Question: Scaling Rpkm/Fpkm Values Between 0 And 1
0
gravatar for skm770
5.4 years ago by
skm770150
skm770150 wrote:

Rpkm and fpkm values vary a lot some times from 10^-6 to 10^6. I have seen people comparing methylation with RNA-seq. Since methylation usually lies between 0 and 1 people scale the RPKM between 0 and 1. I am unaware of how do people do that and was wondering if there any r packages available to do the same.

thanks

rpkm fpkm • 3.1k views
ADD COMMENTlink written 5.4 years ago by skm770150
1

Bizar question... people compare methylation with rna-seq? Do you mean associate methylation with expression? The fact that methylation values range between 0 and 1 (not always, it depends on what metric you use) doesn't mean rna-seq data needs to be in the same range. Yes, rpkm values can vary a lot (both within and between samples). More importantly, they are not normally distributed but log-transformed rpkm (do you mean fpkm?) are so start with that. However, I think you should contact a local bioinformatician to help you out. If you still want to scale rpkm values have a look into the scale()-function of R. Its in the R base functionality so no need to install any packages.

ADD REPLYlink modified 5.4 years ago • written 5.4 years ago by Irsan6.8k

What I meant by comparison is they see how expression is changing with RNA-seq usual ways they do it is in using box-plot with methylation and RNA-seq to see general patterns and dividing genes according to the methylation levels 10%,20%...100%.

ADD REPLYlink written 5.4 years ago by skm770150

Ok so I guess you have whole genome expression and methylation data and you want to know wich genes are epigenetically regulated? Why do you want to know that? If a gene is epigenetically regulated does that make it more interesting? I'm not just giving you a hard time, I'm trying to get things clear so you get maximum response on your question

ADD REPLYlink modified 5.4 years ago • written 5.4 years ago by Irsan6.8k

Yes we would like to see if a gene is epigentically regulated/not or in this particular case what is the pattern of expression and methylation.

ADD REPLYlink written 5.3 years ago by skm770150

Then for each gene, perform a linear regression of methylation-values on expression values of your complete cohort. Make sure your expression values are normally distributed (log2(FPKM)). Do false discovery rate adjustment on the resulting p-values and filter on fdr-adjusted < 0.05 and with negative slope (meaning more methylation results in less expression). if you want, you can use these genes again to do hierarchical clustering.

ADD REPLYlink modified 5.3 years ago • written 5.3 years ago by Irsan6.8k

can you explain how to "for each gene, perform a linear regression of methylation-values on expression values of your complete cohort",I'm very interesting , thanksĀ 

ADD REPLYlink written 5.0 years ago by Ares Cao20
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