Benchmarks for Prostate Cancer Data Set

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
44.46	171.4	10.67	1.351	0.01587	0.02427	227.9

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","RF.ref","IDI","t-test","Kendall","mRMR")

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(selFrequency,trace = "none",mar = c(10,10),main = "Features",cexRow = 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")

pander::pander(summary(BSWiMSMODEL$bagging$bagged.model,caption="Colon",round = 3))

• coefficients:

  Table continues below

  	Estimate	lower	OR	upper	u.Accuracy
  X40282_s_at	-0.0033	0.9961	0.9967	0.9973	0.8333
  X37639_at	0.00336	1.003	1.003	1.004	0.8725
  X41468_at	0.001861	1.001	1.002	1.002	0.8529
  X40436_g_at	0.002672	1.002	1.003	1.003	0.7745
  X32243_g_at	-0.00269	0.9967	0.9973	0.9979	0.8137
  X37366_at	0.001827	1.001	1.002	1.002	0.8431
  X38634_at	-0.009618	0.9881	0.9904	0.9927	0.8333
  X31444_s_at	-0.0004967	0.9994	0.9995	0.9996	0.7843
  X2041_i_at	-0.02004	0.975	0.9802	0.9853	0.7647
  X33121_g_at	0.006488	1.005	1.007	1.008	0.7941
  X41288_at	-0.00531	0.9933	0.9947	0.9961	0.8137
  X34840_at	0.005391	1.004	1.005	1.007	0.8039
  X36491_at	0.003699	1.003	1.004	1.005	0.8039
  X216_at	-0.0006353	0.9992	0.9994	0.9995	0.7843
  X39939_at	-0.009762	0.9875	0.9903	0.9931	0.7941
  X32598_at	-0.002882	0.9963	0.9971	0.998	0.8824
  X38057_at	-0.0008888	0.9988	0.9991	0.9994	0.7647
  X38087_s_at	-0.006285	0.9918	0.9937	0.9957	0.7353
  X39756_g_at	0.0008522	1.001	1.001	1.001	0.7941
  X37720_at	0.002832	1.002	1.003	1.004	0.8431
  X33198_at	-0.001194	0.9984	0.9988	0.9992	0.7941
  X769_s_at	-0.0003782	0.9995	0.9996	0.9997	0.7745
  X38026_at	-0.001195	0.9984	0.9988	0.9992	0.6667
  X36814_at	-0.002217	0.9971	0.9978	0.9985	0.7451
  X575_s_at	0.0004621	1	1	1.001	0.8333
  X38028_at	-0.01366	0.9819	0.9864	0.991	0.8137
  X32786_at	0.00211	1.001	1.002	1.003	0.7255
  X36601_at	-0.0002904	0.9996	0.9997	0.9998	0.8333
  X556_s_at	-0.001872	0.9975	0.9981	0.9988	0.7843
  X1980_s_at	0.000532	1	1.001	1.001	0.7353
  X38038_at	-0.0007719	0.999	0.9992	0.9995	0.6863
  X38255_at	0.003448	1.002	1.003	1.005	0.6569
  X40856_at	-0.0005574	0.9992	0.9994	0.9996	0.8431
  X37068_at	0.0134	1.009	1.013	1.018	0.8039
  X36589_at	-0.002641	0.9964	0.9974	0.9983	0.7647
  X41661_at	0.00364	1.002	1.004	1.005	0.6078
  X41504_s_at	-0.00484	0.9934	0.9952	0.997	0.7353
  X33546_at	-0.008432	0.9885	0.9916	0.9947	0.5784
  X1767_s_at	-0.003082	0.9958	0.9969	0.9981	0.7941
  X829_s_at	-0.001939	0.9973	0.9981	0.9988	0.6863
  X1612_s_at	0.0001855	1	1	1	0.5882
  X36928_at	0.003256	1.002	1.003	1.005	0.6961
  X33415_at	0.0005804	1	1.001	1.001	0.7353
  X37736_at	-0.003497	0.9952	0.9965	0.9979	0.6275
  X35807_at	-0.002879	0.996	0.9971	0.9982	0.6765
  X914_g_at	0.007999	1.005	1.008	1.011	0.7941
  X38044_at	-0.0009023	0.9987	0.9991	0.9995	0.8235
  X39545_at	-0.003826	0.9947	0.9962	0.9977	0.7843
  X36569_at	-0.0003228	0.9995	0.9997	0.9998	0.7255
  X33102_at	-0.002996	0.9958	0.997	0.9982	0.6471
  X39315_at	-0.002527	0.9964	0.9975	0.9985	0.7745
  X863_g_at	-0.008072	0.9886	0.992	0.9953	0.6961
  X41483_s_at	0.00012	1	1	1	0.5882
  X36638_at	0.0003602	1	1	1.001	0.549
  X36864_at	-0.01062	0.9849	0.9894	0.994	0.7451
  X41106_at	0.0009226	1.001	1.001	1.001	0.6961
  X40248_at	-0.01001	0.9856	0.99	0.9945	0.598
  X41385_at	-0.006293	0.9909	0.9937	0.9966	0.7451
  X32225_at	0.003609	1.002	1.004	1.005	0.6471
  X32747_at	-0.0001819	0.9997	0.9998	0.9999	0.6765
  X41741_at	0.0003762	1	1	1.001	0.5196
  X33741_at	-0.001983	0.9971	0.998	0.999	0.6373
  X38322_at	-0.0008333	0.9988	0.9992	0.9996	0.7157
  X38406_f_at	-0.000243	0.9996	0.9998	0.9999	0.8627
  X1846_at	0.003726	1.002	1.004	1.006	0.6373
  X37043_at	-8.14e-05	0.9999	0.9999	1	0.7255
  X1052_s_at	0.0001609	1	1	1	0.6569
  X34820_at	-7.622e-05	0.9999	0.9999	1	0.7157
  X40243_at	0.0003885	1	1	1.001	0.5392
  X35905_s_at	2.711e-05	1	1	1	0.5392
  X37599_at	-0.0005311	0.9992	0.9995	0.9998	0.7059
  X38391_at	-0.0007015	0.9988	0.9993	0.9998	0.598

