[BioC] normalization and analysis of connected designs

[w.huber at dkfz-heidelberg.de]

Hi Ramon,

You can use the chaining of "generalized log-ratios" (from vsn) in the
same way as you suggested for the log-ratios. You can see the
transformation that vsn makes as resulting in shrunken estimates of the
log-ratios. The advantage over log-ratios is that you don't have to worry
so much about the dependence of their variance on the mean (e.g.
log(R*G)).

Another point: It may not always be true that

[1]	h_3G - h_3R + h_2G - h_2R + h_1G - h_1R

is a better estimate for the D-A comparison than

[2]	h_3G - h_1R

Here, h_3G is the green channel on array 3, h_1R the red on array 1, and
so on. For good arrays, [2] should have a three times lower variance.
However, [1] may be able to correct for spotting irregularities between
the chips. Thus which is better depends on the data and the quality of the
chips. You may want to try both.

Best regards

  Wolfgang

On Tue, 1 Jul 2003, Ramon Diaz wrote:
> Suppose we have an experiment with cDNA microarrays with the structure:
> A -> B -> C -> D
> (i.e., A and B hybridized in the same array, A with Cy3, B with Cy5; B and C
> in the same array, with B with Cy3, etc).
>
> In this design, and if we use log_2(R/G), testing A == D is straightforward
> since A and D are connected and we can express D - A as the sum of the log
> ratios in the three arrays.
>
> But suppose we use some non-linear normalization of the data, such as loess as
> in Yang et al. 2002 (package marrayNorm) or the variance stabilization method
> of Huber et al., 2002 (package vsn).  Now, the values we have after the
> normalization are no longer log_2(R/G) but something else (that changes with,
> e.g., log_2(R*G)).  Doesn't this preclude the simple "just add the ratios"?
> Is there something obvious I am missing?
>
> Thanks,
>
> Ramón
>