Differential methylation analysis and paired samples
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Entering edit mode
tim.meese • 0
@timmeese-23719
Last seen 4 months ago
Belgium

Hello

I am new to the analysis of bisulfite sequencing data with edgeR. I read the user guide and wrote code to perform the analysis. With this post, I want to check if my design matrix and contrasts are the correct ones to assess the question of interest.

target
  sample subject condition
1 SampleA animal1    normal
2 SampleB animal1   treated
3 SampleC animal2    normal
4 SampleD animal2   treated
5 SampleE animal3    normal
6 SampleF animal3   treated

I want to discover which promoters are differentially methylated between treated and normal, while correcting for animal.

I generated the design matrix as described in the f1000 paper "Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR". I first created the design matrix that I would use for a RNA-seq differential expression and afterwards expanded it with modelMatrixMeth.

d <- model.matrix(~ 0 + subject + condition, data = target)

d <- modelMatrixMeth(d)

d

   Sample1 Sample2 Sample3 Sample4 Sample5 Sample6 subjectanimal1 subjectanimal2 subjectanimal3
1        1       0       0       0       0       0              1              0              0
2        1       0       0       0       0       0              0              0              0
3        0       1       0       0       0       0              1              0              0
4        0       1       0       0       0       0              0              0              0
5        0       0       1       0       0       0              0              1              0
6        0       0       1       0       0       0              0              0              0
7        0       0       0       1       0       0              0              1              0
8        0       0       0       1       0       0              0              0              0
9        0       0       0       0       1       0              0              0              1
10       0       0       0       0       1       0              0              0              0
11       0       0       0       0       0       1              0              0              1
12       0       0       0       0       0       1              0              0              0
   conditiontreated
1                 0
2                 0
3                 1
4                 0
5                 0
6                 0
7                 1
8                 0
9                 0
10                0
11                1
12                0

Afterwards, I proceeded as described in the f1000 paper. I used glmLRT(fit) to test the relevant coefficient. I thought that "conditiontreated" was the coefficient that describes the difference between normal and treated while correcting for animal, but I am not quite sure if this is correct.

Many thanks in advance.

edger bisulfite sequencing • 628 views
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Entering edit mode
@gordon-smyth
Last seen 5 hours ago
WEHI, Melbourne, Australia

Yes, your design matrix is correct and conditiontreated is the treated vs normal coefficient.

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Entering edit mode

Thank you very much.

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