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type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar"> <span class="icon-bar"></span> <span class="icon-bar"></span> <span class="icon-bar"></span> </button> <a class="navbar-brand" href="index.html">truncash</a> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> <a href="index.html">Home</a> </li> <li> <a href="about.html">About</a> </li> <li> <a href="license.html">License</a> </li> </ul> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/LSun/truncash"> <span class="fa fa-github"></span> </a> </li> </ul> </div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> <div class="fluid-row" id="header"> <h1 class="title toc-ignore">Real Data with Simulated Signals: Part V</h1> <h4 class="author"><em>Lei Sun</em></h4> <h4 class="date"><em>2017-06-03</em></h4> </div> <!-- The file analysis/chunks.R contains chunks that define default settings shared across the workflowr files. --> <!-- Update knitr chunk options --> <!-- Insert the date the file was last updated --> <p><strong>Last updated:</strong> 2017-12-21</p> <!-- Insert the code version (Git commit SHA1) if Git repository exists and R package git2r is installed --> <p><strong>Code version:</strong> 6e42447</p> <!-- Add your analysis here --> <pre class="r"><code>library(edgeR) library(limma) library(sva) library(cate) library(vicar) library(ashr) library(pROC) source("../code/gdash.R")</code></pre> <pre class="r"><code>mat = readRDS("../data/liver.sim.rds")</code></pre> <pre class="r"><code>counts_to_summary = function (counts, design) { dgecounts = edgeR::calcNormFactors(edgeR::DGEList(counts = counts, group = design[, 2])) v = limma::voom(dgecounts, design, plot = FALSE) lim = limma::lmFit(v) r.ebayes = limma::eBayes(lim) p = r.ebayes$p.value[, 2] t = r.ebayes$t[, 2] z = sign(t) * qnorm(1 - p/2) betahat = lim$coefficients[,2] sebetahat = betahat / z return (list(betahat = betahat, sebetahat = sebetahat, z = z)) }</code></pre> <pre class="r"><code>one_sim <- function (mat, ngene, nsamp, pi0, sd) { ## add simulated signals mat.sim = seqgendiff::poisthin(t(mat), nsamp = nsamp, ngene = ngene, gselect = "random", signal_params = list(mean = 0, sd = sd), prop_null = pi0) counts = t(mat.sim$Y) ## ngene * nsamples matrix design = mat.sim$X beta = mat.sim$beta which_signal = (beta != 0) ## methods using summary statistics only summary = counts_to_summary(counts, design) fit.pvalue = (1 - pnorm(abs(summary$z))) * 2 fit.BH = p.adjust(fit.pvalue, method = "BH") fit.qvalue = qvalue::qvalue(fit.pvalue) fit.locfdr = locfdr::locfdr(summary$z, bre = round(ngene / 20), plot = 0) fit.ash = ashr::ash(summary$betahat, summary$sebetahat, mixcompdist = "normal", method = "fdr") fit.gdash = gdash(summary$betahat, summary$sebetahat) fit.gdash.ash = ashr::ash(summary$betahat, summary$sebetahat, fixg = TRUE, g = fit.gdash$fitted_g) ## methods using data matrix Y = t(log(counts + 0.5)) X = design num_sv <- sva::num.sv(dat = t(Y), mod = X, method = "be") mout <- vicar::mouthwash(Y = Y, X = X, k = num_sv, cov_of_interest = 2, include_intercept = FALSE) cate_cate <- cate::cate.fit(X.primary = X[, 2, drop = FALSE], X.nuis = X[, -2, drop = FALSE], Y = Y, r = num_sv, adj.method = "rr") sva_sva <- sva::sva(dat = t(Y), mod = X, mod0 = X[, -2, drop = FALSE], n.sv = num_sv) X.sva <- cbind(X, sva_sva$sv) lmout <- limma::lmFit(object = t(Y), design = X.sva) eout <- limma::ebayes(lmout) svaout <- list() svaout$betahat <- lmout$coefficients[, 2] svaout$sebetahat <- lmout$stdev.unscaled[, 2] * sqrt(eout$s2.post) svaout$pvalues <- eout$p.value[, 2] ## result: roc auc roc_res = c( pvalue = pROC::roc(response = which_signal, predictor = fit.pvalue)$auc, BH = pROC::roc(response = which_signal, predictor = fit.BH)$auc, qvalue = pROC::roc(response = which_signal, predictor = fit.qvalue$lfdr)$auc, locfdr = pROC::roc(response = which_signal, predictor = fit.locfdr$fdr)$auc, ash = pROC::roc(response = which_signal, predictor = ashr::get_lfdr(fit.ash))$auc, cash = pROC::roc(response = which_signal, predictor = ashr::get_lfdr(fit.gdash.ash))$auc, mouthwash = pROC::roc(response = which_signal, predictor = c(mout$result$lfdr))$auc, cate = pROC::roc(response = which_signal, predictor = c(cate_cate$beta.p.value))$auc, sva = pROC::roc(response = which_signal, predictor = c(svaout$pvalues))$auc ) ## ash with summary statistics method_list <- list() method_list$cate <- list() method_list$cate$betahat <- c(cate_cate$beta) method_list$cate$sebetahat <- c(sqrt(cate_cate$beta.cov.row * cate_cate$beta.cov.col) / sqrt(nrow(X))) method_list$sva <- list() method_list$sva$betahat <- c(svaout$betahat) method_list$sva$sebetahat <- c(svaout$sebetahat) ashfit <- lapply(method_list, FUN = function(x) {ashr::ash(x$betahat, x$sebetahat, mixcompdist = "normal", method = "fdr")}) ashfit$ash <- fit.ash ashfit$cash <- fit.gdash.ash ashfit$mouthwash <- mout ashfit = ashfit[c("ash", "cash", "mouthwash", "cate", "sva")] ## pi0 pi0_res <- sapply(ashfit, FUN = ashr::get_pi0) pi0_res <- c( qvalue = fit.qvalue$pi0, locfdr = min(1, fit.locfdr$fp0["mlest", "p0"]), pi0_res ) ## mse mse_res <- sapply(ashfit, FUN = function(x) {mean((ashr::get_pm(x) - beta)^2)}) mse_res <- c(ols = mean((summary$betahat - beta)^2), mse_res) ## pFDP calibration pFDP_alpha = function (alpha, tail_stat, true, obs) { return(1 - mean(true[tail_stat <= alpha])) } pFSP_alpha = function (alpha, tail_stat, true, obs) { return(mean(sign(obs[tail_stat <= alpha]) != sign(true[tail_stat <= alpha]))) } tail_cali_list = function (alpha_list, tail_cali_alpha, tail_stat, true, obs) { sapply(alpha_list, tail_cali_alpha, tail_stat, true, obs) } alpha_list = seq(0, 0.2, by = 0.001) pFDP <- sapply( ashfit, FUN = function (x) { tail_cali_list(alpha_list, pFDP_alpha, ashr::get_qvalue(x), which_signal, x$data$x) } ) pFDP_BH = tail_cali_list(alpha_list, pFDP_alpha, fit.BH, which_signal, summary$betahat) pFDP_qvalue = tail_cali_list(alpha_list, pFDP_alpha, fit.qvalue$qvalues, which_signal, summary$betahat) pFDP_res = cbind(BH = pFDP_BH, qvalue = pFDP_qvalue, pFDP) ## pFSR calibration pFSP_res <- sapply( ashfit, FUN = function (x) { tail_cali_list(alpha_list, pFSP_alpha, ashr::get_svalue(x), beta, x$data$x) } ) return(list(pi = pi0_res, mse = mse_res, auc = roc_res, alpha = alpha_list, pFDP = pFDP_res, pFSP = pFSP_res)) }</code></pre> <pre class="r"><code>n_sim = function (n, mat, ngene, nsamp, pi0, sd) { pi0_list = mse_list = auc_list = pFDP_list = pFSP_list = list() for (i in 1 : n) { one_res = one_sim(mat, ngene, nsamp, pi0, sd) pi0_list[[i]] = one_res$pi mse_list[[i]] = one_res$mse auc_list[[i]] = one_res$auc pFDP_list[[i]] = one_res$pFDP pFSP_list[[i]] = one_res$pFSP } alpha_vec = one_res$alpha pi0_mat = matrix(unlist(pi0_list), nrow = n, byrow = TRUE) colnames(pi0_mat) = names(pi0_list[[1]]) mse_mat = matrix(unlist(mse_list), nrow = n, byrow = TRUE) colnames(mse_mat) = names(mse_list[[1]]) auc_mat = matrix(unlist(auc_list), nrow = n, byrow = TRUE) colnames(auc_mat) = names(auc_list[[1]]) pFDP_mat = list() for (j in 1 : ncol(pFDP_list[[1]])) { pFDP_mat[[j]] = t(sapply(pFDP_list, FUN = function(x) {rbind(x[, j])})) } names(pFDP_mat) = colnames(pFDP_list[[1]]) pFSP_mat = list() for (j in 1 : ncol(pFSP_list[[1]])) { pFSP_mat[[j]] = t(sapply(pFSP_list, FUN = function(x) {rbind(x[, j])})) } names(pFSP_mat) = colnames(pFSP_list[[1]]) return(list(pi0 = pi0_mat, mse = mse_mat, auc = auc_mat, alpha = alpha_vec, pFDP = pFDP_mat, pFSP = pFSP_mat)) }</code></pre> <pre class="r"><code>sd = 0.6 pi0 = 0.9 ngene = 1e3 nsamp = 10 nsim = 100 set.seed(777) system.time(res <- n_sim(nsim, mat, ngene, nsamp, pi0, sd))</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 1 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 1 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 1 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 1 