Dear members,
I have several RNA-seq data sets from published articles. We think integration of them may be interesting. But they come from different types of libraries (single-end/paired-end, PolyA+/rRNA depletion). How can I normalize them and in this way, compare their expression directly?
I read an article using different types of data just normalizing them by RPKM, is this a good method?
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ktian.whu •
FPKM/RPKM is not a normalization method. It is a unit, meant for representing some sort of molar concentration of transcripts.
In my experience, if you have very different libraries, FPKM will confound biological comparability instead of making it more comparable. Calculating FPKM (imho) does not remove technical bias, but adds an unknown proportionality factor to each library, which is based on library composition. Therefore if you have very different protocols it will not help to length-scale abundances. Problems of using FPKM have been stated in several posts, e.g.:
Some more problems:
Using FPKM scaled values for your use-case might be the worst possible application.
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Michael Dondrup
Would it be right if we quantile normalize the dataframe containing FPKM values of genes obtained from Cufflinks, where samples in dataframe had different types of libraries?
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Manvendra Singh
From my data, it doesn't seem that quantile normalization cures it. Of course this is a very limited view. I can only say that observations on our data correlate with the published concerns, and we seem to exactly observe the library dependent bias that was mentioned. In principle all points I made above apply even more for quantile normalized FPKM (not peer reviewed combination of two methods, etc.).
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Michael Dondrup
Thanks for sharing this. It helps.
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Manvendra Singh
If you want to get differentially expressed genes, you need to control the library type as a factor when you use DESeq or limma.
You may also use sva to remove batch effect
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Ming Tommy Tang
When combining these datasets please take extreme caution with results interpretation and design your analysis in such a way that you exclude bias effect.
For more information about the biases in library prep read this:
Lahens NF, Kavakli IH, Zhang R, Hayer K, Black MB, Dueck H, Pizarro A, Kim J, Irizarry R, Thomas RS, Grant GR, Hogenesch JB.
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Floris Brenk
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I really wonder why these many RPKM/FPKM questions come up lately.
Any update on this work? I am planning for something similar.