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<h1>DEGSet</h1>
<div class="hidden name"><code>DEGSet.Rd</code></div>
</div>
<div class="ref-description">
<p>S4 class to store data from differentially expression analysis.
It should be compatible with different package and stores the information
in a way the methods will work with all of them.</p>
</div>
<pre class="usage"><span class='fu'>DEGSet</span>(<span class='no'>resList</span>, <span class='no'>default</span>)
<span class='fu'>DEGSet</span>(<span class='no'>resList</span>, <span class='no'>default</span>)
<span class='fu'>as.DEGSet</span>(<span class='no'>object</span>, <span class='no'>...</span>)
<span class='co'># S4 method for TopTags</span>
<span class='fu'>as.DEGSet</span>(<span class='no'>object</span>, <span class='kw'>default</span> <span class='kw'>=</span> <span class='st'>"raw"</span>, <span class='kw'>extras</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)
<span class='co'># S4 method for data.frame</span>
<span class='fu'>as.DEGSet</span>(<span class='no'>object</span>, <span class='no'>contrast</span>, <span class='kw'>default</span> <span class='kw'>=</span> <span class='st'>"raw"</span>,
<span class='kw'>extras</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)
<span class='co'># S4 method for DESeqResults</span>
<span class='fu'>as.DEGSet</span>(<span class='no'>object</span>, <span class='kw'>default</span> <span class='kw'>=</span> <span class='st'>"shrunken"</span>,
<span class='kw'>extras</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>resList</th>
<td><p>List with results as elements containing log2FoldChange,
pvalues and padj as column. Rownames should be feature names. Elements
should have names.</p></td>
</tr>
<tr>
<th>default</th>
<td><p>The name of the element to use by default.</p></td>
</tr>
<tr>
<th>object</th>
<td><p>Different objects to be transformed to DEGSet when using <code>as.DEGSet</code>.</p></td>
</tr>
<tr>
<th>...</th>
<td><p>Optional parameters of the generic.</p></td>
</tr>
<tr>
<th>extras</th>
<td><p>List of extra tables related to the same comparison when using <code>as.DEGSet</code>.</p></td>
</tr>
<tr>
<th>contrast</th>
<td><p>To name the comparison when using <code>as.DEGSet</code>.</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>For now supporting only <code><a href='https://rdrr.io/pkg/DESeq2/man/results.html'>DESeq2::results()</a></code> output.
Use constructor <code><a href='degComps.html'>degComps()</a></code> to create the object.</p>
<p>The list will contain one element for each comparison done.
Each element has the following structure:</p><ul>
<li><p>DEG table</p></li>
<li><p>Optional table with shrunk Fold Change when it has been done.</p></li>
</ul>
<p>To access the raw table use <code><a href='deg.html'>deg(dgs, "raw")</a></code>, to access the
shrunken table use <code><a href='deg.html'>deg(dgs, "shrunken")</a></code> or just <code><a href='deg.html'>deg(dgs)</a></code>.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>DESeq2</span>)</div><div class='output co'>#> <span class='message'>Loading required package: S4Vectors</span></div><div class='output co'>#> <span class='message'>Loading required package: stats4</span></div><div class='output co'>#> <span class='message'>Loading required package: BiocGenerics</span></div><div class='output co'>#> <span class='message'>Loading required package: parallel</span></div><div class='output co'>#> <span class='message'></span>
#> <span class='message'>Attaching package: ‘BiocGenerics’</span></div><div class='output co'>#> <span class='message'>The following objects are masked from ‘package:parallel’:</span>
#> <span class='message'></span>
#> <span class='message'> clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,</span>
#> <span class='message'> clusterExport, clusterMap, parApply, parCapply, parLapply,</span>
#> <span class='message'> parLapplyLB, parRapply, parSapply, parSapplyLB</span></div><div class='output co'>#> <span class='message'>The following objects are masked from ‘package:stats’:</span>
#> <span class='message'></span>
#> <span class='message'> IQR, mad, sd, var, xtabs</span></div><div class='output co'>#> <span class='message'>The following objects are masked from ‘package:base’:</span>
#> <span class='message'></span>
#> <span class='message'> anyDuplicated, append, as.data.frame, basename, cbind, colnames,</span>
#> <span class='message'> dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,</span>
