Hello Tinku,

First, you have to divide the FPKM of the second value (of the second group) on the FPKM of the first value to get the Fold Change (FC). then, put the equation in Excel =Log(FC, 2) to get the log2 fold change value from FPKM value. 

Noted with thanks @Rana al-bagdadi

Dear

I send you an approach coming from bioinformatique links sources:

Suppose 2 gene expression values A,B (treatment):

A=10

B=15

Foldchange is  B/A => FC=1.5 or greater is Up regulated , and if the values were B=10,A=15 we'll have FC=0.66 it means all values less than 0.66 will be down regulated.

So to calculate log2-foldchange, its formula is

log2FC=Log2(B)-Log2(A)

which then all values greater than 0.5849 were be up regulated and all values less than -0.5849 (or FC =0.666) were be down regulated genes, protein or etc.

For calculating Fold change from log2 just do , Power(2, log2_Value)

 , Power(2, 0.5849)=1.5

You can also read Microarray data normalization and transformation. Nature Genetics 32, 496 - 501 (2002) .

Hoping to be helpful

Marius

http://www.nature.com/ng/journal/v32/n4s/pdf/ng1032.pdf

What about the scenario where fpkm value of a is 0 and b is 10, If we divide 10/0 we get infinity. Or consider a case where a is 10 and b is 0, in that case b/a will be zero and the log value would be inifinity. Is that the correct way of representing fold change

Hello i am new to RNA-seq data analysis and i am working with RNA-seq data analysis of Tumor and Normal samples. I have some queries regarding the FC calculation and interpretation

Question 1:

I am unable to understand the above post shown by Marius K. Somda . It says, all value greater than the calculated FC FC=0.66 is up regulated and similarly, value less than FC=0.66 is down regulated.

My question is what is "all value" in the data set, as FC=0.66 is the actual fold change, we calculated.

Lets consider B is expression value of Gene-1 in Tumor sample1 and A is expression value of Gene-1 in Normal sample1

As per above formula

FC=B(Tumor)/A(Normal)=10/15= 0.666667.

So, how do we interpret this FC value? can we say there is a down regulation of Gene-1 in the Tumor sample as compared to Normal sample?

Question 2:

In the case of 0 values i used to add +1 to all values to avoid "Inf" and "NA" values

Marius K. Somda :For two samples it is far easy to understand. If I have 30 samples with normalized counts then how to calculate log2fold change?

DID YOU SOLVE YOUR PROBLEM?I have the same question as yours@ Shaikh Mohammed Inayat

Is it technically right to add +1 in place of 0 values to avoid from Inf values @ Abdessamad El Kaoutari

how do we calculate fold change for more than two groups ?

Thank you so much Rana Jaber Tarish Al-Baghdadi

How to calculate P-value from FPKM?

Kutti Biotech the FPKMs of a gene represents the abundance of that gene normalised by gene length and sequence depth and it doesn`t affect the stats. so you calculate the p.value as you would normally do with any other experiments

Kutti Biotech You can handle the FPKMs as the level of expression as Dr. Boschian says.

Everyone without computational experience to perform gene-count level statistical analysis in R or python, please do yourself a favor and use Biojupies. https://maayanlab.cloud/biojupies/ developed by the Maayan Lab.

Cheers,

F

1) In excel =Log(number, base).

2) To get back to TPM or FPKM value the formula would be in excel= 2^ - number.