Hi Santana,

You might also try the sva function in the sva package. This function is
specifically designed to identify batch effects and other sources of
variation. PCA typically confounds any signal of interest with potential
batch effects, so may be somewhat deceiving, particularly if the batches
are not balanced across groups of interest.

Best,

Jeff

On Wed, Sep 5, 2012 at 5:35 PM, Wolfgang Huber <whuber@embl.de> wrote:

> Dear Santana
>
> you could try the arrayQualityMetrics function in the eponymous package,
> which produces PCA plots and other diagnostics and is helpful to detect
> batch effects.
>
> The function runs either on the AffyBatch object, or the normalised
> ExpressionSet; the former is more useful to understand how well the
> experiment worked, the latter, how well subsequent analyses might work.
>
>         Best wishes
>         Wolfgang
>
>
> Sep/5/12 3:10 PM, James W. MacDonald scripsit:
>
>  Hi Santana,
>>
>> On 9/5/2012 2:14 AM, Santana Sarma wrote:
>>
>>> Hi,
>>>
>>>
>>> How is it possible to judge whether there is any batch effect in two
>>> groups
>>> of Affymetrix .cel files ? I have got currently one Affybatch object by
>>> reading all the .cell files.
>>>
>>
>> There are several things you can look at. I find PCA plots very helpful
>> to look for batch effects. You might also look at density plots (hist()
>> function in affy) as well as boxplots. But IMO PCA is the most useful.
>>
>> Best,
>>
>> Jim
>>
>>
>>
>>>
>>> Being new to Affymetrix analysis, any advice/elaboration will be very
>>> helpful.
>>>
>>>
>>> Cheers,
>>>
>>> Santana
>>>
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>>>
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>>
>>
>
> --
> Best wishes
>         Wolfgang
>
> Wolfgang Huber
> EMBL
> http://www.embl.de/research/**units/genome_biology/huber<http://www.embl.de/research/units/genome_biology/huber>
>
>
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