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case_studies/2014-02-mack/index.html

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case_studies/2014-10-santos/index.html

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case_studies/2016-04-hill/index.html

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case_studies/2017-07-miller/index.html

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presentations/introduction-paxtoolsr-metabolomics-29-06-2016/index.html

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presentations/pathway-commons-aacr-2018/index.html

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presentations/pathway-commons-ascb-2017/index.html

@@ -157,8 +157,8 @@ <h1>How can you interpret gene lists from -omics experiments?</h1>
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presentations/pathway-commons-ashg-2017/index.html

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presentations/pathway-commons-ismb-2017/index.html

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presentations/tour-search-app-21-08-2017/index.html

@@ -150,8 +150,8 @@ <h1>Tour - Pathway Search</h1>
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primers/data_analysis/gsea/index.html

@@ -545,13 +545,41 @@ <h4 id="normalized-enrichment-score-accounting-for-gene-set-size">Normalized enr
 
 <p>Given the observed and null normalized enrichment scores, the FDR can be calculated using an approach similar to that described in the previous section (equation \eqref{eq:13}).</p>
 
-
+<hr />
+
+<h2 id="references">References</h2>
+<ul>
+  <li>Ackermann M &amp; Strimmer K.A general modular framework for gene set enrichment analysis. BMC Bioinformatics. 2009 Feb 3;10:47. doi: 10.1186/1471-2105-10-47.</li>
+  <li>Barry WT et al. Significance analysis of functional categories in gene expression studies: a
+structured permutation approach. Bioinformatics. 2005 May 1;21(9):1943-9. doi: 10.1093/bioinformatics/bti260.
+Epub 2005 Jan 12.</li>
+  <li>Damian D &amp; Gorfine M. Statistical concerns about the GSEA procedure. Nat Genet. 2004 Jul;36(7):663; author reply 663. doi: 10.1038/ng0704-663a.</li>
+  <li>Irizarry RA et al. Gene set enrichment analysis made simple. Stat Methods Med Res. 2009 Dec;18(6):565-75. doi: 10.1177/0962280209351908.</li>
+  <li>Khatri P &amp; Drăghici S. Ontological analysis of gene expression data: current tools, limitations, and
+open problems. Bioinformatics. 2005 Sep 15;21(18):3587-95. doi:
+10.1093/bioinformatics/bti565.  Epub 2005 Jun 30.</li>
+  <li>Khatri P et al. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):e1002375. doi: 10.1371/journal.pcbi.1002375. Epub
+2012 Feb 23.</li>
+  <li>Mootha VK et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are
+coordinately downregulated in human diabetes. Nat Genet. 2003 Jul;34(3):267-73. doi: 10.1038/ng1180.</li>
+  <li>Noble WS. How does multiple testing correction work?Nat Biotechnol. 2009 Dec;27(12):1135-7. doi: 10.1038/nbt1209-1135.</li>
+  <li>Subramanian A et al. Gene set enrichment analysis: a knowledge-based approach for interpreting
+genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50. doi:
+10.1073/pnas.0506580102. Epub 2005 Sep 30.</li>
+  <li>Tamayo P et al. The limitations of simple gene set enrichment analysis assuming gene
+independence. Stat Methods Med Res. 2016 Feb;25(1):472-87. doi: 10.1177/0962280212460441.
+Epub  2012 Oct 14.</li>
+  <li>Creixell P et al. Pathway and network analysis of cancer genomes. Nat Methods. 2015 Jul;12(7):615-621. doi: 10.1038/nmeth.3440.</li>
+</ul>
+
+
+  <!-- 
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   <h2>References</h2>
   
   <div class="reference_group" data-reflist="[19192285 15647293 15226741 20048385 15994189 22383865 12808457 20010596 16199517 23070592 26125594]"></div>
 
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primers/data_analysis/rna_sequencing_analysis/index.html

@@ -962,13 +962,42 @@ <h3 id="estimating-per-gene-tag-wise-dispersions">Estimating per-gene (‘tag-wi
 
 <p>In the end, an estimate of negative binomial parameters enables us to calculate the exact probabilities of RNA-seq mapped read counts and derive a <script type="math/tex">P</script> value for each gene, as discussed in the previous section. Dispersion plays an important role in hypothesis tests for DEGs. Underestimates of <script type="math/tex">\phi</script> lead to lower estimates of variance relative to the mean, which may generate false evidence that a gene is differentially expressed and <em>vice versa</em>.</p>
 