#> <span class='message'> grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,</span>
#> <span class='message'> order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,</span>
#> <span class='message'> rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,</span>
#> <span class='message'> union, unique, unsplit, which, which.max, which.min</span></div><div class='output co'>#> <span class='message'></span>
#> <span class='message'>Attaching package: ‘S4Vectors’</span></div><div class='output co'>#> <span class='message'>The following object is masked from ‘package:base’:</span>
#> <span class='message'></span>
#> <span class='message'> expand.grid</span></div><div class='output co'>#> <span class='message'>Loading required package: IRanges</span></div><div class='output co'>#> <span class='message'>Loading required package: GenomicRanges</span></div><div class='output co'>#> <span class='message'>Loading required package: GenomeInfoDb</span></div><div class='output co'>#> <span class='message'>Loading required package: SummarizedExperiment</span></div><div class='output co'>#> <span class='message'>Loading required package: Biobase</span></div><div class='output co'>#> <span class='message'>Welcome to Bioconductor</span>
#> <span class='message'></span>
#> <span class='message'> Vignettes contain introductory material; view with</span>
#> <span class='message'> 'browseVignettes()'. To cite Bioconductor, see</span>
#> <span class='message'> 'citation("Biobase")', and for packages 'citation("pkgname")'.</span></div><div class='output co'>#> <span class='message'>Loading required package: DelayedArray</span></div><div class='output co'>#> <span class='message'>Loading required package: matrixStats</span></div><div class='output co'>#> <span class='message'></span>
#> <span class='message'>Attaching package: ‘matrixStats’</span></div><div class='output co'>#> <span class='message'>The following objects are masked from ‘package:Biobase’:</span>
#> <span class='message'></span>
#> <span class='message'> anyMissing, rowMedians</span></div><div class='output co'>#> <span class='message'>Loading required package: BiocParallel</span></div><div class='output co'>#> <span class='message'></span>
#> <span class='message'>Attaching package: ‘DelayedArray’</span></div><div class='output co'>#> <span class='message'>The following objects are masked from ‘package:matrixStats’:</span>
#> <span class='message'></span>
#> <span class='message'> colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges</span></div><div class='output co'>#> <span class='message'>The following objects are masked from ‘package:base’:</span>
#> <span class='message'></span>
#> <span class='message'> aperm, apply, rowsum</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>edgeR</span>)</div><div class='output co'>#> <span class='message'>Loading required package: limma</span></div><div class='output co'>#> <span class='message'></span>
#> <span class='message'>Attaching package: ‘limma’</span></div><div class='output co'>#> <span class='message'>The following object is masked from ‘package:DESeq2’:</span>
#> <span class='message'></span>
#> <span class='message'> plotMA</span></div><div class='output co'>#> <span class='message'>The following object is masked from ‘package:BiocGenerics’:</span>
#> <span class='message'></span>
#> <span class='message'> plotMA</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>limma</span>)
<span class='no'>dds</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/DESeq2/man/makeExampleDESeqDataSet.html'>makeExampleDESeqDataSet</a></span>(<span class='kw'>betaSD</span> <span class='kw'>=</span> <span class='fl'>1</span>)
<span class='fu'>colData</span>(<span class='no'>dds</span>)<span class='kw'>[[</span><span class='st'>"treatment"</span>]] <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/sample.html'>sample</a></span>(<span class='fu'>colData</span>(<span class='no'>dds</span>)<span class='kw'>[[</span><span class='st'>"condition"</span>]], <span class='fl'>12</span>)
<span class='fu'><a href='https://rdrr.io/pkg/DESeq2/man/design.html'>design</a></span>(<span class='no'>dds</span>) <span class='kw'><-</span> ~ <span class='no'>condition</span> + <span class='no'>treatment</span>
