[BioC] Finding similarities and differences for more than one catergory

[Sean Davis]

On Thursday 14 September 2006 07:11, Daniel Brewer wrote:
> Hi,
>
> I have results from a series of 2-colour microarray experiements that
> compare reference RNA to RNA from cells that fall into 4 catergories:
> Cancer CD133+
> Normal CD133+
> Cancer CD133-
> Normal CD133-
>
> What I would like to find genes that are:
> 1) Significantly different from the reference RNA
> AND
> 2) either (in both CD133+/- seperately)
> 	i) significantly different between cancer  and normal
> 	or ii) significantly _similar_ between cancer and normal
>
> I have been thinking of using the following strategy:
> 1) Treat CD133+ and - results separately
> 2) Use results from lmFit to filter out genes that are not significantly
>  different from reference RNA in BOTH Cancer and normal
> 3) Perform a t-test between cancer and normal results and take genes
> with p>0.05 as significantly different and p>0.95 as significantly similar.

There is not a good test to show that a gene is "unchanged" between two 
groups, so point 2.ii doesn't really make sense.  In hypothesis testing 
terms, using t-tests or the like allow you to "regect the null hypothesis" 
with a given amount of certainty.  However, NOT rejecting the null hypothesis 
(of differential expression) is NOT the same as proving the null hypothesis, 
no matter how non-significant the p-values are.

> Is this a reasonable approach or would it be better to use ANOVA or
> regression analysis.  To add to the complexity at some point I would
> also like to compare the CD133+/- samples

Using all the data simultaneously is the better way to go, so use limma (or 
some other package) to treat the data as the two-factor experiment that it 
is.  

Sean