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2.1 years ago

ATCG
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370

Is it appropriate to apply quantile normalization on RNA-Seq before computing the Pearson correlation? Specifically, RNA-Seq data displays a dense tail distribution, and Person's correlation (`cor`

function in R) assumes a normal distribution. I tried this in R using the `normalize.quantiles`

function discussed in the lecture, and my results make a lot of sense, but I want to make sure that this is an appropriate transformation before accepting these results.
Thank you for your help!

Of course you will get normal distributions out of

`normalize.quantiles`

but I wouldn't count on it. The main problem is the low counts, you will get random effects amplified. I would try spearman's correlation instead but make sure you normalize for library size first.Yes. My pipeline is rawcounts-->DESeq2-->normalized.quantiles Or do you suggest normalizing the rawcounts using a different method? Is there an R function to do the library normalization?

DESeq2 normalization is great. Maybe try spearman's on the normalized count table. Make sure the

`r`

distribution looks good. You can use the`normalize.quantiles`

but just make sure you're not getting artifacts from 0 counts.Yes. I am removing rowMeans<10.