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

[Gordon K Smyth]

On Mon, 26 Nov 2012, Gordon K Smyth wrote:

> Dear Belisa,
>
> Your experiment has 17 different conditions, so you obviously cannot analyse 
> it as a 2x2 experiment.  (A 2x2 experiment has only 4 conditions in total.)
>
> The simplest way to analyse your experiment is to create a single factor with 
> 25 levels, and to analyse your data as in Section 8.3 in the limma User's 
> Guide.

That should read "17 levels", one for each condition.

Gordon

> This allows you to test any hypothesis you like, including testing 
> for interactions.
>
> If you have lots of contrasts, but you don't tell topTable() which contrast 
> you want to test for, then topTable() will test whether *any* of the 
> contrasts are different from zero.  This is analogous to an F-test where the 
> numerator degrees of freedomm are the number of contrasts.  The help page for 
> topTable() says:
>
> "topTableF ranks genes on the basis of moderated F-statistics for one or more 
> coefficients. If topTable is called with coef that has length greater than 1, 
> then the specified columns will be extracted from fit and topTableF called on 
> the result. topTable with coef=NULL is the same as topTableF, unless the 
> fitted model fit has only one column."
>
> You might find it very help to collaborate with a statistical 
> bioinformatician at your own institute, if one is available.
>
> Best wishes
> Gordon
>
>
>> Date: Sat, 24 Nov 2012 06:06:22 -0800 (PST)
>> From: "Belisa Santos [guest]" <guest at bioconductor.org>
>> To: bioconductor at r-project.org, belisa.santos.duarte at gmail.com
>> Subject: [BioC] Help interpreting many contrasts in one contrast
>> 	matrix	versus many individual contrast matrices
>> 
>> 
>> 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
>> 
>> --
>> Sent via the guest posting facility at bioconductor.org.
>

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