Benchmarks for Prostate Cancer Data Set
José Tamez-Peña
April 22, 2018
1 FRESA.CAD Benchmark
1.1 Prostate Cancer Data Set
ProstateData <- read.delim("./prostate/prostate.txt")
Prostate <- ProstateData[,-1]
rownames(Prostate) <- ProstateData[,1]
ExperimentName <- "Prostate"
bswimsReps <- 20;
theData <- Prostate;
theOutcome <- "Class";
reps <- 75;
fraction <- 0.8;
theData[,1:ncol(theData)] <- sapply(theData,as.numeric)
BSWiMSFileName <- paste(ExperimentName,"FRESAMethod.RDATA",sep = "_")
CVFileName <- paste(ExperimentName,"CVMethod.RDATA",sep = "_")1.2 Benchmarking
BSWiMSMODEL <- BSWiMS.model(formula = paste(theOutcome," ~ 1"),data = theData,NumberofRepeats = bswimsReps)
save(BSWiMSMODEL,file = BSWiMSFileName)
load(file = BSWiMSFileName)
par(mfrow = c(2,2),cex=0.6);
cp <- BinaryBenchmark(theData,theOutcome,reps,fraction)
par(mfrow = c(1,1),cex=1.0);
save(cp,file = CVFileName)
load(file = CVFileName)
1.3 Results
1.3.1 Classifier Results
hm <- heatMaps(Outcome = "Outcome",data = cp$testPredictions,title = "Heat Map",Scale = TRUE,hCluster = "col",cexRow = 0.25,cexCol = 0.75,srtCol = 45) 
#The Times
pander::pander(cp$cpuElapsedTimes)| BSWiMS | RF | RPART | LASSO | SVM | KNN | ENS |
|---|---|---|---|---|---|---|
| 25.88 | 103.4 | 8.251 | 1.043 | 0.0116 | 0.01467 | 130.3 |
learningTime <- -1*cp$cpuElapsedTimes
par(mfrow = c(2,1),cex=1.0);
pr <- plot(cp)
par(mfrow = c(1,1),cex=1.0);1.3.2 Radar Plots
op <- par(no.readonly = TRUE)
library(fmsb)
par(mfrow = c(1,2),xpd = TRUE,pty = "s",mar = c(1,1,1,1))
mNames <- names(cp$cpuElapsedTimes)
classRanks <- c(pr$minMaxMetrics$BER[1],pr$minMaxMetrics$ACC[2],pr$minMaxMetrics$AUC[2],pr$minMaxMetrics$SEN[2],pr$minMaxMetrics$SPE[2],min(cp$cpuElapsedTimes))
classRanks <- rbind(classRanks,c(pr$minMaxMetrics$BER[2],0,0,0,0,max(cp$cpuElapsedTimes)))
classRanks <- as.data.frame(rbind(classRanks,cbind(t(pr$metrics[c("BER","ACC","AUC","SEN","SPE"),mNames]),cp$cpuElapsedTimes)))
colnames(classRanks) <- c("BER","ACC","AUC","SEN","SPE","CPU")
classRanks$BER <- -classRanks$BER
classRanks$CPU <- -classRanks$CPU
colors_border = c( rgb(1.0,0.0,0.0,1.0), rgb(0.0,1.0,0.0,1.0) , rgb(0.0,0.0,1.0,1.0), rgb(0.2,0.2,0.0,1.0), rgb(0.0,1.0,1.0,1.0), rgb(1.0,0.0,1.0,1.0), rgb(0.0,0.0,0.0,1.0) )
colors_in = c( rgb(1.0,0.0,0.0,0.05), rgb(0.0,1.0,0.0,0.05) , rgb(0.0,0.0,1.0,0.05),rgb(1.0,1.0,0.0,0.05), rgb(0.0,1.0,1.0,0.05) , rgb(1.0,0.0,1.0,0.05), rgb(0.0,0.0,0.0,0.05) )
radarchart(classRanks,axistype = 0,maxmin = T,pcol = colors_border,pfcol = colors_in,plwd = c(6,2,2,2,2,2,2),plty = 1, cglcol = "grey", cglty = 1,axislabcol = "black",cglwd = 0.8, vlcex = 0.5 ,title = "Prediction Model")
legend("topleft",legend = rownames(classRanks[-c(1,2),]),bty = "n",pch = 20,col = colors_in,text.col = colors_border,cex = 0.5,pt.cex = 2)
filnames <- c("BSWiMS","LASSO_MIN","RF","IDI","tStudent","kendall","mRMR.classic")
filterRanks <- c(pr$minMaxMetrics$BER[1],pr$minMaxMetrics$ACC[2],pr$minMaxMetrics$AUC[2],pr$minMaxMetrics$SEN[2],pr$minMaxMetrics$SPE[2],max(cp$jaccard),min(cp$featsize));
filterRanks <- rbind(filterRanks,c(pr$minMaxMetrics$BER[2],0,0,0,0,min(cp$jaccard),max(cp$featsize)));
filterRanks <- as.data.frame(rbind(filterRanks,cbind(t(pr$metrics_filter[c("BER","ACC","AUC","SEN","SPE"),filnames]),cp$jaccard[filnames],cp$featsize[filnames])));
colnames(filterRanks) <- c("BER","ACC","AUC","SEN","SPE","Jaccard","SIZE")
filterRanks$BER <- -filterRanks$BER
filterRanks$SIZE <- -filterRanks$SIZE
colors_border = c( rgb(1.0,0.0,0.0,1.0), rgb(0.0,1.0,0.0,1.0) , rgb(0.0,0.0,1.0,1.0), rgb(0.2,0.2,0.0,1.0), rgb(0.0,1.0,1.0,1.0), rgb(1.0,0.0,1.0,1.0), rgb(0.0,0.0,0.0,1.0) )
colors_in = c( rgb(1.0,0.0,0.0,0.05), rgb(0.0,1.0,0.0,0.05) , rgb(0.0,0.0,1.0,0.05),rgb(1.0,1.0,0.0,0.05), rgb(0.0,1.0,1.0,0.05) , rgb(1.0,0.0,1.0,0.05), rgb(0.0,0.0,0.0,0.05) )
radarchart(filterRanks,axistype = 0,maxmin = T,pcol = colors_border,pfcol = colors_in,plwd = c(6,2,2,2,2,2,2),plty = 1, cglcol = "grey", cglty = 1,axislabcol = "black",cglwd = 0.8, vlcex = 0.6,title = "Filter Method" )
legend("topleft",legend = rownames(filterRanks[-c(1,2),]),bty = "n",pch = 20,col = colors_in,text.col = colors_border,cex = 0.5,pt.cex = 2)
