[BioC] (no subject)

Simon Anders anders at embl.de
Fri Mar 8 17:13:20 CET 2013

Dear Leo

On 08/03/13 16:54, Liu, XiaoChuan wrote:
> 1.      What is the meaning when I use “count ~ 1”? Here 1 is a
> cut-off? Or other meaning? I saw you give an example like this in
> DESeq Reference Manual. So I try to follow using it. But I do not
> know the meaning for test.

No, "~ 1" means that no factors except for the intercept should be used 
in the model. This formula notation is not specific to DESeq, it is a 
part of R and hence discussed in most textbooks covering R. (If you are 
unfamiliar with linear models and the associated concepts, the books by 
Dobson or Dalgaard might be a useful read.)

> 2.      In Part 2, I did 6 different GLMs tests by DESeq. And I also
> do the overlap amongst results. The overlap sometimes is very small.
> Why are they so different? How to explain it? Could you give me some
> comments?

Sorry, I don't understand the question. You perform tests for different 
hypotheses and are surprised to get different results?

You will need to tell us more specifically what biological hypotheses 
you wish to test, and then we can maybe advise you how to formulate this 
as a linear model.

> 3.      In Part 3, I also do the overlap with 6 results in Part 2.
> But the overlap are very small. I wonder if I make a mistake to
> misuse the variance stabilizing transformation? If I want to directly
> use the ANOVA function in R to calculate co-factor P-value, could I
> use the raw count? Or How to normalize the raw counts then I can use
> ANOVA function in R?

No, ordinary-least-square (OLS) ANOVA requires data to be homoscedastic 
and this count data is not. This is, after all, the whole point of 
either using GLMs on the raw count data, or OLS ANOVA on 
variance-stabilized data.

I would have expected some similarity between the results of a GLM 
ANODEV anaysis of the count data and the OLS ANOVA analysis on the vST 
data, but as you did not post the code you used, it is hard to say 
whether you may have made a mistake.


More information about the Bioconductor mailing list