Dear all,


I am starting to analyze the results of a DGE experiment, and I have 
some doubts about how to perform multi-group comparisons. Our data set 
consists in just one developmental stage, but I have data from 12 
different genotypes (I have duplicates of the samples).

I will like to identify all the differentially expressed genes among the 
different genotypes. I will like to know your opinion about edgeR and 
baySeq packages as both seam to be able to perform multi-group analysis.

And I will also appreciate if you could give me any idea about the best 
way to design mi contrast hypothesis.


*When working with edgeR I have employ this type of contrast matrix for 
my analysis*


 > cultivar <- 
factor(c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12))

 > design <- model.matrix(~cultivar)

 > design

(Intercept) cultivar2 cultivar3 cultivar4 cultivar5 cultivar6 cultivar7 
…....

1 1 0 0 0 0 0 0

2 1 0 0 0 0 0 0

3 1 1 0 0 0 0 0

4 1 1 0 0 0 0 0

5 1 0 1 0 0 0 0

6 1 0 1 0 0 0 0

7 1 0 0 1 0 0 0

8 1 0 0 1 0 0 0

9 1 0 0 0 1 0 0

10 1 0 0 0 1 0 0

:

:

:

attr(,"assign")

[1] 0 1 1 1 1 1 1 1 1 1 1 1

attr(,"contrasts")

attr(,"contrasts")$cultivar

[1] "contr.treatment"


*However I believe that will just help me to identify the genes that are 
differentially expressed in one of the cultivars with respect to the 
rest. Besides if I keep going doing all the contrast at the same time as 
people from the bioconductor mail list suggest me to extract the 
differentially expressed genes I get a mistake.*


 > glmfit.d2 <- glmFit(d2, design, dispersion = d2$common.dispersion)

 > names(glmfit.d2)

[1] "coefficients" "fitted.values" "deviance" "df.residual"

[5] "abundance" "design" "offset" "dispersion"

[9] "method" "counts" "samples"

 > lrt.d2 <- glmLRT(d2, glmfit.d2, coef =2:12)


 > summary(decideTestsDGE(lrt.d2, adjust.method="BH", p.value=0.05))

Error in array(x, c(length(x), 1L), if (!is.null(names(x))) 
list(names(x), :

attempt to set an attribute on NULL


*If I check just one of my possible contrasts it works*


 > lrt.d2test <- glmLRT(d2, glmfit.d2, coef =2)

 > summary(decideTestsDGE(lrt.d2test, adjust.method="BH", p.value=0.05))

[,1]

-1 875

0 14026

1 1460


*However I think I am just getting genes whose expression is different 
in one of the cultivars, but not changing in the rest. And I will like 
just to get all the genes differentially expressed no matter in which 
cultivar or cultivars.*


*I have also try to carry these analysis with baySeq package, and I have 
design this contrast hypothesis.*


Slot "groups":

$NDE

[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$DE

[1] 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12


*But, I believe with these approach I will not be able to identify genes 
that for example may have the same expression in cultivars 1,2,3,4 but 
different to the expression in cultivars 5,6,7,8. What do you think 
about it? Do you think I need to add model for each of the possible 
combinations? *

*I will appreciate any suggestion that you could give me about the best 
way to analyze those data.*


*Thanks in advance for your attention*



*Laura Pascual*


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