detach("package:fmsb", unload=TRUE)
par(mfrow = c(1,1))
par(op)1.3.3 Feature Analysis
rm <- rowMeans(cp$featureSelectionFrequency)
selFrequency <- cp$featureSelectionFrequency[rm > 0.1,]
gplots::heatmap.2(as.matrix(selFrequency),trace = "none",mar = c(10,10),main = "Features",cexRow = 0.25,cexCol = 0.5)
topFeat <- min(ncol(BSWiMSMODEL$bagging$formulaNetwork),30);
gplots::heatmap.2(BSWiMSMODEL$bagging$formulaNetwork[1:topFeat,1:topFeat],trace="none",mar = c(10,10),main = "B:SWiMS Formula Network",cexRow = 0.5,cexCol = 0.5)
pander::pander(summary(BSWiMSMODEL$bagging$bagged.model,caption="Colon",round = 3))coefficients:
Table continues below Estimate lower OR upper u.Accuracy X41468_at 0.001315 1.001 1.001 1.002 0.8529 X40282_s_at -0.001884 0.9978 0.9981 0.9985 0.8333 X37639_at 0.002599 1.002 1.003 1.003 0.8725 X31444_s_at -0.0006037 0.9993 0.9994 0.9995 0.7843 X38634_at -0.005558 0.9935 0.9945 0.9955 0.8333 X37366_at 0.001482 1.001 1.001 1.002 0.8431 X216_at -0.0005807 0.9993 0.9994 0.9995 0.7843 X32243_g_at -0.002086 0.9975 0.9979 0.9983 0.8137 X40436_g_at 0.001512 1.001 1.002 1.002 0.7745 X33121_g_at 0.005232 1.004 1.005 1.006 0.7941 X34840_at 0.005177 1.004 1.005 1.006 0.8039 X769_s_at -0.0004932 0.9994 0.9995 0.9996 0.7745 X41288_at -0.00414 0.995 0.9959 0.9968 0.8137 X36666_at 5.417e-05 1 1 1 0.7941 X37720_at 0.002804 1.002 1.003 1.004 0.8431 X2041_i_at -0.01391 0.9834 0.9862 0.9889 0.7647 X38087_s_at -0.004263 0.9947 0.9957 0.9968 0.7353 X38028_at -0.01251 0.9845 0.9876 0.9906 0.8137 X36491_at 0.002968 1.002 1.003 1.004 0.8039 X33415_at 0.0008701 1.001 1.001 1.001 0.7353 X35277_at -0.002173 0.9972 0.9978 0.9985 0.7843 X39756_g_at 0.0008906 1.001 1.001 1.001 0.7941 X32598_at -0.00332 0.9959 0.9967 0.9975 0.8824 X36587_at 1.772e-05 1 1 1 0.7059 X36601_at -0.0007303 0.9991 0.9993 0.9995 0.8333 X38038_at -0.0005597 0.9993 0.9994 0.9996 0.6863 X38044_at -0.002182 0.9972 0.9978 0.9984 0.8235 X38026_at -0.0007995 0.9989 0.9992 0.9995 0.6667 X556_s_at -0.00184 0.9977 0.9982 0.9987 0.7843 X36814_at -0.001151 0.9985 0.9989 0.9992 0.7451 X34735_at -0.001485 0.998 0.9985 0.999 0.6275 X40856_at -0.001216 0.9985 0.9988 0.9991 0.8431 X39939_at -0.009 0.9884 0.991 0.9937 0.7941 X1980_s_at 0.0002241 1 1 1 0.7353 X32786_at 0.001467 1.001 1.001 1.002 0.7255 X36589_at -0.00338 0.9956 0.9966 0.9976 0.7647 X1767_s_at -0.003643 0.9951 0.9964 0.9976 0.7941 X39315_at -0.003119 0.9959 0.9969 0.9978 0.7745 X38255_at 0.002197 1.001 1.002 1.003 0.6569 X39755_at 5.44e-05 1 1 1 0.7941 X33198_at -0.003008 0.9959 0.997 0.9981 0.7941 X41504_s_at -0.003581 0.9951 0.9964 0.9977 0.7353 X37394_at -0.0004319 0.9994 0.9996 0.9997 0.6667 X41661_at 0.003002 1.002 1.003 1.004 0.6078 X36928_at 0.002553 1.002 1.003 1.003 0.6961 X829_s_at -0.001636 0.9978 0.9984 0.999 0.6863 X39283_at -0.003336 0.9954 0.9967 0.9979 0.5882 X35905_s_at 5.191e-05 1 1 1 0.5392 X40777_at 0.0005139 1 1.001 1.001 0.5392 X575_s_at 0.0005154 1 1.001 1.001 0.8333 X33102_at -0.003344 0.9954 0.9967 0.9979 0.6471 X914_g_at 0.009911 1.006 1.01 1.014 0.7941 X38322_at -0.001244 0.9983 0.9988 0.9992 0.7157 X39545_at -0.007076 0.9902 0.9929 0.9957 0.7843 X33546_at -0.007541 0.9896 0.9925 0.9954 0.5784 X36037_g_at -0.001409 0.9981 0.9986 0.9991 0.6667 X41483_s_at 7.469e-05 1 1 1 0.5882 X37736_at -0.00514 0.9928 0.9949 0.9969 0.6275 X37068_at 0.01658 1.01 1.017 1.023 0.8039 X36569_at -0.001658 0.9976 0.9983 0.999 0.7255 X1612_s_at 9.907e-05 1 1 1 0.5882 X37707_i_at -0.0006382 0.9991 0.9994 0.9996 0.7255 X41741_at 0.000337 1 1 1 0.5196 X863_g_at -0.007505 0.9894 0.9925 0.9957 0.6961 X41385_at -0.00544 0.9923 0.9946 0.9969 0.7451 X36629_at -0.0005432 0.9992 0.9995 0.9997 0.5392 X41106_at 0.0009969 1.001 1.001 1.001 0.6961 X38391_at -0.00105 0.9985 0.999 0.9994 0.598 X36864_at -0.01089 0.9845 0.9892 0.9939 0.7451 X31902_at 0.001353 1.001 1.001 1.002 0.598 X35807_at -0.001751 0.9974 0.9983 0.9991 0.6765 X40248_at -0.003088 0.9955 0.9969 0.9984 0.598 X36638_at 0.000181 1 1 1 0.549 X32225_at 0.003275 1.002 1.003 1.005 0.6471 X37043_at -7.145e-05 0.9999 0.9999 1 0.7255 X38406_f_at -0.0004848 0.9993 0.9995 0.9998 0.8627 X32206_at -0.0006234 0.9991 0.9994 0.9997 0.7843 X37599_at -0.0005044 0.9992 0.9995 