How is q value defined? 
*

*From Applied Statistical Genetics with R by Foulkes (pFDR = positive false discovery rate):

The q-value is defined similarly as the greatest lower bound of the $pFDR$ that can result from rejecting a test statistic $T$ and a
  sample $x$ with $T(x)=t$ based on a rejection region $Γ$ in the set
  of nested rejection regions. Formally, the q-value is written $$q(t)
    =\inf_{Γ :t∈Γ } [pFDR(Γ )]$$

Since $pFDR$ is involved here, there must be several null
hypotheses, and different null may have different testing rule. But
here does it assume that there is one common testing rule $(T, Γ)$
for all the nulls?

*From Wikipedia

The q-value is defined to be the FDR analogue of the p-value. The q-value of an individual hypothesis test is the maximum FDR at which the test may be called significant. One approach is to
  directly estimate q-values rather than fixing a level at which to
  control the FDR.



*

*Why are we talking about the q-value of an individual hypothesis test? Isn't the q-value for multiple nulls and their test
rules?

*Should "maximum FDR"  be "minimum FDR" instead?

*What does "One approach is to directly estimate q-values rather than fixing a level at which to control the FDR" try to mean? What is "one approach" trying to solve?


*Am I right that pFDR depends on and changes with the sample's distribution, so does q-value?
Thanks and regards!
 A: *

*Indeed. It is assumed that all test statistics that fall in $\Gamma$ are rejected and others are not.
Also note that in a pure (i.e., subjective) Bayesian framework, there need not be multiple hypotheses to define the pFDR. This is, however, now the view Storey has been promoting. 

*This Wikipedia entry actually discusses a different meaning/definition of the q-value, which preceded Storey's definition and makes life rather confusing (see also here). 
The "individual" hypothesis discussed is the marginal hypothesis rejected. 
It is true that hypothesis will typically not be rejected alone. That is why the marginal rejection is discussed.
It should indeed be the minimal FDR. 
The two competing approaches to multiplicity control are: (1) Set an error level and find a procedure that controls it. (2) Estimate the error level and reject when the estimated error level reaches a desired value. 

*The estimated $pFDR(\Gamma)$ depends on the sample. It's value does not depend on the sampling distribution of each test statistic (it is simple arithmetics), but it's sampling properties will naturally depend on the sampling distribution. Particularly, the test statistics need to be independent for Storey's estimator to be unbiased to the true $pFDR(\Gamma)$.
