[BioC] use Combat to adjust for hidden variables without knowing batch effect

[shirley zhang]

Hi Michael,

Many thanks for your great suggestions. They are very helpful.

Best,
Shirley

On Tue, Jul 16, 2013 at 11:56 PM, Michael Breen
<breenbioinformatics at gmail.com> wrote:
> Hi Shirley,
>
> It's often not recommended to batch correct without considerable evidence of
> a batch effect. (i.e. date, cohorts etc..)
>
> What is recommended is to proceed with various sorts of quality assessment
> to visualize potential batch effects. For example, we will often produce:
>
> -3D PCA plots wrapping 1, 2, 3, standard deviations around the data points
> -Hierarchical clustering using pearsons correlation
> (for each of these it helps to overlap a color scheme onto the potential
> batches to aid in visualizing)
> -Array to Array distance plots
>
> If you find no evidence of batches then skip the batch adjustment. If exists
> a potential effect, correct with Combat or SCAN and proceed with your
> analysis.
>
> Good luck,
>
> Michael
>
>
> On Mon, Jul 15, 2013 at 6:10 PM, shirley zhang <shirley0818 at gmail.com>
> wrote:
>>
>> I know if the batch effect is known. We can use Combat to adjust for
>> the batch effect.  However, if the batch effect is unknown, could I
>> still use Combat or SVA to adjust for some hidden variables? We know
>> that our blood samples  were NOT
>> drawn at the same time from individuals, and RNA were NOT extracted at
>> the same time.
>>
>> Many thanks,
>> Shirley
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>
>