0.9998 0.7059 X1846_at 0.001573 1.001 1.002 1.002 0.6373 X34820_at -7.263e-05 0.9999 0.9999 1 0.7157 X38768_at -0.0009424 0.9985 0.9991 0.9997 0.5882 Table continues below r.Accuracy full.Accuracy u.AUC r.AUC full.AUC X41468_at 0.7059 0.9373 0.8538 0.7051 0.9377 X40282_s_at 0.749 0.9412 0.8315 0.7481 0.9412 X37639_at 0.7475 0.9309 0.8727 0.7454 0.931 X31444_s_at 0.7685 0.9178 0.7842 0.7682 0.9177 X38634_at 0.7869 0.9345 0.8315 0.7862 0.9346 X37366_at 0.7508 0.9102 0.845 0.7501 0.9105 X216_at 0.8132 0.9265 0.7827 0.8137 0.9263 X32243_g_at 0.7576 0.9085 0.8131 0.7572 0.9082 X40436_g_at 0.7874 0.9071 0.7746 0.7857 0.9072 X33121_g_at 0.8012 0.915 0.7958 0.7999 0.9153 X34840_at 0.7621 0.9046 0.8038 0.7603 0.9046 X769_s_at 0.7619 0.9006 0.7742 0.7617 0.9007 X41288_at 0.8029 0.9319 0.8131 0.8019 0.9318 X36666_at 0.6863 0.8627 0.7938 0.6808 0.8627 X37720_at 0.8059 0.9412 0.8435 0.8047 0.9415 X2041_i_at 0.8431 0.9267 0.7623 0.8421 0.9267 X38087_s_at 0.8315 0.9203 0.7331 0.8313 0.9201 X38028_at 0.7926 0.9092 0.8112 0.7919 0.909 X36491_at 0.8002 0.908 0.805 0.7999 0.9082 X33415_at 0.8088 0.9281 0.7358 0.8083 0.9285 X35277_at 0.7059 0.8562 0.7819 0.7018 0.8554 X39756_g_at 0.7892 0.8911 0.7946 0.7878 0.8908 X32598_at 0.8008 0.9174 0.8808 0.7995 0.9169 X36587_at 0.5098 0.7059 0.7054 0.5 0.7054 X36601_at 0.7493 0.8676 0.8323 0.7482 0.867 X38038_at 0.8064 0.9338 0.685 0.8074 0.9339 X38044_at 0.8039 0.9006 0.8231 0.8034 0.9003 X38026_at 0.8456 0.9289 0.6646 0.8458 0.929 X556_s_at 0.8211 0.9056 0.7831 0.8204 0.9054 X36814_at 0.7941 0.8725 0.7442 0.7927 0.8719 X34735_at 0.8275 0.9118 0.6242 0.8254 0.9114 X40856_at 0.774 0.8865 0.8423 0.7735 0.8865 X39939_at 0.8151 0.9118 0.7931 0.8147 0.9118 X1980_s_at 0.7819 0.875 0.7354 0.7816 0.8751 X32786_at 0.8422 0.9407 0.7254 0.841 0.9406 X36589_at 0.7951 0.8843 0.7642 0.7939 0.8841 X1767_s_at 0.8158 0.8927 0.7923 0.8164 0.8924 X39315_at 0.8088 0.9069 0.7742 0.8083 0.9067 X38255_at 0.8676 0.9363 0.6542 0.8673 0.9362 X39755_at 0.7941 0.8873 0.7946 0.7954 0.8873 X33198_at 0.8015 0.8983 0.7938 0.8011 0.8981 X41504_s_at 0.8574 0.9348 0.7346 0.8579 0.9351 X37394_at 0.848 0.9216 0.6627 0.8477 0.9212 X41661_at 0.8333 0.9412 0.6038 0.8315 0.9408 X36928_at 0.8556 0.917 0.6969 0.8554 0.9169 X829_s_at 0.8441 0.9328 0.6854 0.8445 0.9331 X39283_at 0.8294 0.9137 0.5862 0.8285 0.9135 X35905_s_at 0.8873 0.9363 0.5388 0.8871 0.936 X40777_at 0.8588 0.9098 0.5396 0.8587 0.9098 X575_s_at 0.8137 0.8922 0.8342 0.8122 0.8922 X33102_at 0.8415 0.8995 0.645 0.8425 0.8997 X914_g_at 0.8491 0.909 0.7965 0.8488 0.9091 X38322_at 0.7964 0.8899 0.7127 0.7957 0.8893 X39545_at 0.835 0.9003 0.7842 0.8347 0.9003 X33546_at 0.8358 0.9056 0.5773 0.8353 0.9055 X36037_g_at 0.8543 0.9188 0.6627 0.8538 0.9186 X41483_s_at 0.8497 0.9183 0.5877 0.8492 0.9183 X37736_at 0.8325 0.9044 0.6265 0.8319 0.9044 X37068_at 0.8595 0.9254 0.8035 0.8587 0.9253 X36569_at 0.8415 0.9052 0.7238 0.8404 0.905 X1612_s_at 0.8647 0.9333 0.5885 0.8645 0.933 X37707_i_at 0.7843 0.866 0.725 0.784 0.8659 X41741_at 0.8667 0.9118 0.5185 0.8665 0.9115 X863_g_at 0.8573 0.915 0.6946 0.8574 0.9147 X41385_at 0.8548 0.9124 0.7438 0.8546 0.9122 X36629_at 0.8676 0.9338 0.5365 0.867 0.9338 X41106_at 0.8399 0.8856 0.6985 0.841 0.8863 X38391_at 0.8725 0.9363 0.5973 0.8723 0.9362 X36864_at 0.8371 0.9036 0.7442 0.8365 0.9037 X31902_at 0.8562 0.915 0.5992 0.8556 0.915 X35807_at 0.8407 0.8873 0.6738 0.8399 0.8868 X40248_at 0.8595 0.9118 0.5977 0.8595 0.9118 X36638_at 0.8569 0.9176 0.5481 0.8569 0.9175 X32225_at 0.8676 0.9412 0.6458 0.8676 0.9414 X37043_at 0.8824 0.9216 0.7235 0.8808 0.9212 X38406_f_at 0.876 0.927 0.8619 0.8754 0.9267 X32206_at 0.8627 0.9118 0.7831 0.8621 0.9117 X37599_at 0.902 0.9314 0.7038 0.901 0.9315 X1846_at 0.8701 0.8922 0.6377 0.8698 0.8917 X34820_at 0.8578 0.8578 0.7135 0.8571 0.8573 X38768_at 0.7582 0.8137 0.5885 0.7547 0.8138 IDI NRI z.IDI z.NRI Frequency X41468_at 0.4805 1.571 9.919 12.89 1 X40282_s_at 0.4327 1.582 9.581 14.93 1 X37639_at 0.4202 1.354 8.93 9.601 1 X31444_s_at 0.4025 1.493 8.622 11.85 0.65 X38634_at 0.3601 1.442 8.211 10.89 0.95 X37366_at 0.3369 1.314 7.941 10.1 0.95 X216_at 0.35 1.415 7.684 11.33 0.9 X32243_g_at 0.3262 