Hi Robert,

Thank you for asking. I am sorry if the two methods caused confusing.

You are right about the understanding of the "roast" paper and "romer" in
your first paragraph.

The null hypothesis in "roast" is for the focused gene sets as demonstrated
in the real data example of the "roast" paper. "mroast" function in limma
can test multiple gene sets at the same time. Essentially "mroast" is doing
the same as you run a series of "roast" followed by multiple testing
adjustments for p values. "mroast" works in a more computing efficiently way
than a series of "roast".

The null hypothesis in roast is equivalent as the self-contained hypothesis
discussed by Goeman and Bühlmann (2007)
http://www.ncbi.nlm.nih.gov/pubmed/17303618
Bioinformatics. 2007 Apr 15;23(8):980-7

>From my point of view, GSEA tests the combbined hypothesis, as it uses rank
(a test score for competitive test, relative to other genes)  and sample
labeling permutations (which generates the null distribution under the
self-contained hypothesis, "significantly associated with a phenotypic
comparison").

Roast is can handle both small sample size data as well as larger samples
size data. Sample size is a concern for self-contained hypothesis (focus
set).

In your case, I think romer is more suitable. Let me know if you have more
questions regarding these two functions in limma.

I am still using my email address in Monash in the mailing list though I am
in WEHI now.

Cheers,
Di

On Wed, Jul 14, 2010 at 12:58 AM, Robert M. Flight <rflight79@gmail.com>wrote:

> Hi All,
>
> I am having trouble with the distinction between the functions "roast"
> and "romer" in the limma package. From the publication describing
> "roast" (http://dx.doi.org/10.1093/bioinformatics/btq401), it seems
> that it tests a particular gene set for differential expression,
> whereas "romer" tests a battery of sets to find those that are
> differentially expressed compared to the rest?
>
> I am really having trouble discerning the true difference between
> these two, and how they compare to GSEA. I always thoght that the
> primary purpose of GSEA was to determine those gene sets that are
> significantly associated with a phenotypic comparison, i.e. those gene
> sets showing differential expression.
>
> If any one can help me clear this up, that would be great, because as
> of now I am thoroughly confused. To me, if I have a dataset, and I
> want to know which gene sets (from say MSigDB) are differentially
> expressed, then it sounds like I would use "roast", but the way it is
> described in the publication (and the help in limma), this isn't what
> I would do, but rather I should use "romer", and see if any of the
> sets show differential expression compared to the rest in the
> database.
>
> Color me confused,
>
> -Robert
>
> Robert M. Flight, Ph.D.
> Bioinformatics and Biomedical Computing Laboratory
> University of Louisville
> Louisville, KY
>
> PH 502-852-0467
> EM robert.flight@louisville.edu
> EM rflight79@gmail.com
>
> Williams and Holland's Law:
>        If enough data is collected, anything may be proven by
> statistical methods.
>
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