  Table continues below

  	r.Accuracy	full.Accuracy	u.AUC	r.AUC	full.AUC
  X40282_s_at	0.7275	0.9407	0.8315	0.7264	0.9406
  X37639_at	0.6897	0.9294	0.8727	0.6876	0.9296
  X41468_at	0.7034	0.9377	0.8538	0.7026	0.9382
  X40436_g_at	0.8	0.9196	0.7746	0.7983	0.9198
  X32243_g_at	0.7394	0.9174	0.8131	0.7391	0.9174
  X37366_at	0.7384	0.9107	0.845	0.7377	0.9112
  X38634_at	0.79	0.9345	0.8315	0.7893	0.9345
  X31444_s_at	0.7706	0.9304	0.7842	0.7704	0.9304
  X2041_i_at	0.8314	0.9294	0.7623	0.8301	0.9291
  X33121_g_at	0.8008	0.9154	0.7958	0.7995	0.9155
  X41288_at	0.7868	0.9245	0.8131	0.7856	0.9243
  X34840_at	0.7868	0.9208	0.8038	0.786	0.9207
  X36491_at	0.7804	0.9127	0.805	0.7802	0.9129
  X216_at	0.8126	0.921	0.7827	0.8129	0.921
  X39939_at	0.7825	0.913	0.7931	0.7816	0.9128
  X32598_at	0.799	0.9191	0.8808	0.7978	0.9187
  X38057_at	0.6863	0.8627	0.7635	0.6831	0.8627
  X38087_s_at	0.8364	0.9288	0.7331	0.8363	0.9286
  X39756_g_at	0.8028	0.9066	0.7946	0.8015	0.9065
  X37720_at	0.8279	0.9319	0.8435	0.827	0.932
  X33198_at	0.7843	0.8824	0.7938	0.7838	0.8821
  X769_s_at	0.8155	0.9127	0.7742	0.8148	0.9125
  X38026_at	0.8456	0.9289	0.6646	0.8458	0.929
  X36814_at	0.8137	0.9216	0.7442	0.8123	0.9212
  X575_s_at	0.7876	0.8856	0.8342	0.7868	0.8859
  X38028_at	0.8077	0.9101	0.8112	0.8073	0.9099
  X32786_at	0.8446	0.9441	0.7254	0.8433	0.9441
  X36601_at	0.7569	0.8814	0.8323	0.7553	0.881
  X556_s_at	0.8394	0.9216	0.7831	0.8388	0.9215
  X1980_s_at	0.8219	0.9248	0.7354	0.8219	0.9248
  X38038_at	0.7941	0.9078	0.685	0.795	0.9078
  X38255_at	0.8676	0.9363	0.6542	0.8673	0.9362
  X40856_at	0.7864	0.8908	0.8423	0.7866	0.8909
  X37068_at	0.8529	0.9271	0.8035	0.8517	0.9269
  X36589_at	0.7892	0.8734	0.7642	0.7877	0.8732
  X41661_at	0.8333	0.9412	0.6038	0.8315	0.9408
  X41504_s_at	0.8711	0.9377	0.7346	0.8718	0.9381
  X33546_at	0.8611	0.933	0.5773	0.8605	0.9329
  X1767_s_at	0.834	0.9052	0.7923	0.8344	0.9051
  X829_s_at	0.8478	0.9345	0.6854	0.8482	0.9347
  X1612_s_at	0.8388	0.9074	0.5885	0.8382	0.9073
  X36928_at	0.8595	0.9281	0.6969	0.8595	0.928
  X33415_at	0.8431	0.924	0.7358	0.8427	0.9242
  X37736_at	0.866	0.9118	0.6265	0.8664	0.9118
  X35807_at	0.8301	0.9063	0.6738	0.8294	0.906
  X914_g_at	0.8538	0.9199	0.7965	0.8534	0.92
  X38044_at	0.848	0.902	0.8231	0.8476	0.9017
  X39545_at	0.8147	0.8922	0.7842	0.8137	0.8916
  X36569_at	0.7974	0.8856	0.7238	0.7959	0.885
  X33102_at	0.8453	0.8998	0.645	0.8464	0.9001
  X39315_at	0.8556	0.9225	0.7742	0.8555	0.9223
  X863_g_at	0.837	0.9081	0.6946	0.836	0.9079
  X41483_s_at	0.8611	0.9199	0.5877	0.8608	0.9199
  X36638_at	0.8667	0.9333	0.5481	0.8667	0.9332
  X36864_at	0.8474	0.9136	0.7442	0.8475	0.9136
  X41106_at	0.7917	0.8407	0.6985	0.7925	0.8416
  X40248_at	0.8655	0.9104	0.5977	0.8659	0.9104
  X41385_at	0.8668	0.9198	0.7438	0.8666	0.9196
  X32225_at	0.8505	0.9338	0.6458	0.8506	0.934
  X32747_at	0.8627	0.9265	0.6762	0.8635	0.9269
  X41741_at	0.8725	0.9294	0.5185	0.8727	0.9293
  X33741_at	0.8775	0.9069	0.6373	0.8769	0.9067
  X38322_at	0.8209	0.8939	0.7127	0.8197	0.8935
  X38406_f_at	0.8706	0.9284	0.8619	0.8703	0.9284
  X1846_at	0.8627	0.9069	0.6377	0.8624	0.9065
  X37043_at	0.8824	0.9216	0.7235	0.8808	0.9212
  X1052_s_at	0.866	0.9118	0.6573	0.866	0.9123
  X34820_at	0.848	0.8922	0.7135	0.8475	0.8921
  X40243_at	0.8725	0.951	0.5346	0.8727	0.9508
  X35905_s_at	0.8725	0.9314	0.5388	0.8727	0.9319
  X37599_at	0.902	0.9314	0.7038	0.901	0.9315
  X38391_at	0.8088	0.8137	0.5973	0.8098	0.8144