1.364 7.669 9.989 0.9 X40436_g_at 0.3599 1.356 7.662 10.66 0.95 X33121_g_at 0.3388 1.36 7.536 10.48 0.9 X34840_at 0.3331 1.359 7.533 9.592 0.75 X769_s_at 0.3485 1.331 7.424 9.592 0.7 X41288_at 0.3228 1.489 7.118 12.22 0.95 X36666_at 0.3091 1.295 7.077 8.833 0.1 X37720_at 0.2932 1.049 6.785 8.892 1 X2041_i_at 0.2813 1.31 6.55 9.944 0.95 X38087_s_at 0.2872 1.486 6.547 13.22 0.8 X38028_at 0.2627 1.309 6.505 9.936 0.95 X36491_at 0.2755 1.289 6.485 9.245 0.65 X33415_at 0.2663 1.424 6.479 10.59 0.3 X35277_at 0.2697 1.169 6.475 7.71 0.15 X39756_g_at 0.2782 1.371 6.446 9.723 0.9 X32598_at 0.2688 1.194 6.41 8.612 0.95 X36587_at 0.2887 0.8215 6.299 4.554 0.1 X36601_at 0.2504 1.143 6.203 7.577 0.7 X38038_at 0.2737 1.26 6.168 8.366 0.2 X38044_at 0.254 1.287 6.086 8.8 0.35 X38026_at 0.2138 1.111 6.028 6.978 0.2 X556_s_at 0.2537 1.28 5.983 8.668 0.8 X36814_at 0.2677 1.471 5.964 11.03 0.1 X34735_at 0.2459 1.291 5.935 9.377 0.25 X40856_at 0.2429 1.253 5.852 8.576 0.95 X39939_at 0.2297 1.217 5.791 7.949 0.7 X1980_s_at 0.2248 1.107 5.79 6.909 0.2 X32786_at 0.2428 1.402 5.774 10.73 1 X36589_at 0.243 1.18 5.711 7.746 0.5 X1767_s_at 0.2248 1.299 5.601 8.867 0.95 X39315_at 0.2125 1.128 5.529 7.176 0.6 X38255_at 0.1938 1.567 5.521 13.24 0.1 X39755_at 0.2207 1.297 5.509 8.906 0.1 X33198_at 0.2172 1.293 5.412 8.703 0.4 X41504_s_at 0.2147 1.383 5.364 10.48 1 X37394_at 0.2136 1.145 5.273 7.918 0.1 X41661_at 0.2196 1.131 5.269 7.045 0.25 X36928_at 0.2001 1.306 5.248 9.772 0.75 X829_s_at 0.2027 1.304 5.248 8.951 1 X39283_at 0.2122 1.27 5.211 8.812 0.25 X35905_s_at 0.2156 1.432 5.211 11.25 0.1 X40777_at 0.1986 1.527 5.162 13.3 0.25 X575_s_at 0.1853 0.9795 5.087 6.097 0.15 X33102_at 0.1889 1.131 5.065 7.076 0.6 X914_g_at 0.185 1.123 5.06 7.511 0.9 X38322_at 0.1977 1.038 5.057 6.68 0.85 X39545_at 0.1786 1.315 5.015 9.071 0.6 X33546_at 0.1954 1.255 5.01 8.862 0.4 X36037_g_at 0.1879 1.399 4.986 10.09 0.35 X41483_s_at 0.1833 1.439 4.934 10.76 0.15 X37736_at 0.1665 1.179 4.867 7.699 0.6 X37068_at 0.1825 1.149 4.863 7.725 0.9 X36569_at 0.153 0.8956 4.628 5.261 0.3 X1612_s_at 0.1606 1.188 4.626 8.121 0.25 X37707_i_at 0.1468 1.128 4.594 7.046 0.15 X41741_at 0.1666 1.242 4.592 8.535 0.25 X863_g_at 0.1792 1.295 4.571 10.25 0.45 X41385_at 0.1381 1.462 4.557 12.1 0.8 X36629_at 0.1802 1.407 4.526 10.86 0.2 X41106_at 0.1552 1.012 4.427 6.05 0.15 X38391_at 0.1553 1.272 4.368 8.348 0.1 X36864_at 0.161 1.016 4.367 6.118 0.9 X31902_at 0.1536 1.212 4.217 7.9 0.15 X35807_at 0.1444 1.004 4.188 6.565 0.4 X40248_at 0.138 1.445 4.107 10.64 0.15 X36638_at 0.1311 1.067 4.089 6.513 0.25 X32225_at 0.1456 1.441 4.071 10.55 0.3 X37043_at 0.1177 1.089 3.863 6.781 0.1 X38406_f_at 0.1272 0.9887 3.835 5.968 1 X32206_at 0.1101 0.6877 3.792 3.875 0.1 X37599_at 0.1078 1.091 3.577 6.717 0.1 X1846_at 0.09008 0.8892 3.419 5.144 0.2 X34820_at 0.07212 1.002 3.066 6.047 0.1 X38768_at 0.07453 0.9826 2.862 6.337 0.15 - Accuracy: 0.9804
- tAUC: 0.9804
- sensitivity: 0.9808
- specificity: 0.98
bootstrap:
hm <- heatMaps(Outcome = theOutcome,data = theData[,c(theOutcome,rownames(selFrequency))],title = "Heat Map",Scale = TRUE,hCluster = "col",cexRow = 0.25,cexCol = 0.5,srtCol = 45)
vlist <- rownames(selFrequency)
vlist <- cbind(vlist,vlist)
univ <- univariateRankVariables(variableList = vlist,formula = paste(theOutcome,"~1"),Outcome = theOutcome,data = theData,type = "LOGIT",rankingTest = "zIDI",uniType = "Binary")[,c("controlMean","controlStd","caseMean","caseStd","ROCAUC","WilcoxRes.p")] 100 : X37573_at 200 : X40248_at
cnames <- colnames(univ);
univ <- cbind(univ,rm[rownames(univ)])
colnames(univ) <- c(cnames,"Frequency")
univ <- univ[order(-univ[,5]),]
pander::pander(univ[1:topFeat,],caption = "Features",round = 4)| controlMean | controlStd | caseMean | caseStd | ROCAUC | |
|---|---|---|---|---|---|
| X37639_at | 48.62 | 28.61 | 177 | 85.02 | 0.9604 |
| X38406_f_at | 750.1 | 403.1 | 292.2 | 125 | 0.9346 |
| X40282_s_at | 222.1 | 165.9 | 47.58 | 29.87 | 0.9252 |
| X37720_at | 149.1 | 36.86 | 251 | 63.55 | 0.9229 |
| X32598_at | 71.82 | 55.15 | 12.98 | 14.3 | 0.9135 |
| X41468_at | 32.55 | 60.89 | 269.6 | 181.5 | 0.9123 |
| X41288_at | 154.8 | 45.52 | 91.08 | 31.46 | 0.9 |