<span class='no'>dds</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/DESeq2/man/DESeq.html'>DESeq</a></span>(<span class='no'>dds</span>)</div><div class='output co'>#> <span class='message'>estimating size factors</span></div><div class='output co'>#> <span class='message'>estimating dispersions</span></div><div class='output co'>#> <span class='message'>gene-wise dispersion estimates</span></div><div class='output co'>#> <span class='message'>mean-dispersion relationship</span></div><div class='output co'>#> <span class='message'>final dispersion estimates</span></div><div class='output co'>#> <span class='message'>fitting model and testing</span></div><div class='input'><span class='no'>res</span> <span class='kw'><-</span> <span class='fu'><a href='degComps.html'>degComps</a></span>(<span class='no'>dds</span>, <span class='kw'>combs</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"condition"</span>))</div><div class='output co'>#> <span class='message'>Doing 1 element(s).</span></div><div class='output co'>#> <span class='message'>Doing results() for each element.</span></div><div class='output co'>#> <span class='message'>Doing lcfSrink() for each element.</span></div><div class='output co'>#> <span class='message'>using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).</span>
#> <span class='message'></span>
#> <span class='message'>Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.</span>
#> <span class='message'>See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.</span>
#> <span class='message'>Reference: https://doi.org/10.1093/bioinformatics/bty895</span></div><div class='input'><span class='fu'><a href='deg.html'>deg</a></span>(<span class='no'>res</span>)</div><div class='output co'>#> log2 fold change (MAP): condition B vs A
#> Wald test p-value: condition B vs A
#> DataFrame with 1000 rows and 6 columns
#> baseMean log2FoldChange lfcSE
#> <numeric> <numeric> <numeric>
#> gene202 595.738080502665 2.84842810352625 0.266347713336691
#> gene710 117.423055944441 2.27457157814062 0.269412722270367
#> gene365 62.4434899710059 2.58422310739862 0.312590047837112
#> gene92 220.780204589055 2.34371133129609 0.293935357285173
#> gene364 504.522116500683 2.02714357936619 0.261124625693044
#> ... ... ... ...
#> gene817 1.01863690444656 0.449433023322897 0.445454565205662
#> gene843 0.337116176782868 -0.213542721980008 0.335419175582519
#> gene896 0.641791262357825 -0.193113193147619 0.453528848117175
#> gene929 0.400097105619871 0.148754379342596 0.336592062169539
#> gene986 1.03912504596143 -0.08078789533133 0.501091699324092
#> stat pvalue padj
#> <numeric> <numeric> <numeric>
#> gene202 10.4242414625226 1.92186681865069e-25 1.84307027908601e-22
#> gene710 8.19578166036082 2.48969903778264e-16 1.19381068861678e-13
#> gene365 8.02865482517521 9.85471916802617e-16 3.15022522737903e-13
#> gene92 7.7626720152767 8.31584691463868e-15 1.99372429778462e-12
#> gene364 7.51756890822073 5.58041485417993e-14 1.07032356903171e-11
#> ... ... ... ...
#> gene817 1.05361747680227 0.292058084434074 NA
#> gene843 -0.406932816121088 0.684057332932902 NA
#> gene896 -0.612499137852159 0.5402075682426 NA
#> gene929 0.414219583248068 0.678713301164469 NA
#> gene986 0.0436335995171099 0.965196468651691 NA</div><div class='output co'>#> <numeric></div><div class='input'><span class='fu'><a href='deg.html'>deg</a></span>(<span class='no'>res</span>, <span class='kw'>tidy</span> <span class='kw'>=</span> <span class='st'>"tibble"</span>)</div><div class='output co'>#> <span style='color: #555555;'># A tibble: 1,000 x 7</span><span>
#> gene baseMean log2FoldChange lfcSE stat pvalue padj
#> </span><span style='color: #555555;font-style: italic;'><chr></span><span> </span><span style='color: #555555;font-style: italic;'><dbl></span><span> </span><span style='color: #555555;font-style: italic;'><dbl></span><span> </span><span style='color: #555555;font-style: italic;'><dbl></span><span> </span><span style='color: #555555;font-style: italic;'><dbl></span><span> </span><span style='color: #555555;font-style: italic;'><dbl></span><span> </span><span style='color: #555555;font-style: italic;'><dbl></span><span>
#> </span><span style='color: #555555;'> 1</span><span> gene202 596. 2.85 0.266 10.4 1.92</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-25</span><span> 1.84</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-22</span><span>
#> </span><span style='color: #555555;'> 2</span><span> gene710 117. 2.27 0.269 8.20 2.49</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-16</span><span> 1.19</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-13</span><span>
