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@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@r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>

	[[alternative HTML version deleted]]