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 1 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 1 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 1 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Warning in log(rowSums(sweep(x = exp(ldmix - ldmax), MARGIN = 2, STATS = pi_vals, : NaNs produced</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code>Warning in locfdr::locfdr(summary$z, bre = round(ngene/20), plot = 0): CM estimation failed, middle of histogram non-normal</code></pre> <pre><code>Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 2 Iteration (out of 5 ):1 2 3 4 5 Number of significant surrogate variables is: 3 Iteration (out of 5 ):1 2 3 4 5 </code></pre> <pre><code> user system elapsed 1703.469 381.549 2135.044 </code></pre> <p><img src="figure/real_data_simulation_5.rmd/unnamed-chunk-7-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation_5.rmd/unnamed-chunk-7-2.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation_5.rmd/unnamed-chunk-7-3.png" width="672" style="display: block; margin: auto;" /></p> <p><img src="figure/real_data_simulation_5.rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation_5.rmd/unnamed-chunk-8-2.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation_5.rmd/unnamed-chunk-8-3.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation_5.rmd/unnamed-chunk-8-4.png" width="672" style="display: block; margin: auto;" /></p> <section id="session-information" class="level2"> <h2>Session information</h2> <!-- Insert the session information into the document --> <pre class="r"><code>sessionInfo()</code></pre> <pre><code>R version 3.4.3 (2017-11-30) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS High Sierra 10.13.2 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] Rmosek_8.0.69 PolynomF_1.0-1 CVXR_0.94-4 [4] REBayes_1.2 Matrix_1.2-12 SQUAREM_2017.10-1 [7] EQL_1.0-0 ttutils_1.0-1 pROC_1.10.0 [10] ashr_2.2-2 vicar_0.1.6 cate_1.0.4 [13] sva_3.26.0 BiocParallel_1.12.0 genefilter_1.60.0 [16] mgcv_1.8-22 nlme_3.1-131 edgeR_3.20.2 [19] limma_3.34.4 loaded via a namespace (and not attached): [1] Biobase_2.38.0 svd_0.4.1 bit64_0.9-7 [4] splines_3.4.3 foreach_1.4.4 ECOSolveR_0.3-2 [7] R.utils_2.6.0 stats4_3.4.3 blob_1.1.0 [10] yaml_2.1.16 RSQLite_2.0 backports_1.1.2 [13] lattice_0.20-35 digest_0.6.13 colorspace_1.3-2 [16] R.oo_1.21.0 htmltools_0.3.6 plyr_1.8.4 [19] XML_3.98-1.9 esaBcv_1.2.1 xtable_1.8-2 [22] corpcor_1.6.9 scales_0.5.0 scs_1.1-1 [25] git2r_0.20.0 tibble_1.3.4 annotate_1.56.1 [28] gmp_0.5-13.1 IRanges_2.12.0 ggplot2_2.2.1 [31] BiocGenerics_0.24.0 lazyeval_0.2.1 Rmpfr_0.6-1 [34] survival_2.41-3 magrittr_1.5 memoise_1.1.0 [37] evaluate_0.10.1 R.methodsS3_1.7.1 doParallel_1.0.11 [40] MASS_7.3-47 truncnorm_1.0-7 tools_3.4.3 [43] matrixStats_0.52.2 stringr_1.2.0 S4Vectors_0.16.0 [46] munsell_0.4.3 locfit_1.5-9.1 AnnotationDbi_1.40.0 [49] compiler_3.4.3 rlang_0.1.4 grid_3.4.3 [52] leapp_1.2 RCurl_1.95-4.8 iterators_1.0.9 [55] bitops_1.0-6 rmarkdown_1.8 gtable_0.2.0 [58] codetools_0.2-15 DBI_0.7 R6_2.2.2 [61] ruv_0.9.6 knitr_1.17 bit_1.1-12 [64] rprojroot_1.3-1 stringi_1.1.6 pscl_1.5.2 [67] parallel_3.4.3 Rcpp_0.12.14 </code></pre> </section> <hr> <p> This <a href="http://rmarkdown.rstudio.com">R Markdown</a> site was created with <a href="https://github.com/jdblischak/workflowr">workflowr</a> </p> <hr> <!-- To enable disqus, uncomment the section below and provide your disqus_shortname --> <!-- disqus <div id="disqus_thread"></div> <script type="text/javascript"> /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ var disqus_shortname = 'rmarkdown'; // required: replace example with your forum shortname /* * * DON'T EDIT BELOW THIS LINE * * */ (function() { var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); })(); </script> <noscript>Please enable JavaScript to view the <a href="http://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript> <a 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