There are many fold-change calculation formulars. Since I read the paper A comparison of fold-change and the t-statistic for microarray data analysis, I think I need to summaries each mathods and make sure I understand them.
Because fold-change is widely used in RNA research. The background of the fold-change calculation in this post is based on how find the differentially-expressed genes in a experiment.
The original method
The standard definition of the fold-change is
treatment are the
raw expression levels.
An simple method
In some papers, I found they use deviation of control and treatment as the fold-change result.
If the difference of control and treatment data are not to large, I think we should use the simple method.
Some differentially expressed genes have large differences (B-A) but small ratios (A/B), this is another point why using simple methold instead of the original method.
If the difference or ratios of control and treatment is dynamic between genes, we need to scale the range of fold-change result.
Here we need the log fold-changes.
This method is used in qPCR experiment.
DCt: Target Ct - Housekeeping Ct
DDCt: Sample DCt - Calibrator DCt (Calibrator is your group of comparison)
Fold calculus: 2^-DDCt
For more detail, please see:
If we need the direction of the fold-change trend, we can use the sign function.