# 多重检验中的FDR错误控制方法与p-value的校正

The False Discovery Rate (FDR) of a set of predictions is the expected percent of false predictions in the set of predictions. For example if the algorithm returns 100 genes with a false discovery rate of .3 then we should expect 70 of them to be correct.

The FDR is very different from a p-value, and as such a much higher FDR can be tolerated than with a p-value. In the example above a set of 100 predictions of which 70 are correct might be very useful, especially if there are thousands of genes on the array most of which are not differentially expressed. In contrast p-value of .3 is generally unacceptabe in any circumstance. Meanwhile an FDR of as high as .5 or even higher might be quite meaningful.

> p<-c(0.0003,0.0001,0.02)
>  p
[1] 3e-04 1e-04 2e-02
>
[1] 1e-04 3e-04 2e-02