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Type 'q()' to quit R. > library(ClassComparison) Loading required package: oompaBase > > nGenes <- 1000 > nSamplesPerGroup <- 10 > nGroups <- 2 > > suppressWarnings( RNGversion("3.5.3") ) > set.seed(944637) > data <- matrix(rnorm(nGenes*nSamplesPerGroup*nGroups), + nrow=nGenes) > classes <- factor(rep(c("A", "B"), each=nSamplesPerGroup)) > > mtt <- MultiTtest(data, classes) > summary(mtt) Row-by-row two-sample t-tests with 1000 rows Positive sign indicates an increase in class: A Call: MultiTtest(data = data, classes = classes) T-statistics: Min. 1st Qu. Median Mean 3rd Qu. Max. -3.25711 -0.72072 -0.01096 -0.02219 0.66601 3.30242 P-values: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00396 0.25717 0.50326 0.49595 0.71980 0.99873 > > mw <- MultiWilcoxonTest(data, classes) Warning messages: 1: In hist.default(wilstats, breaks = histbreaks, plot = FALSE, probability = TRUE) : argument 'probability' is not made use of 2: In bs(X, degree = 3L, knots = c(93.6666666666667, 116.333333333333 : some 'x' values beyond boundary knots may cause ill-conditioned bases > summary(mw) Call: MultiWilcoxonTest(data = data, classes = classes) Row-by-row Wilcoxon rank-sum tests with 1000 rows Rank-sum statistics: Min. 1st Qu. Median Mean 3rd Qu. Max. 67.0 95.0 105.0 104.7 114.0 141.0 Large values indicate an increase in class: A With prior = 1 and alpha = 0.9 the upper tail contains 0 values above 155 the lower tail contains 0 values below 58 > > mlm <- MultiLinearModel(Y ~ classes, data.frame(classes=classes), data) > summary(mlm) Row-by-row linear models with 1000 rows Call: MultiLinearModel(form = Y ~ classes, clindata = data.frame(classes = classes), arraydata = data) F-statistics: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000003 0.132789 0.466590 1.059960 1.369473 10.905953 P-values: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00396 0.25717 0.50326 0.49595 0.71980 0.99873 > > dud <- Dudoit(data, classes, nPerm=100, verbose=FALSE) > summary(dud) Row-by-row two-sample t-tests with 1000 rows Positive sign indicates an increase in class: A Call: Dudoit(data = data, classes = classes, nPerm = 100, verbose = FALSE) T-statistics: Min. 1st Qu. Median Mean 3rd Qu. Max. -3.25711 -0.72072 -0.01096 -0.02219 0.66601 3.30242 P-values: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00396 0.25717 0.50326 0.49595 0.71980 0.99873 > > tn <- TNoM(data, classes) [1] "ordering..." [1] "matrifying..." [1] "lapplying..." [1] "cumsuming..." [1] "more matrifying..." [1] "another apply..." > summary(tn) TNoM object with 1000 rows and 20 columns Call: TNoM(data = data, classes = classes) Number of genes with maximum number of misclassifications: 0 1 2 3 4 5 6 7 8 9 10 0 0 0 9 38 134 242 367 201 9 0 > > sam <- Sam(data, classes) 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . 13 . 14 . 15 . 16 . 17 . 18 . 19 . 20 . 21 . 22 . 23 . 24 . 25 . 26 . 27 . 28 . 29 . 30 . 31 . 32 . 33 . 34 . 35 . 36 . 37 . 38 . 39 . 40 . 41 . 42 . 43 . 44 . 45 . 46 . 47 . 48 . 49 . 50 . 51 . 52 . 53 . 54 . 55 . 56 . 57 . 58 . 59 . 60 . 61 . 62 . 63 . 64 . 65 . 66 . 67 . 68 . 69 . 70 . 71 . 72 . 73 . 74 . 75 . 76 . 77 . 78 . 79 . 80 . 81 . 82 . 83 . 84 . 85 . 86 . 87 . 88 . 89 . 90 . 91 . 92 . 93 . 94 . 95 . 96 . 97 . 98 . 99 . 100 . > summary(sam) Using a cutoff of 1 , we called 999 genes significant with expected FDR = 0.997 ( 996 ) Warning message: In min(object@observed[positive]) : no non-missing arguments to min; returning Inf > > tgs <- TwoGroupStats(data, classes) > summary(tgs) first group: 10 second group: 10 mean1 mean2 overallMean var1 Min. :-1.066820 Min. :-0.954547 Min. :-0.794206 Min. :0.09493 1st Qu.:-0.236997 1st Qu.:-0.210246 1st Qu.:-0.156798 1st Qu.:0.68727 Median :-0.006114 Median : 0.011374 Median :-0.015762 Median :0.92272 Mean :-0.011047 Mean :-0.002704 Mean :-0.006876 Mean :1.01419 3rd Qu.: 0.204623 3rd Qu.: 0.202182 3rd Qu.: 0.143322 3rd Qu.:1.27837 Max. : 0.945447 Max. : 1.019569 Max. : 0.656275 Max. :3.05482 var2 overallVar pooledVar Min. :0.07301 Min. :0.3366 Min. :0.3217 1st Qu.:0.64692 1st Qu.:0.7691 1st Qu.:0.7532 Median :0.90129 Median :0.9683 Median :0.9726 Mean :0.99857 Mean :1.0045 Mean :1.0064 3rd Qu.:1.27840 3rd Qu.:1.1985 3rd Qu.:1.2096 Max. :2.86346 Max. :2.0843 Max. :2.1399 > smoo <- SmoothTtest(tgs) > summary(smoo) Smooth T test of Group One versus Group Two AverageLogIntensity LogRatio SmoothedTStatistic FirstBadFlag Min. :-0.794206 Min. :-1.457071 Min. :-3.29366 Min. :0.3163 1st Qu.:-0.156798 1st Qu.:-0.296676 1st Qu.:-0.68189 1st Qu.:0.8405 Median :-0.015762 Median : 0.006090 Median : 0.01380 Median :0.9790 Mean :-0.006876 Mean : 0.008343 Mean : 0.01937 Mean :1.0001 3rd Qu.: 0.143322 3rd Qu.: 0.317983 3rd Qu.: 0.72420 3rd Qu.:1.1483 Max. : 0.656275 Max. : 1.344550 Max. : 3.05336 Max. :1.7658 SecondBadFlag Min. :0.2750 1st Qu.:0.8278 Median :0.9777 Mean :0.9989 3rd Qu.:1.1565 Max. :1.7499 > #plot(smoo@smooth.t.statistics, mtt@t.statistics) > > proc.time() user system elapsed 2.29 0.17 2.45