  	IDI	NRI	z.IDI	z.NRI	Frequency
  X40282_s_at	0.5345	1.64	11.44	16.21	1
  X37639_at	0.5108	1.438	11.38	11.82	1
  X41468_at	0.4839	1.571	9.98	12.89	1
  X40436_g_at	0.4059	1.535	8.708	12.9	1
  X32243_g_at	0.3652	1.414	8.517	11.1	0.95
  X37366_at	0.364	1.432	8.331	11.82	0.95
  X38634_at	0.3566	1.436	8.185	10.83	0.95
  X31444_s_at	0.3556	1.414	7.625	10.43	0.5
  X2041_i_at	0.3313	1.39	7.476	10.97	0.75
  X33121_g_at	0.3312	1.322	7.392	9.854	0.95
  X41288_at	0.3365	1.462	7.343	11.65	1
  X34840_at	0.31	1.306	7.267	9.216	0.6
  X36491_at	0.323	1.467	7.242	11.39	0.5
  X216_at	0.2974	1.331	6.832	9.521	0.85
  X39939_at	0.2882	1.274	6.735	8.564	0.8
  X32598_at	0.2833	1.197	6.689	8.769	1
  X38057_at	0.2565	1.288	6.344	9.177	0.15
  X38087_s_at	0.2702	1.438	6.32	11.91	0.95
  X39756_g_at	0.2683	1.334	6.317	9.38	0.85
  X37720_at	0.2625	1.063	6.212	8.616	1
  X33198_at	0.2519	1.334	6.099	9.295	0.2
  X769_s_at	0.2689	1.192	6.057	8.124	0.55
  X38026_at	0.2138	1.111	6.028	6.978	0.2
  X36814_at	0.261	1.647	5.946	15.45	0.1
  X575_s_at	0.2418	1.126	5.894	7.345	0.15
  X38028_at	0.2264	1.372	5.802	10.37	0.9
  X32786_at	0.2391	1.404	5.74	10.49	1
  X36601_at	0.2141	1.106	5.678	7.097	0.5
  X556_s_at	0.231	1.321	5.586	9.222	0.65
  X1980_s_at	0.2048	1.151	5.574	7.6	0.3
  X38038_at	0.2372	1.141	5.527	7.4	0.25
  X38255_at	0.1938	1.567	5.521	13.24	0.1
  X40856_at	0.2156	1.184	5.402	7.922	0.7
  X37068_at	0.205	1.207	5.376	8.551	0.8
  X36589_at	0.2176	1.003	5.286	6.182	0.6
  X41661_at	0.2196	1.131	5.269	7.045	0.2
  X41504_s_at	0.2073	1.43	5.262	10.97	1
  X33546_at	0.2075	1.479	5.254	11.91	0.3
  X1767_s_at	0.2045	1.237	5.227	8.307	0.75
  X829_s_at	0.1996	1.302	5.183	8.908	0.95
  X1612_s_at	0.1829	1.346	5.146	9.611	0.45
  X36928_at	0.1925	1.301	5.143	9.323	0.75
  X33415_at	0.1952	1.234	5.084	8.254	0.2
  X37736_at	0.1687	1.25	5.055	8.315	0.3
  X35807_at	0.2006	1.253	5.045	8.453	0.45
  X914_g_at	0.1773	1.135	4.949	7.641	0.6
