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 > >

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 > >