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<h1 class="title toc-ignore">GTEx SNP-gene association statistics used in mash analysis</h1>
<h4 class="author"><em>Sarah Urbut, Gao Wang, Peter Carbonetto and Matthew Stephens</em></h4>
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<p><strong>Last updated:</strong> 2018-06-21</p>
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<pre><code>
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Peter Carbonetto
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I have a complete first draft of the gtexdata page.
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Peter Carbonetto
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2018-06-21
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Added info to gtexdata page.
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Peter Carbonetto
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2018-06-21
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wflow_publish(“gtexdata.Rmd”)
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Rmd
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<a href="https://github.com/stephenslab/gtexresults/blob/87259b7e8e0614ac94f9b252c0d8c1c4adc1186c/analysis/gtexdata.Rmd" target="_blank">87259b7</a>
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Peter Carbonetto
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2018-06-21
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wflow_publish(“gtexdata.Rmd”)
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<p></details></p>
<hr />
<div id="overview" class="section level2">
<h2>Overview</h2>
<p>To apply multivariate adaptive shrinkage (<em>mash</em>) to data from the <a href="http://gtexportal.org">GTEx study</a>, we created an R data set (serialized R object) containing matrices of SNP-gene association statistics. These association statistics include effect estimates, <em>Z</em> scores and corresponding standard errors.</p>
<p>See <a href="fastqtl2mash.html">here</a> for the scripts used to generate these statistics from the SNP-gene data that were provided by the <a href="http://gtexportal.org">GTEx Project</a>.</p>
</div>
<div id="how-to-download-the-data" class="section level2">
<h2>How to download the data</h2>
<p>These are the recommended steps for retrieving the GTEx SNP-gene association statistics:</p>
<ol style="list-style-type: decimal">
<li><p>Download or clone the <a href="https://github.com/stephenslab/gtexresults">git repository</a>.</p></li>
<li><p>The association statistics are found in file <code>MatrixEQTLSumStats.Portable.Z.rds</code>.</p></li>
</ol>
</div>
<div id="how-to-load-the-data-into-r" class="section level2">
<h2>How to load the data into R</h2>
<p>Change the working directory in R (or RStudio) to the <code>analysis</code> directory of the <code>gtexresults</code> repository, e.g.,</p>
<pre class="r"><code>setwd("gtexresults/analysis")</code></pre>
<p>Next, read the data object into R:</p>
<pre class="r"><code>dat <- readRDS("../data/MatrixEQTLSumStats.Portable.Z.rds")</code></pre>
<p>Then get an overview of the data from this file:</p>
<pre class="r"><code>names(dat)
# [1] "strong.b" "strong.s" "strong.z" "random.b"
# [5] "random.s" "random.z" "random.test.b" "random.test.s"
# [9] "random.test.z" "vhat"</code></pre>
</div>
<div id="description-of-the-data" class="section level2">
<h2>Description of the data</h2>
<p>This file contains SNP-gene association statistics for 16,069 genes and 44 human tissues. These 16,069 genes were selected because they all showed some indication of being expressed in all 44 tissues. Therefore, the association statistics are stored as matrices each with 16,069 rows and 44 columns, e.g.,</p>
<pre class="r"><code>dim(dat$strong.b)
# [1] 16069 44</code></pre>
<p>As input to mash, we use a matrix of expression quantitative trait loci (eQTL) effect estimate, and corresponding standard errors. (We also provide <em>Z</em> scores.) See the manuscript for details on how these association statistics were obtained.</p>
<p>These association statistics were subdivided into three subsets:</p>
<ol style="list-style-type: decimal">
<li><p>Results from a subset “strong” tests. These tests were identified by taking the “top eQTL” in each gene based on univariate SNP-gene association tests. (Here, “top eQTL” for a given gene is defined as the SNP with the largest (univariate) <em>Z</em> statistic among all 44 tissues. The estimated effects, <em>Z</em> scores and standard errors for the strong tests are stored in three <span class="math inline">\(16,069 \times 44\)</span> matrices, <code>dat$strong.b</code>, <code>dat$strong.z</code> and <code>dat$strong.s</code>.</p></li>
<li><p>Results from a random subset of 20,000 SNP-gene tests (this includes both “null” and “non”-null tests). The estimated effects, <em>Z</em> stores and standard errors for these random tests are stored in three <span class="math inline">\(20,000 \times 44\)</span> matrices, <code>dat$random.b</code>, <code>dat$random.z</code> and <code>dat$random.z</code>.</p></li>
<li><p>Results from a second random subset of 28,198 SNP-gene tests. This is used for the cross-validation part of the mash analysis. The estimated effects, <em>Z</em> stores and standard errors for these random tests are stored in three <span class="math inline">\(28,198 \times 44\)</span> matrices, <code>dat$random.test.b</code>, <code>dat$random.test.z</code> and <code>dat$random.test.z</code>.</p></li>
</ol>
<p>Finally, the gene expression measurements in the GTEx study are correlated due to sample overlap (sometimes multiple measurements were obtained from the same individual). Therefore, we have also estimated a correlation matrix, which is stored in <code>dat$vhat</code>:</p>
<pre class="r"><code>dim(dat$vhat)
# [1] 44 44</code></pre>
<p>See the manuscript for additional details how these data are used in the mash analysis.</p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.5
#
# 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
#
# loaded via a namespace (and not attached):
# [1] workflowr_1.0.1.9000 Rcpp_0.12.17 digest_0.6.15
# [4] rprojroot_1.3-2 R.methodsS3_1.7.1 backports_1.1.2
# [7] git2r_0.21.0 magrittr_1.5 evaluate_0.10.1
# [10] stringi_1.1.7 whisker_0.3-2 R.oo_1.21.0
# [13] R.utils_2.6.0 rmarkdown_1.9 tools_3.4.3
# [16] stringr_1.3.0 yaml_2.1.18 compiler_3.4.3
# [19] htmltools_0.3.6 knitr_1.20</code></pre>
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