| X32243_g_at | 131.5 | 59.9 | 62.81 | 41.11 | 0.8877 |
| X1767_s_at | 74.93 | 37.34 | 33.67 | 17.3 | 0.8769 |
| X37068_at | 0.41 | 7.08 | 12.94 | 14.2 | 0.8763 |
| X37366_at | 32.66 | 51.84 | 169.2 | 123.5 | 0.876 |
| X40856_at | 196.9 | 83.86 | 101.1 | 51.83 | 0.8727 |
| X39756_g_at | 120 | 84.53 | 272.7 | 106.3 | 0.8719 |
| X36601_at | 262.8 | 163.1 | 131.4 | 67.29 | 0.8712 |
| X39315_at | 72.24 | 34.46 | 35.75 | 19.56 | 0.8631 |
| X33121_g_at | 23.07 | 18.86 | 60.33 | 32.02 | 0.8627 |
| X31444_s_at | 1009 | 308.5 | 614.2 | 267.3 | 0.8625 |
| X769_s_at | 912.3 | 302.2 | 563.4 | 215.4 | 0.8625 |
| X36491_at | 16.98 | 18.46 | 89.37 | 95.84 | 0.8621 |
| X40436_g_at | 119.4 | 78.96 | 257.4 | 103.3 | 0.8617 |
| X34840_at | 19.32 | 14.37 | 48.58 | 23.01 | 0.8581 |
| X36589_at | 69.05 | 94.65 | 36.1 | 14.06 | 0.8563 |
| X33198_at | 71.31 | 23.01 | 48.65 | 12.01 | 0.8562 |
| X36666_at | 310.8 | 172 | 584.1 | 213.9 | 0.8546 |
| X32206_at | 45.82 | 16.8 | 27 | 11.43 | 0.8531 |
| X38028_at | 19.19 | 15.09 | 3.654 | 5.269 | 0.8529 |
| X38044_at | 55.59 | 23.12 | 26.56 | 15.14 | 0.8473 |
| X34775_at | 70.15 | 91.24 | 252.9 | 163.5 | 0.8471 |
| X31538_at | 427.4 | 272.1 | 820.3 | 340.4 | 0.8465 |
| X33137_at | 140.8 | 57.27 | 76.13 | 33.83 | 0.8448 |
| WilcoxRes.p | Frequency | |
|---|---|---|
| X37639_at | 0 | 0.9987 |
| X38406_f_at | 0 | 0.8573 |
| X40282_s_at | 0 | 0.648 |
| X37720_at | 0 | 0.652 |
| X32598_at | 0 | 0.9147 |
| X41468_at | 0 | 0.6227 |
| X41288_at | 0 | 0.792 |
| X32243_g_at | 0 | 0.7307 |
| X1767_s_at | 0 | 0.8267 |
| X37068_at | 0 | 0.7253 |
| X37366_at | 0 | 0.4853 |
| X40856_at | 0 | 0.832 |
| X39756_g_at | 0 | 0.6453 |
| X36601_at | 0 | 0.58 |
| X39315_at | 0 | 0.7707 |
| X33121_g_at | 0 | 0.7573 |
| X31444_s_at | 0 | 0.6373 |
| X769_s_at | 0 | 0.416 |
| X36491_at | 0 | 0.556 |
| X40436_g_at | 0 | 0.7733 |
| X34840_at | 0 | 0.812 |
| X36589_at | 0 | 0.5267 |
| X33198_at | 0 | 0.7387 |
| X36666_at | 0 | 0.488 |
| X32206_at | 0 | 0.5653 |
| X38028_at | 0 | 0.856 |
| X38044_at | 0 | 0.7307 |
| X34775_at | 0 | 0.2747 |
| X31538_at | 0 | 0.2693 |
| X33137_at | 0 | 0.628 |
1.3.4 Feature Analysis
rm <- rowMeans(cp$featureSelectionFrequency)
selFrequency <- cp$featureSelectionFrequency[rm > 0.1,]
gplots::heatmap.2(as.matrix(selFrequency),trace = "none",mar = c(10,10),main = "Features",cexRow = 0.25,cexCol = 0.5)
topFeat <- min(ncol(BSWiMSMODEL$bagging$formulaNetwork),30);
gplots::heatmap.2(BSWiMSMODEL$bagging$formulaNetwork[1:topFeat,1:topFeat],trace="none",mar = c(10,10),main = "B:SWiMS Formula Network",cexRow = 0.5,cexCol = 0.5)
pander::pander(summary(BSWiMSMODEL$bagging$bagged.model,caption="Colon",round = 3))coefficients:
Table continues below Estimate lower OR upper u.Accuracy X41468_at 0.001315 1.001 1.001 1.002 0.8529 X40282_s_at -0.001884 0.9978 0.9981 0.9985 0.8333 X37639_at 0.002599 1.002 1.003 1.003 0.8725 X31444_s_at -0.0006037 0.9993 0.9994 0.9995 0.7843 X38634_at -0.005558 0.9935 0.9945 0.9955 0.8333 X37366_at 0.001482 1.001 1.001 1.002 0.8431 X216_at -0.0005807 0.9993 0.9994 0.9995 0.7843 X32243_g_at -0.002086 0.9975 0.9979 0.9983 0.8137 X40436_g_at 0.001512 1.001 1.002 1.002 0.7745 X33121_g_at 0.005232 1.004 1.005 1.006 0.7941 X34840_at 0.005177 1.004 1.005 1.006 0.8039 X769_s_at -0.0004932 0.9994 0.9995 0.9996 0.7745 X41288_at -0.00414 0.995 0.9959 0.9968 0.8137 X36666_at 5.417e-05 1 1 1 0.7941 X37720_at 0.002804 1.002 1.003 1.004 0.8431 X2041_i_at -0.01391 0.9834 0.9862 0.9889 0.7647 X38087_s_at -0.004263 0.9947 0.9957 0.9968 0.7353 X38028_at -0.01251 0.9845 0.9876 0.9906 0.8137 X36491_at 0.002968 1.002 1.003 1.004 0.8039 X33415_at 0.0008701 1.001 1.001 1.001 0.7353 X35277_at -0.002173 0.9972 0.9978 0.9985 0.7843 X39756_g_at 0.0008906 1.001 1.001 1.001 0.7941 X32598_at -0.00332 0.9959 0.9967 0.9975 0.8824 X36587_at 1.772e-05 1 1 1 0.7059 X36601_at -0.0007303 0.9991 0.9993 0.9995 0.8333 X38038_at -0.0005597 0.9993 0.9994 0.9996 0.6863 X38044_at -0.002182 0.9972 0.9978 0.9984 0.8235 X38026_at -0.0007995 0.9989 0.9992 0.9995 0.6667 X556_s_at -0.00184 0.9977 0.9982 0.9987 0.7843 X36814_at -0.001151 0.9985 0.9989 0.9992 0.7451 X34735_at -0.001485 0.998 0.9985 0.999 0.6275 X40856_at -0.001216 0.9985 0.9988 0.9991 0.8431 X39939_at -0.009 0.9884 0.991 0.9937 0.7941 X1980_s_at 0.0002241 1 1 1 0.7353 X32786_at 0.001467 1.001 1.001 1.002 0.7255 X36589_at -0.00338 0.9956 0.9966 0.9976 0.7647 X1767_s_at -0.003643 0.9951 0.9964 0.9976 0.7941 X39315_at -0.003119 0.9959 0.9969 0.9978 0.7745 X38255_at 0.002197 1.001 1.002 1.003 0.6569 X39755_at 5.44e-05 1 1 1 0.7941 X33198_at -0.003008 0.9959 0.997 0.9981 0.7941 X41504_s_at -0.003581 0.9951 0.9964 0.9977 0.7353 X37394_at -0.0004319 0.9994 0.9996 0.9997 0.6667 X41661_at 0.003002 1.002 1.003 1.004 0.6078 X36928_at 0.002553 1.002 1.003 1.003 0.6961 X829_s_at -0.001636 0.9978 0.9984 0.999 0.6863 X39283_at -0.003336 0.9954 0.9967 0.9979 0.5882 X35905_s_at 5.191e-05 1 1 1 0.5392 X40777_at 0.0005139 1 1.001 1.001 0.5392 X575_s_at 0.0005154 1 1.001 1.001 0.8333 X33102_at -0.003344 0.9954 0.9967 0.9979 0.6471 X914_g_at 0.009911 1.006 1.01 1.014 0.7941 X38322_at -0.001244 0.9983 0.9988 0.9992 0.7157 X39545_at -0.007076 0.9902 0.9929 0.9957 0.7843 X33546_at -0.007541 0.9896 0.9925 0.9954 0.5784 X36037_g_at -0.001409 0.9981 0.9986 0.9991 0.6667 X41483_s_at 7.469e-05 1 1 1 0.5882 X37736_at -0.00514 0.9928 0.9949 0.9969 0.6275 X37068_at 0.01658 1.01 1.017 1.023 0.8039 X36569_at -0.001658 0.9976 0.9983 0.999 0.7255 X1612_s_at 9.907e-05 1 1 1 0.5882 X37707_i_at -0.0006382 0.9991 0.9994 0.9996 0.7255 X41741_at 0.000337 1 1 1 0.5196 X863_g_at -0.007505 0.9894 0.9925 0.9957 0.6961 X41385_at -0.00544 0.9923 0.9946 0.9969 0.7451 X36629_at -0.0005432 0.9992 0.9995 0.9997 0.5392 X41106_at 0.0009969 1.001 1.001 1.001 0.6961 X38391_at -0.00105 0.9985 0.999 0.9994 0.598 X36864_at -0.01089 0.9845 0.9892 0.9939 0.7451 X31902_at 0.001353 1.001 1.001 1.002 0.598 X35807_at -0.001751 0.9974 0.9983 0.9991 0.6765 X40248_at -0.003088 0.9955 0.9969 0.9984 0.598 X36638_at 0.000181 1 1 1 0.549 X32225_at 0.003275 1.002 1.003 1.005 0.6471 X37043_at -7.145e-05 0.9999 0.9999 1 0.7255 X38406_f_at -0.0004848 0.9993 0.9995 0.9998 0.8627 X32206_at -0.0006234 0.9991 0.9994 0.9997 0.7843 X37599_at -0.0005044 0.9992 0.9995 0.9998 0.7059 X1846_at 0.001573 1.001 1.002 1.002 0.6373 X34820_at -7.263e-05 0.9999 0.9999 1 0.7157 X38768_at -0.0009424 0.9985 0.9991 0.9997 0.5882 Table continues below r.Accuracy full.Accuracy u.AUC r.AUC full.AUC X41468_at 0.7059 0.9373 0.8538 0.7051 0.9377 X40282_s_at 0.749 0.9412 0.8315 0.7481 0.9412 X37639_at 0.7475 0.9309 0.8727 0.7454 0.931 X31444_s_at 0.7685 0.9178 0.7842 0.7682 0.9177 X38634_at 0.7869 0.9345 0.8315 0.7862 0.9346 X37366_at 0.7508 0.9102 0.845 0.7501 0.9105 X216_at 0.8132 0.9265 0.7827 0.8137 0.9263 X32243_g_at 0.7576 0.9085 0.8131 0.7572 0.9082 X40436_g_at 0.7874 0.9071 0.7746 0.7857 0.9072 X33121_g_at 0.8012 0.915 0.7958 0.7999 0.9153 X34840_at 0.7621 0.9046 0.8038 0.7603 0.9046 X769_s_at 0.7619 0.9006 0.7742 0.7617 0.9007 X41288_at 0.8029 0.9319 0.8131 0.8019 0.9318 X36666_at 0.6863 0.8627 0.7938 0.6808 0.8627 X37720_at 0.8059 0.9412 0.8435 0.8047 0.9415 X2041_i_at 0.8431 0.9267 0.7623 0.8421 0.9267 X38087_s_at 0.8315 0.9203 0.7331 0.8313 0.9201 X38028_at 0.7926 0.9092 0.8112 0.7919 0.909 X36491_at 0.8002 0.908 0.805 0.7999 0.9082 X33415_at 0.8088 0.9281 0.7358 0.8083 0.9285 X35277_at 0.7059 0.8562 0.7819 0.7018 0.8554 X39756_g_at 0.7892 0.8911 0.7946 0.7878 0.8908 X32598_at 0.8008 0.9174 0.8808 0.7995 0.9169 X36587_at 0.5098 0.7059 0.7054 0.5 0.7054 X36601_at 0.7493 0.8676 0.8323 0.7482 0.867 X38038_at 0.8064 0.9338 0.685 0.8074 0.9339 X38044_at 0.8039 0.9006 0.8231 0.8034 0.9003 X38026_at 0.8456 0.9289 0.6646 0.8458 0.929 X556_s_at 0.8211 0.9056 0.7831 0.8204 0.9054 X36814_at 0.7941 0.8725 0.7442 0.7927 0.8719 X34735_at 0.8275 0.9118 0.6242 0.8254 0.9114 X40856_at 0.774 0.8865 0.8423 0.7735 0.8865 X39939_at 0.8151 0.9118 0.7931 0.8147 0.9118 X1980_s_at 0.7819 0.875 0.7354 0.7816 0.8751 X32786_at 0.8422 0.9407 0.7254 0.841 0.9406 X36589_at 0.7951 0.8843 0.7642 0.7939 0.8841 X1767_s_at 0.8158 0.8927 0.7923 0.8164 0.8924 X39315_at 0.8088 0.9069 0.7742 0.8083 0.9067 X38255_at 0.8676 0.9363 0.6542 0.8673 0.9362 X39755_at 0.7941 0.8873 0.7946 0.7954 0.8873 X33198_at 0.8015 0.8983 0.7938 0.8011 0.8981 X41504_s_at 0.8574 0.9348 0.7346 0.8579 0.9351 