### R代码从vignette源的RBM。Rnw ' ################################################### ### 代码块1号:遏制。Rnw: 47-49 (eval = FALSE ) ################################################### ## 源(“//www.anjoumacpherson.com/biocLite.R”)# # biocLite(“遏制 ") ################################################### ### 代码块2号:遏制。可Rnw: 55 - (eval = FALSE ) ################################################### ## 图书馆组织遏制 ) ################################################### ### 代码块3号:遏制。Rnw: 69 - 85 ################################################### 图书馆组织遏制normdata < -矩阵(rnorm(1000 * 6 0 1) 1000年,6)mydesign < - c (0, 0, 0, 1, 1, 1) myresult < - RBM_T (0.05 normdata mydesign, 100年)总结(myresult)和(myresult permutation_p < = 0.05美元)(myresult permutation_p < = 0.05美元)和(myresult bootstrap_p < = 0.05美元)(myresult bootstrap_p < = 0.05美元)permutation_adjp < - p.adjust (myresult permutation_p美元,“黑洞”)和(permutation_adjp < = 0.05) bootstrap_adjp < p.adjust (myresult bootstrap_p美元,“黑洞”)和(bootstrap_adjp < = 0.05 ) ################################################### ### 代码块数量4:遏制。Rnw: 89 - 97 ################################################### unifdata < -矩阵(runif(1000 * 7, 0.10, 0.95), 1000年,7)mydesign2 < - c (0, 0, 0, 1, 1, 1, 1) myresult2 < - RBM_T (unifdata mydesign2,100, 0.05)和(myresult2 permutatioin_p < = 0.05美元)和(myresult2 bootstrap_p < = 0.05美元)(myresult2 bootstrap_p < = 0.05美元)bootstrap2_adjp < - p.adjust (myresult2 bootstrap_p美元,“黑洞”)和(bootstrap2_adjp < = 0.05 ) ################################################### ### 代码块5号:遏制。Rnw: 103 - 129 ################################################### normdata_F < -矩阵(rnorm(1000 * 9 0 2), 1000年,9)mydesign_F < - c (0, 0, 0, 1, 1, 1, 2, 2, 2) aContrast < - c(“X1-X0”、“X2-X1”,“X2-X0”)myresult_F < - RBM_F (normdata_F, mydesign_F aContrast 100, 0.05)总结(myresult_F)和(myresult_F permutation_p美元[1]< = 0.05)和(myresult_F permutation_p美元[2]< = 0.05)和(myresult_F permutation_p美元[3]< = 0.05)它(myresult_F permutation_p美元[1]< = 0.05)它(myresult_F $ permutation_p [2) < = 0.05) (myresult_F permutation_p美元[3]< = 0.05)con1_adjp < - p.adjust (myresult_F permutation_p美元[1],“黑洞”)和(con1_adjp < = 0.05 / 3) con2_adjp < - p.adjust (myresult_F permutation_p美元[2],“黑洞”)和(con2_adjp < = 0.05 / 3) con3_adjp < - p.adjust (myresult_F permutation_p美元[3],“黑洞”)和(con3_adjp < = 0.05 / 3) (con2_adjp < = 0.05 / 3) (con3_adjp < = 0.05 / 3 ) ################################################### ### 代码块6号:遏制。Rnw: 133 - 160 ################################################### unifdata_F < -矩阵(runif(1000 * 18, 0.15, 0.98), 1000年,18)mydesign2_F < - c(代表(0,6),代表(1,6),代表(2,6))aContrast < - c(“X1-X0”、“X2-X1”,“X2-X0”)myresult2_F < - RBM_F (unifdata_F, mydesign2_F aContrast 100, 0.05)总结(myresult2_F)和(myresult2_F bootstrap_p美元[1]< = 0.05)和(myresult2_F bootstrap_p美元[2]< = 0.05)和(myresult2_F bootstrap_p美元[3]< = 0.05)它(myresult2_F bootstrap_p美元[1]< = 0.05)它(myresult2_F $ bootstrap_p [2) < = 0.05) (myresult2_F bootstrap_p美元[3]< = 0.05)con21_adjp < - p.adjust (myresult2_F bootstrap_p美元[1],“黑洞”)和(con21_adjp < = 0.05 / 3) con22_adjp < - p.adjust (myresult2_F bootstrap_p美元[2],“黑洞”)和(con22_adjp < = 0.05 / 3) con23_adjp < - p.adjust (myresult2_F bootstrap_p美元[3],“黑洞”)和(con23_adjp < = 0.05 / 3 ) ################################################### ### 代码块7号:遏制。Rnw: 169 - 193 ################################################### 系统。file("data", package = "RBM") data(ovarian_cancer_methylation) summary(ovarian_cancer_methylation) ovarian_cancer_data <- ovarian_cancer_methylation[, -1] label <- c(1, 1, 0, 0, 1, 1, 0, 0) diff_results <- RBM_T(aData=ovarian_cancer_data, vec_trt=label, repetition=100, alpha=0.05) summary(diff_results) sum(diff_results$ordfit_pvalue<=0.05) sum(diff_results$permutation_p<=0.05) sum(diff_results$bootstrap_p<=0.05) ordfit_adjp <- p.adjust(diff_results$ordfit_pvalue, "BH") sum(ordfit_adjp<=0.05) perm_adjp <- p.adjust(diff_results$permutation_p, "BH") sum(perm_adjp<=0.05) boot_adjp <- p.adjust(diff_results$bootstrap_p, "BH") sum(boot_adjp<=0.05) diff_list_perm <- which(perm_adjp<=0.05) diff_list_boot <- which(boot_adjp<=0.05) sig_results_perm <- cbind(ovarian_cancer_methylation[diff_list_perm, ], diff_results$ordfit_t[diff_list_perm], diff_results$permutation_p[diff_list_perm]) print(sig_results_perm) sig_results_boot <- cbind(ovarian_cancer_methylation[diff_list_boot, ], diff_results$ordfit_t[diff_list_boot], diff_results$bootstrap_p[diff_list_boot]) print(sig_results_boot)