<p>A design matrix is a matrix of values of the grouping variable.</p>
<p>ANOVA needs such a matrix to know which samples belong to which group. Since <code>limma</code> performs an ANOVA it needs this design matrix.</p>
<ol>
<li>You can create it using the <code>model.matrix()</code> method.</li>
<li>Afterwards you can tell <code>limma</code> which groups you want to compare.</li>
</ol>
<p>For point 2 you define a contrast matrix defining the comparisons of interest by using the <code>makeContrasts()</code> method.</p>
<p>It is well explained in this <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf" rel="nofollow noreferrer">manual's</a> section 9.2</p>

CC BY-SA 4.0
2022-08-13T13:19:36.383Z
2022-08-13T14:38:22.993Z
2022-08-13T14:38:22.993Z
Nitesh Shriwash
<p>The design matrix i have is this I would like to know how to use they way its done in deseq2 where we can use the contrast function to make particular comparison</p>
<p>The code im running to make differential testing is this</p>
<pre><code>tcgaGlm = lmFit(as.matrix(tcgaExpr), design = tcgaDesign) 
tcgaGlm = eBayes(tcgaGlm)
</code></pre>
<p>Now I will show the part of the data frame and the question Im trying to ask</p>
<pre><code>head(tcgaDesign)
             Offset CEBPA DNMT3A FAM5C FLT3_ITD FLT3_TKD IDH1 IDH2 KIT KRAS NPM1 NRAS PHF6 PTPN11 RAD21 RUNX1 SMC1A SMC3 STAG2 TET2 TP53 U2AF1 WT1
TCGA-AB-2803      1     0      0     0        0        0    0    0   0    0    0    0    0      0     0     0     0    0     0    0    0     0   0
TCGA-AB-2805      1     0      0     0        0        0    0    1   0    0    0    0    0      0     0     1     0    0     0    0    0     0   0
TCGA-AB-2806      1     0      0     0        0        0    0    0   0    0    0    0    0      0     0     0     0    0     0    0    0     0   0
TCGA-AB-2807      1     0      0     0        0        0    0    1   0    0    0    0    0      0     0     1     0    0     0    0    0     0   0
TCGA-AB-2808      1     1      0     0        0        0    0    0   0    0    0    1    0      0     0     0     0    0     0    0    0     0   0
TCGA-AB-2810      1     0      0     0        0        0    0    1   0    0    1    0    0      0     0     0     0    0     0    0    0     0   0
               complex  minus5_5q   minus7q  plus8_8q     plus21    t_15_17     t_8_21 inv16_t16_16
TCGA-AB-2803 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 1.00000000 0.00000000   0.00000000
TCGA-AB-2805 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 0.00000000 0.00000000   0.00000000
TCGA-AB-2806 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 0.00000000 1.00000000   0.00000000
TCGA-AB-2807 0.1410256 0.08974359 0.1217949 0.1153846 1.00000000 0.09615385 0.04487179   0.04487179
TCGA-AB-2808 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 0.00000000 0.00000000   0.00000000
TCGA-AB-2810 0.1410256 0.08974359 0.1217949 0.1153846 0.05063291 0.09615385 0.04487179   0.04487179
</code></pre>
<p>Here if I have to make a comparison between CEBPA vs DNMT3A mutation in samples which carries CEBPA mutation and other which carries DNMT3A what or how should I do it.</p>
