No advice can be given on the basis of the information you have supplied,
and
some of the concepts you mention ("minimize these outliers") are worrisome.
>From a formal statistical perspective, testing for outliers can only be
conducted
in the context of a specific distributional model (see the monograph
Outliers in Statistical
Data by Barnett and Lewis for a host of algorithms; boxplot rules (there are
various) are
useful but informal.  A primary function is to alert users to observations
that "seem different"
but that may very well be valid and compatible with all other non-outlying
observations.

In some contexts, the "outliers" are scientifically the most interesting
observations, and
one would not want to "minimize" them.  In other contexts, the outliers
arise through
quality problems that can be identified in the data generation workflow, and
data analysis
needs to respond to the identified problems, not just to the observations
that happen to
be flagged as outliers.

Several packages for reasoning about outlyingness in microarray data at
various
stages are present in Bioconductor: mdqc and arrayMvout ... take a look at
these
and at the associated references.

On Thu, Apr 23, 2009 at 5:37 AM, Ashwin Kumar <ashwin.havoc@gmail.com>wrote:

> Hello group,
>
> We are working on microarray data analysis of 35 experiments with
> limma. We have used normexp technique as background correction, loess
> as within array nomalization and quantile as between array
> normalization. After doing quantile normalization I found large number
> of outliers (small circles)  with box plot analysis.I would like to
> know whether the outliers are relevent in data analysis should they be
> removed or can be ignored? Also if they are to be removed can any one
> please let me know how to minimize these outliers?
>
> Thankfully
>
>  A.Ashwin
>  Department Of Biotechnology
>  Manipal Life Science Center
>  Karnataka
>  INDIA
>
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>



-- 
Vincent Carey, PhD
Biostatistics, Channing Lab
617 525 2265

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