It depends.  In CLL, almost all of the copy number changes are single 
copy gains or single copy losses (for different genes).  So the copy 
number data only has two levels, in which case correlation doesn't tell 
you very much and a one-sided t-test is better.

     Kevin

On 7/12/2012 12:18 AM, Alyaa Mahmoud wrote:
> wouldn't we try a correlation test better then ?
>
> On Wed, Jul 11, 2012 at 7:10 PM, Kevin R. Coombes 
> <kevin.r.coombes@gmail.com <mailto:kevin.r.coombes@gmail.com>> wrote:
>
>     I can easily think of a reason for a one-sided test.  Consider the
>     case where I have copy number data and gene expression data on a
>     bunch of samples, and I'd like to test whether changes in gene
>     expression go the same direction as changes in copy number.
>
>     On 7/11/2012 8:21 AM, James W. MacDonald wrote:
>
>         Hi Alyaa,
>
>         On 7/11/2012 2:59 AM, Alyaa Mahmoud wrote:
>
>             Hi All
>
>             Is there a way to determine expression status of a gene
>             (up, down) from
>             corrected p.values ?
>
>
>         No. The p-values are strictly between 0 and 1, so there is no
>         way to discern the sign of the underlying t-statistic. This
>         would not be true if you were doing a one-sided t-test, but I
>         can't imagine a use case for a one-sided t-test in the context
>         of microarray analysis.
>
>         Best,
>
>         Jim
>
>
>
>             Thanks a lot
>             Alyaa
>
>
>
>
>
> -- 
> Alyaa Mahmoud
>
> "Love all, trust a few, do wrong to none"- Shakespeare
>

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