Question: How to calculate "fold changes" in gene expression?
7
4.6 years ago by
juan.rosas170
Puerto Rico
juan.rosas170 wrote:

Hellow I have a question on how to calculate fold changes when analyzing gene expression changes between multiple tumor and control samples per gene?

R gene • 97k views
modified 3.3 years ago by Vasei30 • written 4.6 years ago by juan.rosas170
6

Let's say there are 50 read counts in control and 100 read counts in treatment for gene A. This means gene A is expressing twice in treatment as compared to control (100 divided by 50 =2) or fold change is 2. This works well for over expressed genes as the number directly corresponds to how many times a gene is overexpressed. But when it is other way round (i.e, treatment 50, control 100), the value of fold change will be 0.5 (all underexpressed genes will have values between 0 to 1, while overexpressed genes will have values from 1 to infinity). To make this leveled, we use log2 for expressing the fold change. I.e, log2 of 2 is 1 and log2 of 0.5 is -1.

Hope this helps

@arnstrm what will happen if you have the same number of replicate for both control and treated ? how do you calculate the fold change?

3
4.6 years ago by
Antonio R. Franco4.3k wrote:

Or the bioconductor limma package if you are dealing with arrays and/or RNA-Seq to analyze your data

Limma will give you the log2 expression changes based upon statistical values

But how can I use it?

Have you read the user's guide?

1
4.6 years ago by
Ido Tamir5.0k
Austria
Ido Tamir5.0k wrote:

You could reinvent the wheel of course, but If you ask such a question, use what pros have put a lot of thought in:

http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html

http://www.bioconductor.org/packages/release/bioc/html/edgeR.html

4

It should also be noted that if someone has to ask how to do this sort of thing then he/she probably shouldn't be reinventing this particular wheel...

0
3.3 years ago by
Vasei30
Vasei30 wrote:

Fold change can also be computed in unsupervised fashion, where we don't know the class labels(like case-control or type1-type2) of the samples. In that setting we can use mean expression of a gene as the base value and compute the fold change for that gene in each sample.