[BioC] design in mixed ref and dye-swap experiment

[Silvano Piazza]

Dear Naomi

First of all, thank you very much for your answer.
>
>
> As I have indicated elsewhere on this list, the "p-values" reported by 
> TopTable are actually "q-values".  Hence, if you have fewer 
> "significant" genes than expected by chance under the null hypothesis, 
> the reported p-value is 1.0.
>
> e.g. Suppose you have 1000 genes.  Then if the number of genes 
> significant at alpha% is less than 1000*alpha for each alpha, your 
> TopTable p-value will be 1.0 (i.e. all of the significant genes are 
> estimated to be false positives).
>

that's very clear now, thanks again.


> Your experiment design is needlessly complex and also wasteful.  If 
> you have only 2 conditions, you should do one of the following:
>
> hybridize both conditions to every array (in dye-swap pairs) with no 
> technical replicates (This is most efficient)
> use a reference design with the reference sample always in the same 
> channel. (This is simplest, but has 1/2 the efficiency.)
>
> Mixing these 2 designs, especially with a mix of biological and 
> technical replicates needlessly complicates your analysis.  It also 
> requires a mixed model ANOVA to take into account the different levels 
> of replication.
>

Yes, I know I know....
but  unfortunately I  could not  decide, in this case,  how to make the 
experiments, so my situation is: these experiments are available at the 
moment and I have to find out DE genes, and only for this reason I was 
wondering if there is any correct methods to work in "mixed" (exp vs 
ref and dye-swap) design, thats means to extract more information that 
it is possible.
Thank you

Silvano

> --Naomi
>
> At 10:28 AM 3/25/2005, Silvano Piazza wrote:
>> Hello to everyones,
>> the experiments that I have to consider is very simple:
>>
>> I want  to find significant genes between 2 conditions A and B, but I 
>> have only few experiment so I have to collect both ref versus 
>> conditions (A or B) either dye swap experiment (A versus B and B 
>> versus A)
>>
>> so targets is
>> SlideNumber     Cy3     Cy5
>> array1          ref     A
>> array2          ref     B
>> array3          ref     B
>> array4          ref     B
>> array5          A       B
>> array6          B       A
>>
>> of course array5 and array6 are the dye-swap.
>>
>> So to design the procedure, I follow the LIMMA user guide (by Gordon 
>> Smith), Chapter 14.5 Weaver Mutant Data.
>>
>> so
>> >design <- modelMatrix(targets, ref = "ref")
>>         Found unique target names:
>>         B A ref
>> >design
>>         A       B
>>         array1    0    1
>>         array2    1    0
>>         array3    1    0
>>         array4    1    0
>>         array5   -1    1
>>         array6    1   -1
>> >fit <- lmFit(MA,design)
>> >cont.matrix <- makeContrasts(A.B=A-B,levels=design,weight=MA$weights)
>> >fit2 <- contrasts.fit(fit, cont.matrix)
>> > fit2 <- eBayes(fit2)
>> >topTable(fit2,adjust.method="fdr")
>>         ....omissis...
>>                         M               A                       t     
>>    P.Value         B
>>         209             3.801460        6.538782  8.315672 1.0000000 
>> -4.209468
>>         2328    1.184194        7.343676  6.717978 1.0000000 -4.228492
>>         7877    1.904360        6.504330  6.114349 1.0000000 -4.239110
>>         27187   -4.0759493.771499 -5.783558 1.0000000 -4.246099
>>         3709    3.434542 3.467492  5.639159 1.0000000 -4.249459
>>         7561    2.002753 5.159913  5.616194 1.0000000 -4.250013
>>         7130    2.580527 3.863867  5.600047 1.0000000 -4.250405
>>         19983   -2.1176246.836539 -5.567882 1.0000000 -4.251194
>> So all genes have P.Value equal to 1!!!!!!
>> in previous posts I read that this happen when you have to consider 
>> multivariate test, which i don't known how to manage..., but anyway
>>
>> 1) Am I doing something wrong in the design?
>> 2) Am I doing something wrong in the subsequent evaluation steps?
>> Any ideas
>>
>>
>>
>> Thank you to all
>>
>> Silvano
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> Dr.Silvano Piazza
>> LNCIB,
>> Area Science Park,
>> Padriciano 99
>> Trieste, ITALY
>> Tel. +39040398992
>> Fax +39040398990
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348 (Statistics)
> University Park, PA 16802-2111
>
>
>
Dr.Silvano Piazza
LNCIB,
Area Science Park,
Padriciano 99
Trieste, ITALY
Tel. +39040398992
Fax +39040398990