significance testing for the difference in the ratio of means

I have a question regarding significance testing for the difference in the
ratio of means.
The data consists of a control and a test group, each with and without
treatment.  I am interested in testing if the treatment has a significantly
different effect (say, in terms of fold-activation) on the test group
compared to the control.

The form of the data with arbitrary n and not assuming equal variance:

m1 = mean of (control group) n = 7
m2 = mean of (control group w/ treatment) n=  10
m3 = mean of (test group) n = 8
m4 = mean of (test group w/ treatment) n = 9

H0: m2/m1 = m4/m3
restated,
H0: m2/m1 - m4/m3 = 0;

Method 1: Fieller's Intervals
Use fieller's theorum available in R as part of the mratios package.  This
is a promising way to compute standard error/confidence intervals for each
of the two ratios but will not yield p-values for significance testing.
 Significance by non-overlap of confidence intervals is too stringent a
test and will lead to frequent type II errors.

Method 2: Bootstrap
Abandoning an analytical solution, we try a numerical solution.  I can
repeatedly (1000 or 10,000 times)  draw with replacement samples of size
7,10,8,9 from m1,m2,m3,m4 respectively.  Each iteration, I can compute the
ratio for m2/m1 and m4/m3 as well as the difference.  Standard deviations
of the m2/m1 and the m4/m3 bootstrap distributions can give me standard
errors for these two ratios.  Then, I can test to see where "0" falls on
the third distribution, the distribution of the difference of the ratios.
 If 0 falls on one of the tails, beyond the 2.5th or 97.5th percentile, I
can declare a significant difference in the two ratios.  My question here
is if I can correctly report the percentile location of "0" as the p-value?

Method 3: Permutation test
I understand the best way to obtain a p-value for the significance test
would be to resample under the null hypothesis.  However, as I am comparing
the ratio of means, I do not have individual observations to randomize
between the groups.  The best I can think to do is create an exhaustive
list of all (7x10) = 70 possible observations for m2/m1 from the data.
 Then create a similar list of all (8x9) = 72 possible observations for
m4/m3. Pool all (70+72) = 142 observations and repeatedly randomly assign
them to two groups  of size 70 and 72 to represent the two ratios and
compute the difference in means.  This distribution could represent the
distribution under the null hypothesis and I could then measure where my
observed value falls to compute the p-value.  This however, makes me
uncomfortable as it seems to treat the data as a "mean of ratios" rather
than a "ratio of means".

Method 4: Combination of bootstrap and permutation test
Sample with replacement samples of size 7,10,8,9 from m1,m2,m3,m4
respectively as in method 2 above.  Calculate the two ratios for these 4
samples (m2/m1 and m4/m3).  Record these two ratios into a list.  Repeat
this process an arbitrary (B) number of times and record the two ratios
into your growing list each time.  Hence if B = 10, we will have 20
observations of the ratios.  Then proceed with permutation testing with
these 20 ratio observations by repeatedly randomizing them into two equal
groups of 10 and computing the difference in means of the two groups as we
did in method 3 above.  This could potentially yeild a distribution under
the null hypothesis and p-values could be obtained by localizing the
observed value on this distribution.  I am unsure of appropriate values for
B or if this method is valid at all.

Another complication would be the concern for multiple comparisons if I
wished to include additional  test groups (m5 = testgroup2; m6 = testgroup2
w/ treatment; m7 = testgroup3, m8 = testgoup3 w/ treatment...etc) and how
that might be appropriately handled.

Method 2 seems the most intuitive to me.  Bootstrapping this way will
likely yield appropriate Starndard Errors for the two ratios.  However, I
am very much interested in appropriate p-values for the comparison and I am
not sure if localizing "0" on the bootstrap distribution of the difference
of means is appropriate.

Thank you in advance for your suggestions.

-Rahul

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