[BioC] Help interpreting many contrasts in one contrast matrix versus many individual contrast matrices

[Belisa Santos [guest]]

Hello everybody, 

     I am having a hard time interpreting in a meaningful way the output from a contrast matrix with many contrasts versus a smal contrast matrix with few contrasts and how they compare to each other.

# Description of my dataset:

Control: No treatment and time zero (total 6 replicates)
Treatment A: time1, time2, time3 and time4 (3 replicates each, total 12)
Treatment AB: time1, time2, time3 and time4 (3 replicates each, total 12)
Treatment AC: time1, time2, time3 and time4 (3 replicates each,  total 12)
Treatment ABC: time1, time2, time3 and time4 (3 replicates each, total 12)

Total of 54 microarrays, where A, B and C are different compounds used for the growth media of the cells. 

- I do not have ONE unique research question. I want to see the effect of time, the effect of treatment and the effect of the interaction time-treatment. Also, I have one very specific question which is: What is the effect of the interaction BC? (Not interested in the effect of time for this one...) 

# My approach:
    - I made a design matrix using Control as intercept (so first column (control) filled with 1s)
    - Then made 3 BIG contrast matrices: one for the treatment factor (i.e. all combinations of contrasts between same time different treatment ), one for the time factor (i.e. all combinations of same treatment different time) and one for the interaction treatment-time (all combinations treatment-time). (Still have to come up with a clever way to find the effect of the interaction BC...)

# My doubts are:

   1) Can I describe my experiment as a 2x2 factorial design (2 factors: time and treatment)? (I ask this because I also have that extra control I used as intercept...)

   2) Am I correct to interpret  that given that I have used the control as intercept in the design matrix, all subsequent contrasts will have the effect of control "subtracted"?
       2.1) Is this a correct approach for my case? (Is this conceptually correct? Is it done frequently? Is it the most elegant way to do it, or are there "better" alternatives?)

   3) Finally I am having problems interpreting the outcome of my contrasts from the matrices with many contrasts. For example for my contrast matrix for the treatment factor (there are 24 individual contrasts), when I ask for a topTable (without specifying any particular coefficient), what is exactly the meaning of that list? Are those the union of all the genes that are differently expressed in all contrasts and then ordered? Or is there any other testing done that makes this DEG list more meaningful than just doing individual contrasts, uniting the sets and ordering them... I feel these cannot be the same... but do not know... and I need help to interpret it correctly.

I would really appreciate some help with these doubts. I have read the documentation several times now, but my experimental design is not fully covered by any example... and i would like to be sure that i am analyzing my data correctly.

Thank you in advance for your attention and patience. Kind regards, 

Belisa 

 -- output of sessionInfo(): 

> sessionInfo()
R version 2.15.0 (2012-03-30)
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)

locale:
[1] C/en_US.UTF-8/C/C/C/C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] limma_3.14.1          annotate_1.36.0       hgu133plus2cdf_2.11.0 hgu133plus2.db_2.8.0 
 [5] org.Hs.eg.db_2.8.0    RSQLite_0.11.2        DBI_0.2-5             AnnotationDbi_1.20.2 
 [9] affy_1.36.0           Biobase_2.18.0        BiocGenerics_0.4.0   

loaded via a namespace (and not attached):
 [1] BiocInstaller_1.8.3   IRanges_1.16.4        XML_3.95-0.1          affyio_1.26.0        
 [5] parallel_2.15.0       preprocessCore_1.20.0 stats4_2.15.0         tools_2.15.0         
 [9] xtable_1.7-0          zlibbioc_1.4.0       

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