Question: What is the best way to quantile normalize expression matrix ?
gravatar for jack
4.9 years ago by
jack750 wrote:

I have an expression matrix, its rows belong to different genes and columns belong to samples.

I want to do quantile normalization. But for me seems two ways to normalize it. First: row wise; normalize the expression of  every gene in all samples. Second: normalize columnwise: normalized expression of all genes in one sample and then go to other sample and ....


Which one makes more sense?  

ADD COMMENTlink modified 18 months ago by DataFanatic130 • written 4.9 years ago by jack750
gravatar for Devon Ryan
4.9 years ago by
Devon Ryan89k
Freiburg, Germany
Devon Ryan89k wrote:

The point of quantile normalization is to make the signal distribution of the samples as close as possible, so you normalize the columns. Trying to normalize the rows would introduce a whole host of issues that could seriously muck with the results (e.g., since you look for differential expression across rows, how might normalizing each row affect that...).

ADD COMMENTlink written 4.9 years ago by Devon Ryan89k

Good point Devon. I'm using  normalize.quantiles(x,copy=TRUE) to do quantile normalization, but they didn't explian that, it's normalize by row or column. do you know useful function for quantile normalization ?


ADD REPLYlink written 4.9 years ago by jack750

The only other one that I know of off-hand (aside from tweaked versions, like cqn) is normalizeBetweenArrays() from the limma package. I wouldn't be surprised if there are others.

ADD REPLYlink written 4.9 years ago by Devon Ryan89k
gravatar for  DataFanatic
18 months ago by
DataFanatic130 wrote:

Thank you very much for answering this question Devon, I have a follow-up question: Under what circumstances is it appropriate to do both log transformation and quantile normalization? Thank you in advance.

ADD COMMENTlink written 18 months ago by DataFanatic130
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