-
+<hr />
+
+<h2 id="references">References</h2>
+<ul>
+  <li>Bullard JH et al. Evaluation of statistical methods for normalization and differential expression
+in mRNA-Seq experiments. BMC Bioinformatics. 2010 Feb 18;11:94. doi: 10.1186/1471-2105-11-94.</li>
+  <li>Conesa A et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016 Jan 26;17:13. doi: 10.1186/s13059-016-0881-8.</li>
+  <li>Kim JB et al. Polony multiplex analysis of gene expression (PMAGE) in mouse hypertrophic
+cardiomyopathy. Science. 2007 Jun 8;316(5830):1481-4. doi: 10.1126/science.1137325.</li>
+  <li>Li S et al. Detecting and correcting systematic variation in large-scale RNA sequencing
+data. Nat Biotechnol. 2014 Sep;32(9):888-95. doi: 10.1038/nbt.3000. Epub 2014 Aug
+24.</li>
+  <li>Marioni JC et al. RNA-seq: an assessment of technical reproducibility and comparison with gene
+expression arrays. Genome Res. 2008 Sep;18(9):1509-17. doi: 10.1101/gr.079558.108. Epub 2008 Jun
+11.</li>
+  <li>Mortazavi A et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008 Jul;5(7):621-8. doi: 10.1038/nmeth.1226. Epub 2008 May 30.</li>
+  <li>Oshlack A et al. From RNA-seq reads to differential expression results. Genome Biol. 2010;11(12):220. doi: 10.1186/gb-2010-11-12-220. Epub 2010 Dec
+22.</li>
+  <li>Robinson MD &amp; Smyth GK. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics. 2007 Nov 1;23(21):2881-7. doi: 10.1093/bioinformatics/btm453.
+Epub 2007 Sep 19.</li>
+  <li>Robinson MD &amp; Smyth GK. Small-sample estimation of negative binomial dispersion, with applications to
+SAGE data. Biostatistics. 2008 Apr;9(2):321-32. doi: 10.1093/biostatistics/kxm030. Epub
+2007 Aug 29.</li>
+  <li>Robinson MD &amp; Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq
+data. Genome Biol. 2010;11(3):R25. doi: 10.1186/gb-2010-11-3-r25. Epub 2010 Mar 2.</li>
+  <li>Wang Z et al. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/nrg2484.</li>
+</ul>
+
+
+  <!-- 
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primers/data_analysis/rnai_gene_enrichment_ranking/index.html

@@ -215,13 +215,13 @@ <h2 id="iii-rnai-gene-enrichment-ranking-riger"><a href="#rnaiGeneEnrichmentRank
 <p>Output: A rank ordered list of genes, based on the depletion or enrichment of the shRNAs that target them.</p>
 
 
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primers/statistics/definitions/index.html

@@ -188,8 +188,8 @@ <h2 id="distribution-function"><a href="#distributionFunction" name="distributio
 
 <p>(3) <script type="math/tex">\lim_{x\to-\infty} F(x)=0 \text{ and } \lim_{x\to+\infty} F(x)=1</script></p>
 
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primers/statistics/distributions/index.html

@@ -206,7 +206,10 @@ <h4 id="a-deck-of-cards">A Deck of cards</h4>
   </span><span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">"identity"</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="o">=</span><span class="w"> </span><span class="s2">"#2c3e50"</span><span class="p">,</span><span class="w"> </span><span class="n">colour</span><span class="o">=</span><span class="s2">"black"</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w">
   </span><span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"Number of Aces"</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"Probability"</span><span class="p">)</span></code></pre></figure>
 
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+<img src="/guide/public/R/primers/statistics/distributions/unnamed-chunk-1-1.png" alt="plot of chunk unnamed-chunk-1" width="500" />
+<p class="caption">plot of chunk unnamed-chunk-1</p>
+</div>
 
 <h2 id="ii-binomial-distribution"><a href="#binomial" name="binomial">II. Binomial distribution</a></h2>
 
@@ -305,7 +308,10 @@ <h4 id="a-fair-die">A fair die</h4>
   </span><span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">"identity"</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="o">=</span><span class="w"> </span><span class="s2">"#2c3e50"</span><span class="p">,</span><span class="w"> </span><span class="n">colour</span><span class="o">=</span><span class="s2">"black"</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w">
   </span><span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"Number of Sixes"</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"Probability"</span><span class="p">)</span></code></pre></figure>
 
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+<img src="/guide/public/R/primers/statistics/distributions/unnamed-chunk-2-1.png" alt="plot of chunk unnamed-chunk-2" width="500" />
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 <p>Note that as the dispersion parameter <script type="math/tex">\phi</script> approaches zero the negative binomial variance approaches the mean. Thus the dispersion parameter accounts for the extra variability over and above that expected with a Poisson.</p>
 
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primers/statistics/fishers_exact_test/index.html

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   <li>Rivals et al. Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics v23(4) pp. 401-407, 2006.</li>
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primers/statistics/index.html

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+          <p class="list-group-item-title">Distributions</p>
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-          <p class="list-group-item-title">Definitions</p>
+          <p class="list-group-item-title">Multiple Testing</p>
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