X37394_at 0.848 0.9216 0.6627 0.8477 0.9212 X41661_at 0.8333 0.9412 0.6038 0.8315 0.9408 X36928_at 0.8556 0.917 0.6969 0.8554 0.9169 X829_s_at 0.8441 0.9328 0.6854 0.8445 0.9331 X39283_at 0.8294 0.9137 0.5862 0.8285 0.9135 X35905_s_at 0.8873 0.9363 0.5388 0.8871 0.936 X40777_at 0.8588 0.9098 0.5396 0.8587 0.9098 X575_s_at 0.8137 0.8922 0.8342 0.8122 0.8922 X33102_at 0.8415 0.8995 0.645 0.8425 0.8997 X914_g_at 0.8491 0.909 0.7965 0.8488 0.9091 X38322_at 0.7964 0.8899 0.7127 0.7957 0.8893 X39545_at 0.835 0.9003 0.7842 0.8347 0.9003 X33546_at 0.8358 0.9056 0.5773 0.8353 0.9055 X36037_g_at 0.8543 0.9188 0.6627 0.8538 0.9186 X41483_s_at 0.8497 0.9183 0.5877 0.8492 0.9183 X37736_at 0.8325 0.9044 0.6265 0.8319 0.9044 X37068_at 0.8595 0.9254 0.8035 0.8587 0.9253 X36569_at 0.8415 0.9052 0.7238 0.8404 0.905 X1612_s_at 0.8647 0.9333 0.5885 0.8645 0.933 X37707_i_at 0.7843 0.866 0.725 0.784 0.8659 X41741_at 0.8667 0.9118 0.5185 0.8665 0.9115 X863_g_at 0.8573 0.915 0.6946 0.8574 0.9147 X41385_at 0.8548 0.9124 0.7438 0.8546 0.9122 X36629_at 0.8676 0.9338 0.5365 0.867 0.9338 X41106_at 0.8399 0.8856 0.6985 0.841 0.8863 X38391_at 0.8725 0.9363 0.5973 0.8723 0.9362 X36864_at 0.8371 0.9036 0.7442 0.8365 0.9037 X31902_at 0.8562 0.915 0.5992 0.8556 0.915 X35807_at 0.8407 0.8873 0.6738 0.8399 0.8868 X40248_at 0.8595 0.9118 0.5977 0.8595 0.9118 X36638_at 0.8569 0.9176 0.5481 0.8569 0.9175 X32225_at 0.8676 0.9412 0.6458 0.8676 0.9414 X37043_at 0.8824 0.9216 0.7235 0.8808 0.9212 X38406_f_at 0.876 0.927 0.8619 0.8754 0.9267 X32206_at 0.8627 0.9118 0.7831 0.8621 0.9117 X37599_at 0.902 0.9314 0.7038 0.901 0.9315 X1846_at 0.8701 0.8922 0.6377 0.8698 0.8917 X34820_at 0.8578 0.8578 0.7135 0.8571 0.8573 X38768_at 0.7582 0.8137 0.5885 0.7547 0.8138 IDI NRI z.IDI z.NRI Frequency X41468_at 0.4805 1.571 9.919 12.89 1 X40282_s_at 0.4327 1.582 9.581 14.93 1 X37639_at 0.4202 1.354 8.93 9.601 1 X31444_s_at 0.4025 1.493 8.622 11.85 0.65 X38634_at 0.3601 1.442 8.211 10.89 0.95 X37366_at 0.3369 1.314 7.941 10.1 0.95 X216_at 0.35 1.415 7.684 11.33 0.9 X32243_g_at 0.3262 1.364 7.669 9.989 0.9 X40436_g_at 0.3599 1.356 7.662 10.66 0.95 X33121_g_at 0.3388 1.36 7.536 10.48 0.9 X34840_at 0.3331 1.359 7.533 9.592 0.75 X769_s_at 0.3485 1.331 7.424 9.592 0.7 X41288_at 0.3228 1.489 7.118 12.22 0.95 X36666_at 0.3091 1.295 7.077 8.833 0.1 X37720_at 0.2932 1.049 6.785 8.892 1 X2041_i_at 0.2813 1.31 6.55 9.944 0.95 X38087_s_at 0.2872 1.486 6.547 13.22 0.8 X38028_at 0.2627 1.309 6.505 9.936 0.95 X36491_at 0.2755 1.289 6.485 9.245 0.65 X33415_at 0.2663 1.424 6.479 10.59 0.3 X35277_at 0.2697 1.169 6.475 7.71 0.15 X39756_g_at 0.2782 1.371 6.446 9.723 0.9 X32598_at 0.2688 1.194 6.41 8.612 0.95 X36587_at 0.2887 0.8215 6.299 4.554 0.1 X36601_at 0.2504 1.143 6.203 7.577 0.7 X38038_at 0.2737 1.26 6.168 8.366 0.2 X38044_at 0.254 1.287 6.086 8.8 0.35 X38026_at 0.2138 1.111 6.028 6.978 0.2 X556_s_at 0.2537 1.28 5.983 8.668 0.8 X36814_at 0.2677 1.471 5.964 11.03 0.1 X34735_at 0.2459 1.291 5.935 9.377 0.25 X40856_at 0.2429 1.253 5.852 8.576 0.95 X39939_at 0.2297 1.217 5.791 7.949 0.7 X1980_s_at 0.2248 1.107 5.79 6.909 0.2 X32786_at 0.2428 1.402 5.774 10.73 1 X36589_at 0.243 1.18 5.711 7.746 0.5 X1767_s_at 0.2248 1.299 5.601 8.867 0.95 X39315_at 0.2125 1.128 5.529 7.176 0.6 X38255_at 0.1938 1.567 5.521 13.24 0.1 X39755_at 0.2207 1.297 5.509 8.906 0.1 X33198_at 0.2172 1.293 5.412 8.703 0.4 X41504_s_at 0.2147 1.383 5.364 10.48 1 X37394_at 0.2136 1.145 5.273 7.918 0.1 X41661_at 0.2196 1.131 5.269 7.045 0.25 X36928_at 0.2001 1.306 5.248 9.772 0.75 X829_s_at 0.2027 1.304 5.248 8.951 1 X39283_at 0.2122 1.27 5.211 8.812 0.25 X35905_s_at 0.2156 1.432 5.211 11.25 0.1 X40777_at 0.1986 1.527 5.162 13.3 0.25 X575_s_at 0.1853 0.9795 5.087 6.097 0.15 X33102_at 0.1889 1.131 5.065 7.076 0.6 X914_g_at 0.185 1.123 5.06 7.511 0.9 X38322_at 0.1977 1.038 5.057 6.68 0.85 X39545_at 0.1786 1.315 5.015 9.071 0.6 X33546_at 0.1954 1.255 5.01 8.862 0.4 X36037_g_at 0.1879 1.399 4.986 10.09 0.35 X41483_s_at 0.1833 1.439 4.934 10.76 0.15 X37736_at 0.1665 1.179 4.867 7.699 0.6 X37068_at 0.1825 1.149 4.863 7.725 0.9 X36569_at 0.153 0.8956 4.628 5.261 0.3 X1612_s_at 0.1606 1.188 4.626 8.121 0.25 X37707_i_at 