<p>To begin with I followed this <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf" rel="nofollow noreferrer">manual</a> I m still not sure once you run the model do i need to form a design matrix which comprises of sample which contain mutation in CEBPA and DNMT3A ? or I can go ahead with this experimental design</p>
<pre><code>dput(tcgaDesign)
structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 
1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 
1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 
1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 
0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 
0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 
1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 
1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 
0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 
1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(173L, 31L), .Dimnames = list(
    c(&quot;TCGA-AB-2803&quot;, &quot;TCGA-AB-2805&quot;, &quot;TCGA-AB-2806&quot;, &quot;TCGA-AB-2807&quot;, 
    &quot;TCGA-AB-2808&quot;, &quot;TCGA-AB-2810&quot;, &quot;TCGA-AB-2811&quot;, &quot;TCGA-AB-2812&quot;, 
    &quot;TCGA-AB-2813&quot;, &quot;TCGA-AB-2814&quot;, &quot;TCGA-AB-2815&quot;, &quot;TCGA-AB-2816&quot;, 
    &quot;TCGA-AB-2817&quot;, &quot;TCGA-AB-2818&quot;, &quot;TCGA-AB-2819&quot;, &quot;TCGA-AB-2820&quot;, 
    &quot;TCGA-AB-2821&quot;, &quot;TCGA-AB-2822&quot;, &quot;TCGA-AB-2823&quot;, &quot;TCGA-AB-2824&quot;, 
    &quot;TCGA-AB-2825&quot;, &quot;TCGA-AB-2826&quot;, &quot;TCGA-AB-2828&quot;, &quot;TCGA-AB-2830&quot;, 
    &quot;TCGA-AB-2832&quot;, &quot;TCGA-AB-2833&quot;, &quot;TCGA-AB-2834&quot;, &quot;TCGA-AB-2835&quot;, 
    &quot;TCGA-AB-2836&quot;, &quot;TCGA-AB-2837&quot;, &quot;TCGA-AB-2838&quot;, &quot;TCGA-AB-2839&quot;, 
    &quot;TCGA-AB-2840&quot;, &quot;TCGA-AB-2841&quot;, &quot;TCGA-AB-2842&quot;, &quot;TCGA-AB-2843&quot;, 
    &quot;TCGA-AB-2844&quot;, &quot;TCGA-AB-2845&quot;, &quot;TCGA-AB-2846&quot;, &quot;TCGA-AB-2847&quot;, 
    &quot;TCGA-AB-2848&quot;, &quot;TCGA-AB-2849&quot;, &quot;TCGA-AB-2851&quot;, &quot;TCGA-AB-2853&quot;, 
    &quot;TCGA-AB-2854&quot;, &quot;TCGA-AB-2855&quot;, &quot;TCGA-AB-2856&quot;, &quot;TCGA-AB-2857&quot;, 
    &quot;TCGA-AB-2858&quot;, &quot;TCGA-AB-2859&quot;, &quot;TCGA-AB-2860&quot;, &quot;TCGA-AB-2861&quot;, 
    &quot;TCGA-AB-2862&quot;, &quot;TCGA-AB-2863&quot;, &quot;TCGA-AB-2865&quot;, &quot;TCGA-AB-2866&quot;, 
    &quot;TCGA-AB-2867&quot;, &quot;TCGA-AB-2868&quot;, &quot;TCGA-AB-2869&quot;, &quot;TCGA-AB-2870&quot;, 
    &quot;TCGA-AB-2871&quot;, &quot;TCGA-AB-2872&quot;, &quot;TCGA-AB-2873&quot;, &quot;TCGA-AB-2874&quot;, 
    &quot;TCGA-AB-2875&quot;, &quot;TCGA-AB-2877&quot;, &quot;TCGA-AB-2879&quot;, &quot;TCGA-AB-2880&quot;, 
    &quot;TCGA-AB-2881&quot;, &quot;TCGA-AB-2882&quot;, &quot;TCGA-AB-2884&quot;, &quot;TCGA-AB-2885&quot;, 
    &quot;TCGA-AB-2886&quot;, &quot;TCGA-AB-2887&quot;, &quot;TCGA-AB-2888&quot;, &quot;TCGA-AB-2889&quot;, 
    &quot;TCGA-AB-2890&quot;, &quot;TCGA-AB-2891&quot;, &quot;TCGA-AB-2895&quot;, &quot;TCGA-AB-2896&quot;, 
