<p>Methylation levels have high local correlation, so Fisher's method would be problematic. Having said that, you have no reason to use Fisher's method after a paired t-test. A paired t-test will give you a single p-value per gene, which is what you want. Do be sure to only include CpG with some minimal coverage in both group.</p>

CC BY-SA 3.0
2017-08-10T18:11:16.970Z
2017-08-10T18:11:16.970Z
Devon Ryan
<p>What is typically done in methylation analysis is to assess the islands of methylations. </p>

<p>Check <a href="https://www.bioconductor.org/help/workflows/methylationArrayAnalysis/#customising-visualisations-of-methylation-data" rel="nofollow noreferrer">this workflow</a>, in the section linked it uses some predefined islands for instance. I am no expert on this area but you could asses if the islands or certain regions are more methylated than expected.</p>

CC BY-SA 3.0
2017-09-12T09:13:45.453Z
2017-09-12T09:13:45.453Z
llrs
<p>I have some data I am working with, and I am curious if I am able to combine p-values from a paired t-test for CpG sites in the genome using Fisher's Method to get one p-value for each unique gene. Linked <a href="https://en.wikipedia.org/wiki/Fisher%27s_method" rel="noreferrer">here</a> is the Wikipedia page for Fisher's Method. I understand that an assumption of the method used is that each individual p-value being combined must be independent. I'm relatively new to biostatistics, so I'm curious if using CpGs from the same gene would violate this assumption.</p>

CC BY-SA 3.0
2017-08-11T10:54:03.857Z
Why do you want to melt CpG values? Do you want to know if an specific gene/region is more phosphorylated than others? What are your hypothesis (you tell about a t-test for the CpG sites? Are you testing if they are phosphorylated vs not or more than the rest of the genome or...?
llrs
CC BY-SA 3.0
2017-09-12T10:05:45.413Z
In a nutshell, your intuition is correct: they are not independent.
Konrad Rudolph
<r><bioconductor><gene><methylation>
Melt p-values for CpG sites mapping to the same gene
# Melt p-values for CpG sites mapping to the same gene

(Asked by: username_0 on 2017-08-10)

I have some data I am working with, and I am curious if I am able to combine p-values from a paired t-test for CpG sites in the genome using Fisher's Method to get one p-value for each unique gene. Linked [here](https://en.wikipedia.org/wiki/Fisher%27s_method) is the Wikipedia page for Fisher's Method. I understand that an assumption of the method used is that each individual p-value being combined must be independent. I'm relatively new to biostatistics, so I'm curious if using CpGs from the same gene would violate this assumption.

**Tags:** r, bioconductor, gene, methylation
> **Comments:**
> **username_1**: Why do you want to melt CpG values? Do you want to know if an specific gene/region is more phosphorylated than others? What are your hypothesis (you tell about a t-test for the CpG sites? Are you testing if they are phosphorylated vs not or more than the rest of the genome or...?  
> **username_2**: In a nutshell, your intuition is correct: they are not independent.  

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## 2 answers

### Answer by username_3 on 2017-08-10. Score: 6

Methylation levels have high local correlation, so Fisher's method would be problematic. Having said that, you have no reason to use Fisher's method after a paired t-test. A paired t-test will give you a single p-value per gene, which is what you want. Do be sure to only include CpG with some minimal coverage in both group.


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### Answer by username_1 on 2017-09-12. Score: 1

What is typically done in methylation analysis is to assess the islands of methylations.

Check [this workflow](https://www.bioconductor.org/help/workflows/methylationArrayAnalysis/#customising-visualisations-of-methylation-data), in the section linked it uses some predefined islands for instance. I am no expert on this area but you could asses if the islands or certain regions are more methylated than expected.


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