Dear Georg,

 

I think the first model is more appropriate.

 

In your second model, the deviance under the alternative is larger than it
should (since the difference between A and B is not accounted for).

That makes the LR statistics smaller than they should, which results in
fewer DE genes.

 

By the way, the dispersions have to be re-estimated if a different design
matrix is used.

I'm not sure whether you've done it or not as I cannot tell from the given
code.

 

Regards,

Yunshun Chen





  _____  

Dear Bioconductors,
 
I am working on RNA-seq data with multiple experimental factors and I am
trying to reproduce the edgeR manual, chapter 3.2.3, GLM approach.
 
 
> design <- model.matrix(~0+group, data=y$samples)
> colnames(design) <- levels(y$samples$group)
> design
               A       B       C
sample.1       1       0       0
sample.2       1       0       0
sample.3       0       1       0
sample.4       0       1       0
sample.5       0       0       1
 
> fit <- glmFit(y, design)
 
 
I want to know which genes are differentially expressed in C compared to
the other groups, so I chose to compare C to the average of A and B
 
> lrt <- glmLRT(fit, contrast=c(-0.5,-0.5,1))
 
 
Alternatively I could put A and B in a single group
 
> design
               A.B     C
sample.1       1       0
sample.2       1       0
sample.3       1       0
sample.4       1       0
sample.5       0       1
 
> fit <- glmFit(y, design)
 
an compare C to A.B
 
> lrt <- glmLRT(fit, contrast=c(-1,1))
 
 
When I try this with my own data, the first approach gives me many more
differentially expressed genes than the second one, but the second gene
set is a subset of the first one. I would be very grateful if somebody
could explain to me what is the difference between the approaches, and
which one is the more appropriate for my purpose (find genes specific
for condition C)
 
Best wishes,
 
Georg
 
> sessionInfo()
 
R version 3.0.1 (2013-05-16)
Platform: x86_64-unknown-linux-gnu (64-bit)
 
locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=C                 LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
 
attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     
 
other attached packages:
[1] limma_3.18.13
 
loaded via a namespace (and not attached):
[1] compiler_3.0.1 tools_3.0.1
 

 



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