    &quot;TCGA-AB-2897&quot;, &quot;TCGA-AB-2898&quot;, &quot;TCGA-AB-2899&quot;, &quot;TCGA-AB-2900&quot;, 
    &quot;TCGA-AB-2901&quot;, &quot;TCGA-AB-2903&quot;, &quot;TCGA-AB-2904&quot;, &quot;TCGA-AB-2908&quot;, 
    &quot;TCGA-AB-2909&quot;, &quot;TCGA-AB-2910&quot;, &quot;TCGA-AB-2911&quot;, &quot;TCGA-AB-2912&quot;, 
    &quot;TCGA-AB-2913&quot;, &quot;TCGA-AB-2914&quot;, &quot;TCGA-AB-2915&quot;, &quot;TCGA-AB-2916&quot;, 
    &quot;TCGA-AB-2917&quot;, &quot;TCGA-AB-2918&quot;, &quot;TCGA-AB-2919&quot;, &quot;TCGA-AB-2920&quot;, 
    &quot;TCGA-AB-2921&quot;, &quot;TCGA-AB-2924&quot;, &quot;TCGA-AB-2925&quot;, &quot;TCGA-AB-2927&quot;, 
    &quot;TCGA-AB-2928&quot;, &quot;TCGA-AB-2929&quot;, &quot;TCGA-AB-2930&quot;, &quot;TCGA-AB-2931&quot;, 
    &quot;TCGA-AB-2932&quot;, &quot;TCGA-AB-2933&quot;, &quot;TCGA-AB-2934&quot;, &quot;TCGA-AB-2935&quot;, 
    &quot;TCGA-AB-2936&quot;, &quot;TCGA-AB-2937&quot;, &quot;TCGA-AB-2938&quot;, &quot;TCGA-AB-2939&quot;, 
    &quot;TCGA-AB-2940&quot;, &quot;TCGA-AB-2941&quot;, &quot;TCGA-AB-2942&quot;, &quot;TCGA-AB-2943&quot;, 
    &quot;TCGA-AB-2944&quot;, &quot;TCGA-AB-2946&quot;, &quot;TCGA-AB-2948&quot;, &quot;TCGA-AB-2949&quot;, 
    &quot;TCGA-AB-2950&quot;, &quot;TCGA-AB-2952&quot;, &quot;TCGA-AB-2954&quot;, &quot;TCGA-AB-2955&quot;, 
    &quot;TCGA-AB-2956&quot;, &quot;TCGA-AB-2959&quot;, &quot;TCGA-AB-2963&quot;, &quot;TCGA-AB-2964&quot;, 
    &quot;TCGA-AB-2965&quot;, &quot;TCGA-AB-2966&quot;, &quot;TCGA-AB-2967&quot;, &quot;TCGA-AB-2969&quot;, 
    &quot;TCGA-AB-2970&quot;, &quot;TCGA-AB-2971&quot;, &quot;TCGA-AB-2972&quot;, &quot;TCGA-AB-2973&quot;, 
    &quot;TCGA-AB-2975&quot;, &quot;TCGA-AB-2976&quot;, &quot;TCGA-AB-2977&quot;, &quot;TCGA-AB-2978&quot;, 
    &quot;TCGA-AB-2979&quot;, &quot;TCGA-AB-2980&quot;, &quot;TCGA-AB-2981&quot;, &quot;TCGA-AB-2982&quot;, 
    &quot;TCGA-AB-2983&quot;, &quot;TCGA-AB-2984&quot;, &quot;TCGA-AB-2985&quot;, &quot;TCGA-AB-2986&quot;, 
    &quot;TCGA-AB-2987&quot;, &quot;TCGA-AB-2988&quot;, &quot;TCGA-AB-2990&quot;, &quot;TCGA-AB-2991&quot;, 
    &quot;TCGA-AB-2992&quot;, &quot;TCGA-AB-2993&quot;, &quot;TCGA-AB-2994&quot;, &quot;TCGA-AB-2995&quot;, 
    &quot;TCGA-AB-2996&quot;, &quot;TCGA-AB-2998&quot;, &quot;TCGA-AB-2999&quot;, &quot;TCGA-AB-3000&quot;, 
    &quot;TCGA-AB-3001&quot;, &quot;TCGA-AB-3002&quot;, &quot;TCGA-AB-3005&quot;, &quot;TCGA-AB-3006&quot;, 
    &quot;TCGA-AB-3007&quot;, &quot;TCGA-AB-3008&quot;, &quot;TCGA-AB-3009&quot;, &quot;TCGA-AB-3011&quot;, 
    &quot;TCGA-AB-3012&quot;), c(&quot;Offset&quot;, &quot;CEBPA&quot;, &quot;DNMT3A&quot;, &quot;FAM5C&quot;, 
    &quot;FLT3_ITD&quot;, &quot;FLT3_TKD&quot;, &quot;IDH1&quot;, &quot;IDH2&quot;, &quot;KIT&quot;, &quot;KRAS&quot;, &quot;NPM1&quot;, 
    &quot;NRAS&quot;, &quot;PHF6&quot;, &quot;PTPN11&quot;, &quot;RAD21&quot;, &quot;RUNX1&quot;, &quot;SMC1A&quot;, &quot;SMC3&quot;, 
    &quot;STAG2&quot;, &quot;TET2&quot;, &quot;TP53&quot;, &quot;U2AF1&quot;, &quot;WT1&quot;, &quot;complex&quot;, &quot;minus5_5q&quot;, 
    &quot;minus7q&quot;, &quot;plus8_8q&quot;, &quot;plus21&quot;, &quot;t_15_17&quot;, &quot;t_8_21&quot;, &quot;inv16_t16_16&quot;
    )))
</code></pre>