  X38044_at	0.1962	1.245	4.945	8.388	0.2
  X39545_at	0.1809	1.237	4.899	8.228	0.5
  X36569_at	0.1703	0.8328	4.761	5.009	0.15
  X33102_at	0.1707	1.136	4.726	7.095	0.45
  X39315_at	0.1553	0.9308	4.683	5.578	0.55
  X863_g_at	0.1782	1.316	4.66	10.65	0.4
  X41483_s_at	0.154	1.238	4.618	8.249	0.3
  X36638_at	0.1564	1.194	4.568	8.184	0.25
  X36864_at	0.1688	1.053	4.502	6.438	0.8
  X41106_at	0.1548	0.9481	4.424	5.609	0.2
  X40248_at	0.1546	1.589	4.399	13.57	0.35
  X41385_at	0.1277	1.527	4.334	12.77	0.85
  X32225_at	0.1634	1.498	4.259	11.66	0.2
  X32747_at	0.1191	1.015	4.235	6.037	0.1
  X41741_at	0.1472	1.138	4.233	7.332	0.25
  X33741_at	0.1267	1.172	4.143	7.452	0.1
  X38322_at	0.1386	1.034	4.08	6.872	0.55
  X38406_f_at	0.1338	1.047	3.973	6.573	1
  X1846_at	0.1089	0.9862	3.911	5.898	0.3
  X37043_at	0.1177	1.089	3.863	6.781	0.1
  X1052_s_at	0.107	1.132	3.778	7.207	0.15
  X34820_at	0.1008	1.02	3.722	6.239	0.1
  X40243_at	0.1175	1.525	3.614	12.06	0.1
  X35905_s_at	0.1183	1.568	3.589	12.74	0.15
  X37599_at	0.1078	1.091	3.577	6.717	0.1
  X38391_at	0.07438	0.7246	2.703	4.563	0.1

• 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 = 0.75,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 : X32780_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)

Features (continued below)

	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	0.996
X38406_f_at	0	0.8467
X40282_s_at	0	0.584
X37720_at	0	0.676
X32598_at	0	0.9
X41468_at	0	0.564
X41288_at	0	0.7667
X32243_g_at	0	0.684
X1767_s_at	0	0.7787
X37068_at	0	0.696
X37366_at	0	0.4507
X40856_at	0	0.788
X39756_g_at	0	0.5293
X36601_at	0	0.528
X39315_at	0	0.6987
X33121_g_at	0	0.716
X31444_s_at	0	0.528
X769_s_at	0	0.36
X36491_at	0	0.4813
X40436_g_at	0	0.708
X34840_at	0	0.732
X36589_at	0	0.4627
X33198_at	0	0.668
X36666_at	0	0.4347
X32206_at	0	0.5147
X38028_at	0	0.7907
X38044_at	0	0.656
X34775_at	0	0.184
X31538_at	0	0.2373
X33137_at	0	0.5293