vs 34 36 vs 36 38 vs 38 40 vs 40 42 vs 42 44 vs 44 46 vs 46 48 vs 48 50 vs 50
0.067 0.9857 0.9878 0.9893 0.9907 0.9919 0.9930 0.9937 0.9946 0.9949 0.9956
0.1 0.9882 0.9900 0.9910 0.9921 0.9937 0.9944 0.9952 0.9955 0.9965 0.9972
0.125 0.9909 0.9920 0.9929 0.9938 0.9950 0.9953 0.9957 0.9963 0.9965 0.9969
0.167 0.9917 0.9928 0.9939 0.9946 0.9956 0.9964 0.9967 0.9971 0.9974 0.9978
0.25 0.9924 0.9937 0.9943 0.9952 0.9954 0.9960 0.9970 0.9971 0.9973 0.9977
$DeclareDMR
4 vs 4 6 vs 6 8 vs 8 10 vs 10 12 vs 12 14 vs 14 16 vs 16 18 vs 18 20 vs 20 22 vs 22 24 vs 24 26 vs 26 28 vs 28 30 vs 30
0.067 216 597 840 966 1057 1107 1144 1170 1188 1201 1210 1218 1224 1229
0.1 322 701 897 1023 1086 1130 1158 1178 1191 1199 1204 1209 1214 1218
0.125 344 729 916 1034 1106 1146 1174 1192 1202 1209 1216 1221 1226 1229
0.167 405 768 949 1054 1112 1152 1178 1194 1206 1215 1221 1227 1231 1234
0.25 457 791 969 1064 1114 1153 1174 1189 1197 1206 1211 1214 1218 1220
32 vs 32 34 vs 34 36 vs 36 38 vs 38 40 vs 40 42 vs 42 44 vs 44 46 vs 46 48 vs 48 50 vs 50
0.067 1232 1235 1237 1239 1240 1242 1242 1243 1244 1244
0.1 1221 1223 1225 1226 1228 1229 1230 1230 1232 1233
0.125 1230 1232 1233 1234 1236 1236 1236 1237 1237 1238
0.167 1236 1237 1239 1240 1241 1242 1242 1243 1243 1244
0.25 1222 1223 1224 1226 1226 1226 1228 1228 1228 1229
$FDR.matrix
4 vs 4 6 vs 6 8 vs 8 10 vs 10 12 vs 12 14 vs 14 16 vs 16 18 vs 18 20 vs 20 22 vs 22 24 vs 24 26 vs 26 28 vs 28 30 vs 30
0.067 0.0474 0.0495 0.0494 0.0497 0.0496 0.0498 0.0496 0.0496 0.0494 0.0497 0.0496 0.0497 0.0498 0.0497
0.1 0.0490 0.0496 0.0495 0.0498 0.0497 0.0498 0.0495 0.0495 0.0497 0.0495 0.0497 0.0498 0.0497 0.0499
0.125 0.0484 0.0496 0.0497 0.0494 0.0496 0.0495 0.0496 0.0496 0.0499 0.0496 0.0498 0.0497 0.0497 0.0496
0.167 0.0491 0.0493 0.0495 0.0495 0.0496 0.0496 0.0498 0.0497 0.0499 0.0495 0.0496 0.0497 0.0497 0.0496
0.25 0.0495 0.0495 0.0494 0.0497 0.0495 0.0498 0.0498 0.0497 0.0496 0.0496 0.0498 0.0500 0.0497 0.0496
32 vs 32 34 vs 34 36 vs 36 38 vs 38 40 vs 40 42 vs 42 44 vs 44 46 vs 46 48 vs 48 50 vs 50
0.067 0.0496 0.0496 0.0498 0.0499 0.0499 0.0498 0.0498 0.0497 0.0497 0.0496
0.1 0.0495 0.0495 0.0493 0.0494 0.0497 0.0498 0.0498 0.0497 0.0498 0.0498
0.125 0.0495 0.0497 0.0497 0.0497 0.0497 0.0497 0.0497 0.0497 0.0497 0.0497
0.167 0.0498 0.0497 0.0496 0.0496 0.0496 0.0497 0.0497 0.0497 0.0496 0.0497
0.25 0.0497 0.0496 0.0496 0.0497 0.0498 0.0498 0.0497 0.0497 0.0497 0.0497
attr(,"class")
[1] "list" "MethylSeqDesign"
The result is a MethylSeqDesign object, where the first element is a matrix of predicted genome wide power. DeclareDMR is a matrix of the number of declared differential methylation regions. FDR is the type I error level estimated by bootstrap procedure, by default we control type I error at 0.05.
- Contour plot
plotContour(predict.result)
- 3D plot
plot3d(predict.result)
- Goal1: the minimun budget to achieve 80% power
design.res1<-designOptim(predict.result, pilot.R=pilot.R, R=R, N=N, targetEDR=0.8)
design.res1$res
$`optim.N`
[1] 10
$optim.R
[1] 0.1
$EDR.target
[1] 0.8
$EDR.achive
[1] 0.8202
$min.budget
[1] 9000
- N and R plot, the admissible points are in black, and inadmissible points are in grey. The optimal N and R combination is in red.
design.res1$plot1
- Cost and EDR plot, the corresponding points for admissible points are in black, and for inadmissible points are in grey. The corresponding point for optimal N and R combination is in red.
design.res1$plot2
- Goal2: we want to know the maximum power under $20000 budget
design.res2<-designOptim(predict.result, pilot.R=pilot.R, R=R, N=N, budget=20000)
design.res2$res
$`optim.N`
[1] 20
$optim.R
[1] 0.125
$budget
[1] 20000
$cost
[1] 19500
$max.EDR
[1] 0.9702
- N and R plot, the admissible points are in black, and inadmissible points are in grey. The optimal N and R combination is in red.
design.res2$plot1
- Cost and EDR plot, the corresponding points for admissible points are in black, and for inadmissible points are in grey. The corresponding point for optimal N and R combination is in red.
design.res2$plot2
