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docs/introduction.html

@@ -535,8 +535,8 @@ <h2><span class="header-section-number">1.3</span> Trends in Metabolomics<a href
 </div>
 <div id="workflow-1" class="section level2 hasAnchor" number="1.4">
 <h2><span class="header-section-number">1.4</span> Workflow<a href="introduction.html#workflow-1" class="anchor-section" aria-label="Anchor link to header"></a></h2>
-<div id="htmlwidget-08e8d605cbc9ba6711fa" style="width:300px;height:480px;" class="grViz html-widget"></div>
-<script type="application/json" data-for="htmlwidget-08e8d605cbc9ba6711fa">{"x":{"diagram":"digraph workflow {\nnode [shape = box]\nA [label = \"raw data\"]\nB [label = \"open source format\"]\nC [label = \"DoE folder\"]\nD [label = \"peaks list\"]\nE [label = \"retention time correction\"]\nF [label = \"peaks grouping\"]\nG [label = \"peaks filling\"]\nH [label = \"raw peaks\"]\nI [label = \"data visulization\"]\nJ [label = \"batch effects correction\"]\nK [label = \"corrected peaks\"]\nL [label = \"annotation\"]\nM [label = \"metabolomics pathway analysis\"]\nN [label = \"omics analysis\"]\nO [label = \"biomarkers discovery/diagnoise\"]\n\nA -> B -> C -> D -> E -> F -> G -> H\nH -> I\nI -> J\nH -> J -> K -> L\nL -> M -> N\nL -> O\n                  }","config":{"engine":"dot","options":null}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-aae345f6b281b4afe337" style="width:300px;height:480px;" class="grViz html-widget"></div>
+<script type="application/json" data-for="htmlwidget-aae345f6b281b4afe337">{"x":{"diagram":"digraph workflow {\nnode [shape = box]\nA [label = \"raw data\"]\nB [label = \"open source format\"]\nC [label = \"DoE folder\"]\nD [label = \"peaks list\"]\nE [label = \"retention time correction\"]\nF [label = \"peaks grouping\"]\nG [label = \"peaks filling\"]\nH [label = \"raw peaks\"]\nI [label = \"data visulization\"]\nJ [label = \"batch effects correction\"]\nK [label = \"corrected peaks\"]\nL [label = \"annotation\"]\nM [label = \"metabolomics pathway analysis\"]\nN [label = \"omics analysis\"]\nO [label = \"biomarkers discovery/diagnoise\"]\n\nA -> B -> C -> D -> E -> F -> G -> H\nH -> I\nI -> J\nH -> J -> K -> L\nL -> M -> N\nL -> O\n                  }","config":{"engine":"dot","options":null}},"evals":[],"jsHooks":[]}</script>
 
 </div>
 </div>

docs/raw-data-pretreatment.html

@@ -537,17 +537,17 @@ <h3><span class="header-section-number">6.7.1</span> Non-detects<a href="raw-dat
 ## 
 ## Coefficients:
 ##             Estimate Std. Error z value Pr(&gt;|z|)    
-## (Intercept)   1.0000     0.4325   2.312   0.0208 *  
+## (Intercept)   1.0000     0.4368   2.289   0.0221 *  
 ## x            10.0000     0.3162  31.623   &lt;2e-16 ***
-## Log(scale)    2.1541     0.0000     Inf   &lt;2e-16 ***
+## Log(scale)    2.1518     0.0000     Inf   &lt;2e-16 ***
 ## ---
 ## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 ## 
-## Scale: 8.62 
+## Scale: 8.601 
 ## 
 ## Gaussian distribution
 ## Number of Newton-Raphson Iterations: 1 
-## Log-likelihood: -3073 on 3 Df
+## Log-likelihood: -3071 on 3 Df
 ## Wald-statistic:  1000 on 1 Df, p-value: &lt; 2.22e-16</code></pre>
 <p>According to Ronald Hites’s simulation<span class="citation">(<a href="#ref-hites2019">Hites 2019</a>)</span>, measurements below the LOD (even missing measurements) with the LOD/2 or with the <span class="math inline">\(LOD/\sqrt2\)</span> causes little bias and “Any time you have a % non-detected &gt;20%, for whatever reason, it is unlikely that the data set can give useful results.”</p>
 <p>Another study find random forest could be the best imputation method for missing at random (MAR), and missing completely at random (MCAR) data. Quantile regression imputation of left-censored data is the best imputation methods for left-censored missing not at random data <span class="citation">(<a href="#ref-wei2018">Wei et al. 2018</a>)</span>.</p>

docs/workflow-2.html

@@ -418,8 +418,8 @@ <h1><span class="header-section-number">Chapter 5</span> Workflow<a href="workfl
 <span id="cb7-13"><a href="workflow-2.html#cb7-13" tabindex="-1"></a><span class="st">B --&gt; E</span></span>
 <span id="cb7-14"><a href="workflow-2.html#cb7-14" tabindex="-1"></a><span class="st">C --&gt; H</span></span>
 <span id="cb7-15"><a href="workflow-2.html#cb7-15" tabindex="-1"></a><span class="st">&quot;</span>)</span></code></pre></div>
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-<script type="application/json" data-for="htmlwidget-f4255361ed1fe92a147a">{"x":{"diagram":"\nflowchart TB\nI(peak-picking) --> C\nC(visulization) --> D(normalization/batch correction)\nD --> A(annotation/identification)\nA --> H(statistical analysis)\nC --> A --> B(omics analysis)\nD --> H\nB --> H\nH --> E(experimental validation)\nA --> E\nH --> A\nB --> E\nC --> H\n"},"evals":[],"jsHooks":[]}</script>
+<div class="DiagrammeR html-widget html-fill-item" id="htmlwidget-49e7350f7b6ff79f5ad5" style="width:672px;height:480px;"></div>
+<script type="application/json" data-for="htmlwidget-49e7350f7b6ff79f5ad5">{"x":{"diagram":"\nflowchart TB\nI(peak-picking) --> C\nC(visulization) --> D(normalization/batch correction)\nD --> A(annotation/identification)\nA --> H(statistical analysis)\nC --> A --> B(omics analysis)\nD --> H\nB --> H\nH --> E(experimental validation)\nA --> E\nH --> A\nB --> E\nC --> H\n"},"evals":[],"jsHooks":[]}</script>
 <div id="platform-for-metabolomics-data-analysis" class="section level2 hasAnchor" number="5.1">
 <h2><span class="header-section-number">5.1</span> Platform for metabolomics data analysis<a href="workflow-2.html#platform-for-metabolomics-data-analysis" class="anchor-section" aria-label="Anchor link to header"></a></h2>
 <p>Here is a list for related open source <a href="http://strimmerlab.org/notes/mass-spectrometry.html">projects</a></p>