<p>Simply use %age methylation and this will correct for bias within and between samples. It's not comparable to RNAseq, thus not so critical.</p>
<p>If you are still concerned then use Combat the sva (Surrogate Variable Analysis) package in R, to standardise across different samples for batch effect, also look at the minfi package in Bioconductor which specialises in mythlation. Limma in Bioconductor will also do normalisation, e.g. quantile normalisation.</p>
<p>Thus you are well covered both in basic risk theory and packages to deal with possible bias.</p>

CC BY-SA 4.0
2024-12-12T10:36:48.773Z
2024-12-12T10:36:48.773Z
M__
<p>I was wondering what would be a good method to normalize bisulfite sequence data. Since there are multiple ways to normalize RNA-seq data, I haven't seen too many ways to normalize bisulfite sequencing data. I know my questions are naïve, but I am curious can I apply the same normalization method for RNA-seq to normalize bisulfite data or if it is okay not to normalize bisulfite data. Thank you very much.</p>

<methylation>
normalization of bisulfite sequencing data
# normalization of bisulfite sequencing data

(Asked by: username_0 on 2024-12-12)

I was wondering what would be a good method to normalize bisulfite sequence data. Since there are multiple ways to normalize RNA-seq data, I haven't seen too many ways to normalize bisulfite sequencing data. I know my questions are naïve, but I am curious can I apply the same normalization method for RNA-seq to normalize bisulfite data or if it is okay not to normalize bisulfite data. Thank you very much.

**Tags:** methylation

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## 1 answers

### Answer by username_1 on 2024-12-12. Score: 0

Simply use %age methylation and this will correct for bias within and between samples. It's not comparable to RNAseq, thus not so critical.

If you are still concerned then use Combat the sva (Surrogate Variable Analysis) package in R, to standardise across different samples for batch effect, also look at the minfi package in Bioconductor which specialises in mythlation. Limma in Bioconductor will also do normalisation, e.g. quantile normalisation.

Thus you are well covered both in basic risk theory and packages to deal with possible bias.


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