0.1468 1.128 4.594 7.046 0.15 X41741_at 0.1666 1.242 4.592 8.535 0.25 X863_g_at 0.1792 1.295 4.571 10.25 0.45 X41385_at 0.1381 1.462 4.557 12.1 0.8 X36629_at 0.1802 1.407 4.526 10.86 0.2 X41106_at 0.1552 1.012 4.427 6.05 0.15 X38391_at 0.1553 1.272 4.368 8.348 0.1 X36864_at 0.161 1.016 4.367 6.118 0.9 X31902_at 0.1536 1.212 4.217 7.9 0.15 X35807_at 0.1444 1.004 4.188 6.565 0.4 X40248_at 0.138 1.445 4.107 10.64 0.15 X36638_at 0.1311 1.067 4.089 6.513 0.25 X32225_at 0.1456 1.441 4.071 10.55 0.3 X37043_at 0.1177 1.089 3.863 6.781 0.1 X38406_f_at 0.1272 0.9887 3.835 5.968 1 X32206_at 0.1101 0.6877 3.792 3.875 0.1 X37599_at 0.1078 1.091 3.577 6.717 0.1 X1846_at 0.09008 0.8892 3.419 5.144 0.2 X34820_at 0.07212 1.002 3.066 6.047 0.1 X38768_at 0.07453 0.9826 2.862 6.337 0.15 - Accuracy: 0.9804
- tAUC: 0.9804
- sensitivity: 0.9808
- specificity: 0.98
bootstrap:
hm <- heatMaps(Outcome = theOutcome,data = theData[,c(theOutcome,rownames(selFrequency))],title = "Heat Map",Scale = TRUE,hCluster = "col",cexRow = 0.25,cexCol = 0.5,srtCol = 45)
vlist <- rownames(selFrequency)
vlist <- cbind(vlist,vlist)
univ <- univariateRankVariables(variableList = vlist,formula = paste(theOutcome,"~1"),Outcome = theOutcome,data = theData,type = "LOGIT",rankingTest = "zIDI",uniType = "Binary")[,c("controlMean","controlStd","caseMean","caseStd","ROCAUC","WilcoxRes.p")] 100 : X37573_at 200 : X40248_at
cnames <- colnames(univ);
univ <- cbind(univ,rm[rownames(univ)])
colnames(univ) <- c(cnames,"Frequency")
univ <- univ[order(-univ[,5]),]
pander::pander(univ[1:topFeat,],caption = "Features",round = 4)| controlMean | controlStd | caseMean | caseStd | ROCAUC | |
|---|---|---|---|---|---|
| X37639_at | 48.62 | 28.61 | 177 | 85.02 | 0.9604 |
| X38406_f_at | 750.1 | 403.1 | 292.2 | 125 | 0.9346 |
| X40282_s_at | 222.1 | 165.9 | 47.58 | 29.87 | 0.9252 |
| X37720_at | 149.1 | 36.86 | 251 | 63.55 | 0.9229 |
| X32598_at | 71.82 | 55.15 | 12.98 | 14.3 | 0.9135 |
| X41468_at | 32.55 | 60.89 | 269.6 | 181.5 | 0.9123 |
| X41288_at | 154.8 | 45.52 | 91.08 | 31.46 | 0.9 |
| X32243_g_at | 131.5 | 59.9 | 62.81 | 41.11 | 0.8877 |
| X1767_s_at | 74.93 | 37.34 | 33.67 | 17.3 | 0.8769 |
| X37068_at | 0.41 | 7.08 | 12.94 | 14.2 | 0.8763 |
| X37366_at | 32.66 | 51.84 | 169.2 | 123.5 | 0.876 |
| X40856_at | 196.9 | 83.86 | 101.1 | 51.83 | 0.8727 |
| X39756_g_at | 120 | 84.53 | 272.7 | 106.3 | 0.8719 |
| X36601_at | 262.8 | 163.1 | 131.4 | 67.29 | 0.8712 |
| X39315_at | 72.24 | 34.46 | 35.75 | 19.56 | 0.8631 |
| X33121_g_at | 23.07 | 18.86 | 60.33 | 32.02 | 0.8627 |
| X31444_s_at | 1009 | 308.5 | 614.2 | 267.3 | 0.8625 |
| X769_s_at | 912.3 | 302.2 | 563.4 | 215.4 | 0.8625 |
| X36491_at | 16.98 | 18.46 | 89.37 | 95.84 | 0.8621 |
| X40436_g_at | 119.4 | 78.96 | 257.4 | 103.3 | 0.8617 |
| X34840_at | 19.32 | 14.37 | 48.58 | 23.01 | 0.8581 |
| X36589_at | 69.05 | 94.65 | 36.1 | 14.06 | 0.8563 |
| X33198_at | 71.31 | 23.01 | 48.65 | 12.01 | 0.8562 |
| X36666_at | 310.8 | 172 | 584.1 | 213.9 | 0.8546 |
| X32206_at | 45.82 | 16.8 | 27 | 11.43 | 0.8531 |
| X38028_at | 19.19 | 15.09 | 3.654 | 5.269 | 0.8529 |
| X38044_at | 55.59 | 23.12 | 26.56 | 15.14 | 0.8473 |
| X34775_at | 70.15 | 91.24 | 252.9 | 163.5 | 0.8471 |
| X31538_at | 427.4 | 272.1 | 820.3 | 340.4 | 0.8465 |
| X33137_at | 140.8 | 57.27 | 76.13 | 33.83 | 0.8448 |
| WilcoxRes.p | Frequency | |
|---|---|---|
| X37639_at | 0 | 0.9987 |
| X38406_f_at | 0 | 0.8573 |
| X40282_s_at | 0 | 0.648 |
| X37720_at | 0 | 0.652 |
| X32598_at | 0 | 0.9147 |
| X41468_at | 0 | 0.6227 |
| X41288_at | 0 | 0.792 |
| X32243_g_at | 0 | 0.7307 |
| X1767_s_at | 0 | 0.8267 |
| X37068_at | 0 | 0.7253 |
| X37366_at | 0 | 0.4853 |
| X40856_at | 0 | 0.832 |
| X39756_g_at | 0 | 0.6453 |
| X36601_at | 0 | 0.58 |
| X39315_at | 0 | 0.7707 |
| X33121_g_at | 0 | 0.7573 |
| X31444_s_at | 0 | 0.6373 |
| X769_s_at | 0 | 0.416 |
| X36491_at | 0 | 0.556 |
| X40436_g_at | 0 | 0.7733 |
| X34840_at | 0 | 0.812 |
| X36589_at | 0 | 0.5267 |
| X33198_at | 0 | 0.7387 |
| X36666_at | 0 | 0.488 |
| X32206_at | 0 | 0.5653 |
| X38028_at | 0 | 0.856 |
| X38044_at | 0 | 0.7307 |
| X34775_at | 0 | 0.2747 |
| X31538_at | 0 | 0.2693 |
| X33137_at | 0 | 0.628 |