Merge pull request #20 from BergmannLab/v0.0.3

doc fix

Author: Dan-RAI

docs/_sources/index.rst.txt

@@ -6,7 +6,7 @@
 Welcome to PascalX's documentation!
 ===================================
 
-PascalX is a python3 library (`source <https://github.com/BergmannLab/PascalX>`_) for high precision gene and pathway scoring for GWAS summary statistics. Aggregation of SNP p-values to gene and pathway scores follows the `Pascal <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004714>`_ methodology, which is based on :math:`\chi^2` statistics. The cummulative distribution function of the weighted :math:`\chi^2` distribution is calculated approximately or exactly via a multi-precision C++ implementation of Ruben's and Davies algorithm. This allows to apply the Pascal methodology to modern UK BioBank scale GWAS. In addition, PascalX offers a novel coherence test between two different GWAS on the level of genes, based on the product-normal distribution, as described `here <https://doi.org/10.1101/2021.05.16.21257289>`_.    
+PascalX is a python3 library (`source <https://github.com/BergmannLab/PascalX>`_) for high precision gene and pathway scoring for GWAS summary statistics. Aggregation of SNP p-values to gene and pathway scores follows the `Pascal <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004714>`_ methodology, which is based on :math:`\chi^2` statistics. The cummulative distribution function of the weighted :math:`\chi^2` distribution can be calculated approximately or exactly via a multi-precision C++ implementation of Ruben's and Davies algorithm. This allows to apply the Pascal methodology to modern UK BioBank scale GWAS. In addition, PascalX offers a novel coherence test between two different GWAS on the level of genes, based on the product-normal distribution, as described `here <https://doi.org/10.1101/2021.05.16.21257289>`_.    
 
 **Highlights:**
 

docs/index.html

@@ -164,7 +164,7 @@
 
   <div class="section" id="welcome-to-pascalx-s-documentation">
 <h1>Welcome to PascalX’s documentation!<a class="headerlink" href="#welcome-to-pascalx-s-documentation" title="Permalink to this headline">¶</a></h1>
-<p>PascalX is a python3 library (<a class="reference external" href="https://github.com/BergmannLab/PascalX">source</a>) for high precision gene and pathway scoring for GWAS summary statistics. Aggregation of SNP p-values to gene and pathway scores follows the <a class="reference external" href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004714">Pascal</a> methodology, which is based on <span class="math notranslate nohighlight">\(\chi^2\)</span> statistics. The cummulative distribution function of the weighted <span class="math notranslate nohighlight">\(\chi^2\)</span> distribution is calculated approximately or exactly via a multi-precision C++ implementation of Ruben’s and Davies algorithm. This allows to apply the Pascal methodology to modern UK BioBank scale GWAS. In addition, PascalX offers a novel coherence test between two different GWAS on the level of genes, based on the product-normal distribution, as described <a class="reference external" href="https://doi.org/10.1101/2021.05.16.21257289">here</a>.</p>
+<p>PascalX is a python3 library (<a class="reference external" href="https://github.com/BergmannLab/PascalX">source</a>) for high precision gene and pathway scoring for GWAS summary statistics. Aggregation of SNP p-values to gene and pathway scores follows the <a class="reference external" href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004714">Pascal</a> methodology, which is based on <span class="math notranslate nohighlight">\(\chi^2\)</span> statistics. The cummulative distribution function of the weighted <span class="math notranslate nohighlight">\(\chi^2\)</span> distribution can be calculated approximately or exactly via a multi-precision C++ implementation of Ruben’s and Davies algorithm. This allows to apply the Pascal methodology to modern UK BioBank scale GWAS. In addition, PascalX offers a novel coherence test between two different GWAS on the level of genes, based on the product-normal distribution, as described <a class="reference external" href="https://doi.org/10.1101/2021.05.16.21257289">here</a>.</p>
 <p><strong>Highlights:</strong></p>
 <blockquote>
 <div><ul class="simple">