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

Johnson, William Evan wej at bu.edu
Thu Jul 18 19:19:59 CEST 2013


Shirley, 

Michael has given you some good advice--definitely do these things. 

Also, one other thing to try is to apply SVA, and see if any of the surrogate variables seem to be correlated with your missing variables (maybe you have some idea which samples where collected together or at the same time?).

Hope this helps,

Evan



On Jul 18, 2013, at 4:00 AM, <bioconductor-request at r-project.org>
 wrote:

> 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
>> 



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