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GLMaSPU.html
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<h1>aSPU in GLM</h1>
<h2>adaptive sum of powered score (aSPU) test in Generalized Linear Models</h2>
<p>Several tests for high-dimensional generalized linear models have been proposed recently, however, they are mainly based on a sum of squares of the score vector and only powerful under certain limited alternative hypotheses. In practice, since the associations in a true alternative hypothesis may be sparse or dense or between, the existing tests may or may not be powerful. Here, we propose an adaptive test that maintains high power across a wide range of scenarios. To calculate its p-value, its asymptotic null distribution is derived. Please cite the following manuscript for using the aSPU method:</p>
<div class="infoblock">
<div class="blockcontent">
<p>Wu et al. 2017 “An adaptive test on high-dimensional parameters in generalized linear models” Statistica Sinica, under revision.</p>
</div></div>
<p>For questions or comments regarding methods, contact Wei Pan (<a href="mailto:[email protected]" target=“blank”>[email protected]</a>) and Chong Wu (<a href="mailto:[email protected]" target=“blank”>[email protected]</a>);
For questions or comments regarding data & codes, contact Chong Wu (<a href="mailto:[email protected]" target=“blank”>[email protected]</a>).</p>
<h2>Installation</h2>
<p>To install the stable version from CRAN, simply run the following from an R console:</p>
<div class="infoblock">
<div class="blockcontent">
<p>install.packages(“GLMaSPU”)</p>
</div></div>
<p>To install the latest development builds directly from GitHub, run this instead:</p>
<div class="infoblock">
<div class="blockcontent">
<p>if (!require(“devtools”))
install.packages(“devtools”)
devtools::install_github(“ChongWu-Biostat/GLMaSPU”)</p>
</div></div>
<h2>Typical analysis and output</h2>
<p>This example assumes you have setup the required environment and data as illustrated in the previous section. All analyses are based on R/3.3.1.</p>
<h3>Input:</h3>
<p>At a minimum, we need the following inputs:</p>
<ol>
<li><p>Y - Response. It can be a binary or continuous trait. A vector with length n (number of observations).</p>
</li>
<li><p>X - Genotype or other data; each row for a subject, and each column for a variable of interest. An n by p matrix (n: number of observations, p: number of predictors).</p>
</li>
<li><p>cov - (Optional) Covariates. An n by q matrix (n: number of observations, q: number of covariates).
Additional columns are allowed but will be ignored. We recommend removing the additional columns before analysis to save space.</p>
</li>
<li><p>pow - Gamma set used in SPU test. A vector of the powers. By default, we use pow = c(1,2,…,6,Inf)</p>
</li>
<li><p>model - Corresponding to the Response. “gaussian” for a quantitative response; “binomial” for a binary response.</p>
</li>
</ol>
<h3>Performing the aSPU</h3>
<p>After we prepared the data, we can run GLMaSPU via the following single line.</p>
<div class="codeblock">
<div class="blockcontent"><pre>
aSPU_apval(Y, X, cov = cov, pow = c(1:6, Inf))
</pre></div></div>
<p><b>Note</b> The output is the p-values for SPU(gamma) and aSPU and the p-values are based on asymptotics based method.</p>
<h2>Parametric bootstrap version of HDGLM</h2>
<p>To facilitate researchers compare our methods with Chen's method (Guo et al. 2016), we provided the parametric bootstrap version of HDGLM, which can be performed by the following single line.</p>
<div class="codeblock">
<div class="blockcontent"><pre>
HDGLM_perm(Y, X, cov = cov)
</pre></div></div>
<h2>FAQ</h2>
<p><b>GLMaSPU package looks similar to aSPU package. What's the difference between them?</b></p>
<p>The main difference is that in GLMaSPU package, we develop the asymptotic null distribution and calculate the p-values based on the asymptotic null distribtuion. For aSPU package, the parametric bootstrap or other resampling methods are used to calculate the p-values. If you want to learn more about applying aSPU to genome-wide association studies, you can read our other works, such as Pan et al. 2014.</p>
<h2>Acknowledgements</h2>
<p>This research was supported by National Institutes of Health (NIH) grants R01GM113250, R01HL105397, and R01HL116720 and by the Minnesota Supercomputing Institute. CW is supported by a University of Minnesota Doctoral Dissertation Fellowship.</p>
<h2>References</h2>
<ul>
<li><p>Guo, B. and S. X. Chen (2016). Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(5), 1079-1102.</p>
</li>
<li><p>Pan, W., J. Kim, Y. Zhang, X. Shen, and P. Wei (2014). A powerful and adaptive association test for rare variants. Genetics 197 (4), 1081–1095.</p>
</li>
</ul>
<h2>License</h2>
<p>Maintainer: <a href="wuchong.org" target=“blank”>Chong Wu</a> ([email protected])</p>
<p><a href="http://opensource.org/licenses/MIT" target=“blank”>MIT</a></p>
<p>Copyright (c) 2013-present, Chong Wu ([email protected]), Gongjun Xu ([email protected]) & Wei Pan([email protected]).</p>
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