[BioC] outlier removal from gene chip

[Weiwei Shi]

hi, Sean:

I added some info here:

I did some pathway analysis and compare the results between using
those "outliers" and not using them. My result (validated by domain
knowledge, since they are unsupervised learning) shows the former is
better, which agrees with your suggestion. but i still do not think
the one with -14k and some numbers shown in the summary in the first
email make sense to me.

weiwei

On 9/19/06, Weiwei Shi <helprhelp at gmail.com> wrote:
> my current way is using mahalanobis() distance.
>
> to Sean:
> do u think that example: -14k is ok?
>
>
> On 9/19/06, fhong at salk.edu <fhong at salk.edu> wrote:
> > Dear Weiwei,
> > The definition of outlier is not clear, and no data point should be
> > treated as outlier unless there is reason to believe so. The simple way to
> > detect it is that 1.5IQR criteria, which you can write your own code (one
> > or two lines). Update me if there are any other method to detect outliers.
> >
> > Fangxin
> >
> >
> > > dear listers:
> > >
> > > I have a question on whether bioconductor has some tool-kit to detect
> > > outliers and remove them.
> > >
> > > my original dataset looks like this:
> > >             V1       V51       V53        V55       V57
> > > 1   -493249600  1.459459 -3.069444  -1.300000  1.935484
> > > 2  -1613096495 -1.139269 -5.525281 -16.592593 -1.831978
> > > 3   1626196571 -3.500000 -1.011662   2.223881  3.921053
> > > 4  -1397009217 -3.571429  1.685714  -1.180297 -6.807692
> > > 5   1428659728 -1.405405 -1.469004  -4.779754 -1.033708
> > > 6    459853658 -2.158879 -7.510823  -1.085581 -9.382979
> > > 7    530182506 -1.431677 -1.336343  -3.126437  4.878788
> > > 8   1173842263  1.215385  1.856410  -2.059794 -6.020833
> > > 9        28847  2.407895 -2.048889  -1.730337 -1.178947
> > > 10 -1961875610  2.864159 -2.301234  -4.733264 -1.172058
> > >
> > > V1: internal probe id
> > > the rests are different samples. the cells are fold-change of
> > > disease/normal.
> > >
> > > summary of the sample columns( V51, ... V57) gives the following:
> > >       V51                V53                 V55                V57
> > >  Min.   :-482.000   Min.   : -55.7342   Min.   :-122.074   Min.
> > > :-14086.750
> > >  1st Qu.:  -2.159   1st Qu.:  -1.7312   1st Qu.:  -2.125   1st Qu.:
> > > -1.831
> > >  Median :  -1.199   Median :  -1.0416   Median :  -1.200   Median :
> > > -1.080
> > >  Mean   :  -0.918   Mean   :   0.1662   Mean   :  -1.027   Mean   :
> > > -1.874
> > >  3rd Qu.:   1.441   3rd Qu.:   1.5721   3rd Qu.:   1.419   3rd Qu.:
> > > 1.521
> > >  Max.   : 198.434   Max.   :1478.1639   Max.   :  95.768   Max.   :
> > > 683.519
> > >
> > >
> > > My question is, is there any package which can detect those outliers
> > > (like -14086.750)and remove them and get an "average" for each gene
> > > (instead of each probe)?
> > >
> > > Thank you.
> > >
> > > Weiwei
> > >
> > > --
> > > Weiwei Shi, Ph.D
> > > Research Scientist
> > > GeneGO, Inc.
> > >
> > > "Did you always know?"
> > > "No, I did not. But I believed..."
> > > ---Matrix III
> > >
> > > _______________________________________________
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> > > Bioconductor at stat.math.ethz.ch
> > > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > > Search the archives:
> > > http://news.gmane.org/gmane.science.biology.informatics.conductor
> > >
> > >
> >
> >
> > --------------------
> > Fangxin Hong  Ph.D.
> > Plant Biology Laboratory
> > The Salk Institute
> > 10010 N. Torrey Pines Rd.
> > La Jolla, CA 92037
> > E-mail: fhong at salk.edu
> > (Phone): 858-453-4100 ext 1105
> >
> >
>
>
> --
> Weiwei Shi, Ph.D
> Research Scientist
> GeneGO, Inc.
>
> "Did you always know?"
> "No, I did not. But I believed..."
> ---Matrix III
>


-- 
Weiwei Shi, Ph.D
Research Scientist
GeneGO, Inc.

"Did you always know?"
"No, I did not. But I believed..."
---Matrix III