why expression value must be log2 transformed before further analysis, such differential analysis?

Question

[jlshi.nudt]

Hi, just as the title.

When using microarray, the intensity should be log transformed, that is partly because the intensity values are relative numbers. But in RNA-Seq, why must RPKM be log transformed?

I also notice that the differential analysis results are largely different with/without log transform. why? anyone tell me? thank you.

[Xingyu Yang]

Who told you this? Log transformed expression are easier to detect DEG with low expression level. However, I never heard it's a must to log transformed them.

[jlshi.nudt]

I just saw this transformation on many papers resolved with RPKM and microarray expression data. It seems to be a usual convention.

Btw, I guess that low expression levels are not believable, so I always to filter with some threshold value.

Answer 1

[Devon Ryan]

The general reason to log-transform data (log2 or otherwise) is to make variation similar across orders of magnitude. This isn't really a must, but usually makes things more convenient. Having said that, tools like limma are expecting log2 values, so if you're going to plug your RPKMs into it then it'd be a very good idea to log2 them first.

Answer 2

[mikhail.shugay]

Indeed microarray values and RPKM/FPKM values are better correlated when log-transformed. The reason for it is that the distribution of RPKM/FPKM values is skewed, and by log-transforming it we could bring it closer to normal distribution. It is needless to say that many statistical tests require normally-distributed data..