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<h1 class="title toc-ignore">Real Data with Simulated Signals: Part I</h1>
<h4 class="author"><em>Lei Sun</em></h4>
<h4 class="date"><em>2017-05-18</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> 2017-11-07</p>
<!-- Insert the code version (Git commit SHA1) if Git repository exists and R
package git2r is installed -->
<p><strong>Code version:</strong> 2c05d59</p>
<!-- Add your analysis here -->
<pre class="r"><code>library(ashr)
library(edgeR)
library(limma)
library(qvalue)
library(seqgendiff)
library(sva)
library(cate)</code></pre>
<pre class="r"><code>source("../code/gdash.R")</code></pre>
<pre><code>Warning: replacing previous import 'Matrix::crossprod' by 'gmp::crossprod'
when loading 'cvxr'</code></pre>
<pre><code>Warning: replacing previous import 'Matrix::tcrossprod' by
'gmp::tcrossprod' when loading 'cvxr'</code></pre>
<div id="introduction" class="section level2">
<h2>Introduction</h2>
<p>Using David’s package <code>seqgendiff</code>, we are adding artefactual signals to the real GTEx Liver RNA-seq data.</p>
<pre class="r"><code>mat = read.csv("../data/liver.csv")</code></pre>
<p>The true signal comes from a mixture distribution</p>
<p><span class="math display">\[
g\left(\beta\right) = \pi_0\delta_0 + \left(1 - \pi_0\right)N\left(0, \sigma^2\right)
\]</span> The simulated data matrices are then fed into <code>edgeR</code>, <code>limma</code> pipeline. In the following simulations, we always use <span class="math inline">\(5\)</span> vs <span class="math inline">\(5\)</span>.</p>
</div>
<div id="case-1-pi_0-0.9-sigma2-1." class="section level2">
<h2>Case 1: <span class="math inline">\(\pi_0 = 0.9\)</span>, <span class="math inline">\(\sigma^2 = 1\)</span>.</h2>
<pre class="r"><code>N = 100
nsamp = 10
pi0 = 0.9
sd = 1
system.time(ashvgdash <- N_simulations(N, mat, nsamp, pi0, sd))</code></pre>
<pre><code> user system elapsed
6854.603 627.845 12855.081 </code></pre>
<p><img src="figure/real_data_simulation.rmd/unnamed-chunk-2-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation.rmd/unnamed-chunk-2-2.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="case-2-pi_0-0.9-sigma2-4" class="section level2">
<h2>Case 2: <span class="math inline">\(\pi_0 = 0.9\)</span>, <span class="math inline">\(\sigma^2 = 4\)</span></h2>
<pre class="r"><code>N = 100
nsamp = 10
pi0 = 0.9
sd = 2
system.time(ashvgdash <- N_simulations(N, mat, nsamp, pi0, sd))</code></pre>
<pre><code> user system elapsed
5877.223 621.433 5256.693 </code></pre>
<p><img src="figure/real_data_simulation.rmd/unnamed-chunk-3-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation.rmd/unnamed-chunk-3-2.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="case-3-pi_0-0.5-sigma2-4" class="section level2">
<h2>Case 3: <span class="math inline">\(\pi_0 = 0.5\)</span>, <span class="math inline">\(\sigma^2 = 4\)</span></h2>
<pre class="r"><code>N = 100
nsamp = 10
pi0 = 0.5
sd = 2
system.time(ashvgdash <- N_simulations(N, mat, nsamp, pi0, sd))</code></pre>
<pre><code> user system elapsed
5269.488 574.588 15042.479 </code></pre>
<p><img src="figure/real_data_simulation.rmd/unnamed-chunk-4-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation.rmd/unnamed-chunk-4-2.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="case-4-pi_0-0.9-sigma2-9" class="section level2">
<h2>Case 4: <span class="math inline">\(\pi_0 = 0.9\)</span>, <span class="math inline">\(\sigma^2 = 9\)</span></h2>
<pre class="r"><code>N = 100
nsamp = 10
pi0 = 0.9
sd = 3
system.time(ashvgdash <- N_simulations(N, mat, nsamp, pi0, sd))</code></pre>
<pre><code> user system elapsed
5321.625 558.205 15356.345 </code></pre>
<p><img src="figure/real_data_simulation.rmd/unnamed-chunk-5-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/real_data_simulation.rmd/unnamed-chunk-5-2.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<!-- Insert the session information into the document -->
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
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_7.1.3 PolynomF_0.94 cvxr_0.0.0.9400
[4] REBayes_0.85 Matrix_1.2-11 SQUAREM_2017.10-1
[7] EQL_1.0-0 ttutils_1.0-1 cate_1.0.4
[10] sva_3.26.0 BiocParallel_1.12.0 genefilter_1.60.0
[13] mgcv_1.8-22 nlme_3.1-131 seqgendiff_0.1.0
[16] qvalue_2.10.0 edgeR_3.20.1 limma_3.34.0
[19] ashr_2.1-27
loaded via a namespace (and not attached):
[1] Biobase_2.38.0 svd_0.4.1 bit64_0.9-7
[4] splines_3.4.2 foreach_1.4.3 stats4_3.4.2
[7] blob_1.1.0 yaml_2.1.14 RSQLite_2.0
[10] backports_1.1.1 lattice_0.20-35 digest_0.6.12
[13] colorspace_1.3-2 htmltools_0.3.6 plyr_1.8.4
[16] XML_3.98-1.9 esaBcv_1.2.1 xtable_1.8-2
[19] corpcor_1.6.9 scales_0.5.0 git2r_0.19.0
[22] tibble_1.3.4 annotate_1.56.0 gmp_0.5-13.1
[25] IRanges_2.12.0 ggplot2_2.2.1 BiocGenerics_0.24.0
[28] lazyeval_0.2.1 survival_2.41-3 magrittr_1.5
[31] memoise_1.1.0 evaluate_0.10.1 doParallel_1.0.11
[34] MASS_7.3-47 truncnorm_1.0-7 tools_3.4.2
[37] matrixStats_0.52.2 stringr_1.2.0 S4Vectors_0.16.0
[40] munsell_0.4.3 locfit_1.5-9.1 AnnotationDbi_1.40.0
[43] compiler_3.4.2 rlang_0.1.4 grid_3.4.2
[46] leapp_1.2 RCurl_1.95-4.8 iterators_1.0.8
[49] bitops_1.0-6 rmarkdown_1.6 gtable_0.2.0
[52] codetools_0.2-15 DBI_0.7 reshape2_1.4.2
[55] ruv_0.9.6 knitr_1.17 bit_1.1-12
[58] rprojroot_1.2 stringi_1.1.5 pscl_1.5.2
[61] parallel_3.4.2 Rcpp_0.12.13 </code></pre>
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