#> </span><span style='color: #555555;'> 3</span><span> gene365 62.4 2.58 0.313 8.03 9.85</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-16</span><span> 3.15</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-13</span><span>
#> </span><span style='color: #555555;'> 4</span><span> gene92 221. 2.34 0.294 7.76 8.32</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-15</span><span> 1.99</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-12</span><span>
#> </span><span style='color: #555555;'> 5</span><span> gene364 505. 2.03 0.261 7.52 5.58</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-14</span><span> 1.07</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-11</span><span>
#> </span><span style='color: #555555;'> 6</span><span> gene968 69.4 2.62 0.345 7.11 1.17</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-12</span><span> 1.87</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-10</span><span>
#> </span><span style='color: #555555;'> 7</span><span> gene836 49.2 2.41 0.335 7.05 1.78</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-12</span><span> 2.43</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-10</span><span>
#> </span><span style='color: #555555;'> 8</span><span> gene77 32.5 -</span><span style='color: #BB0000;'>2.31</span><span> 0.334 -</span><span style='color: #BB0000;'>6.75</span><span> 1.50</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-11</span><span> 1.80</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>- 9</span><span>
#> </span><span style='color: #555555;'> 9</span><span> gene342 484. 1.45 0.225 6.50 7.90</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-11</span><span> 8.42</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>- 9</span><span>
#> </span><span style='color: #555555;'>10</span><span> gene880 27.5 2.80 0.402 6.48 9.01</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>-11</span><span> 8.64</span><span style='color: #555555;'>e</span><span style='color: #BB0000;'>- 9</span><span>
#> </span><span style='color: #555555;'># … with 990 more rows</span><span></div><div class='input'><span class='co'># From edgeR</span>
<span class='no'>dge</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/edgeR/man/DGEList.html'>DGEList</a></span>(<span class='kw'>counts</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/pkg/DESeq2/man/counts.html'>counts</a></span>(<span class='no'>dds</span>), <span class='kw'>group</span><span class='kw'>=</span><span class='fu'>colData</span>(<span class='no'>dds</span>)<span class='kw'>[[</span><span class='st'>"treatment"</span>]])
<span class='no'>dge</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/edgeR/man/estimateCommonDisp.html'>estimateCommonDisp</a></span>(<span class='no'>dge</span>)
<span class='no'>res</span> <span class='kw'><-</span> <span class='fu'>as.DEGSet</span>(<span class='fu'><a href='https://rdrr.io/pkg/edgeR/man/topTags.html'>topTags</a></span>(<span class='fu'><a href='https://rdrr.io/pkg/edgeR/man/exactTest.html'>exactTest</a></span>(<span class='no'>dge</span>)))
<span class='co'># From limma</span>
<span class='no'>v</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/limma/man/voom.html'>voom</a></span>(<span class='fu'><a href='https://rdrr.io/pkg/DESeq2/man/counts.html'>counts</a></span>(<span class='no'>dds</span>), <span class='fu'><a href='https://rdrr.io/r/stats/model.matrix.html'>model.matrix</a></span>(~<span class='no'>treatment</span>, <span class='fu'>colData</span>(<span class='no'>dds</span>)), <span class='kw'>plot</span><span class='kw'>=</span><span class='fl'>FALSE</span>)
<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/limma/man/lmFit.html'>lmFit</a></span>(<span class='no'>v</span>)
<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/limma/man/ebayes.html'>eBayes</a></span>(<span class='no'>fit</span>, <span class='kw'>robust</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
<span class='no'>res</span> <span class='kw'><-</span> <span class='fu'>as.DEGSet</span>(<span class='fu'><a href='https://rdrr.io/pkg/limma/man/toptable.html'>topTable</a></span>(<span class='no'>fit</span>, <span class='kw'>n</span> <span class='kw'>=</span> <span class='st'>"Inf"</span>), <span class='st'>"A_vs_B"</span>)</div><div class='output co'>#> <span class='message'>Removing intercept from test coefficients</span></div></span></pre>
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<p>Lorena Pantano</p>
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