I am not a statistician so apologies if my terminology is wrong
I have a dataset of >14 million p values derived from Fisher's exact testing on genome scale sequencing data.
Benjamini-Hochberg correction of these p-values turns out only ~1500 significant p-values so I have been attempting to use qvalue R package to relax the FDR and therefore gain a greater number of significant p-values.
Applying the qvalue package with default options returns a $\pi_0$ of 1
qobj<-qvalue(p.fish.unadj) qsummary(qobj) pi0: 1
I presume this is a "good thing" and means that:
- I have good power in my dataset to detect true null results and therefore true alternative results also
- $\pi_0=1$ is an approximation because my true alternatives i.e. significant tests is such a small number compared to the overall size of the dataset
Am I right?