CC BY-SA 4.0
2022-08-08T11:44:36.233Z
got an answer to start https://www.biostars.org/p/9533929/#9533942
kcm
<rna-seq><limma>
How to use the contrast function in ebayes for making different comparisons
# How to use the contrast function in ebayes for making different comparisons

(Asked by: username_0 on 2022-08-03)

The design matrix i have is this I would like to know how to use they way its done in deseq2 where we can use the contrast function to make particular comparison
The code im running to make differential testing is this
tcgaGlm = lmFit(as.matrix(tcgaExpr), design = tcgaDesign) 
tcgaGlm = eBayes(tcgaGlm)

Now I will show the part of the data frame and the question Im trying to ask
head(tcgaDesign)
             Offset CEBPA DNMT3A FAM5C FLT3_ITD FLT3_TKD IDH1 IDH2 KIT KRAS NPM1 NRAS PHF6 PTPN11 RAD21 RUNX1 SMC1A SMC3 STAG2 TET2 TP53 U2AF1 WT1
TCGA-AB-2803      1     0      0     0        0        0    0    0   0    0    0    0    0      0     0     0     0    0     0    0    0     0   0
TCGA-AB-2805      1     0      0     0        0        0    0    1   0    0    0    0    0      0     0     1     0    0     0    0    0     0   0
TCGA-AB-2806      1     0      0     0        0        0    0    0   0    0    0    0    0      0     0     0     0    0     0    0    0     0   0
TCGA-AB-2807      1     0      0     0        0        0    0    1   0    0    0    0    0      0     0     1     0    0     0    0    0     0   0
TCGA-AB-2808      1     1      0     0        0        0    0    0   0    0    0    1    0      0     0     0     0    0     0    0    0     0   0
TCGA-AB-2810      1     0      0     0        0        0    0    1   0    0    1    0    0      0     0     0     0    0     0    0    0     0   0
               complex  minus5_5q   minus7q  plus8_8q     plus21    t_15_17     t_8_21 inv16_t16_16
TCGA-AB-2803 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 1.00000000 0.00000000   0.00000000
TCGA-AB-2805 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 0.00000000 0.00000000   0.00000000
TCGA-AB-2806 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 0.00000000 1.00000000   0.00000000
TCGA-AB-2807 0.1410256 0.08974359 0.1217949 0.1153846 1.00000000 0.09615385 0.04487179   0.04487179
TCGA-AB-2808 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 0.00000000 0.00000000   0.00000000
TCGA-AB-2810 0.1410256 0.08974359 0.1217949 0.1153846 0.05063291 0.09615385 0.04487179   0.04487179

Here if I have to make a comparison between CEBPA vs DNMT3A mutation in samples which carries CEBPA mutation and other which carries DNMT3A what or how should I do it.
To begin with I followed this manual I m still not sure once you run the model do i need to form a design matrix which comprises of sample which contain mutation in CEBPA and DNMT3A ? or I can go ahead with this experimental design
dput(tcgaDesign)
structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
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    )))

**Tags:** rna-seq, limma
> **Comments:**
> **username_0**: got an answer to start https://www.biostars.org/p/9533929/#9533942  

---

## 1 answers

### Answer by username_1 on 2022-08-13. Score: 2 | Accepted answer

A design matrix is a matrix of values of the grouping variable.

ANOVA needs such a matrix to know which samples belong to which group. Since `limma` performs an ANOVA it needs this design matrix.

1. You can create it using the `model.matrix()` method.
2. Afterwards you can tell `limma` which groups you want to compare.

For point 2 you define a contrast matrix defining the comparisons of interest by using the `makeContrasts()` method.

It is well explained in this [manual's](https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf) section 9.2


---

