Hi Jenny,

Thank you for your explanation, as a biologist who started doing analysis by
R, my experimental plan is to select the a few top genes based on the p.
value and to evaluate using PCR. I completely agree with you that arrays may
not represent the true biology, but I feel that I should not go wrong in the
statical analysis.

Regards,
Abhilash

On Wed, Jul 16, 2008 at 8:31 PM, Jenny Drnevich <drnevich@illinois.edu>
wrote:

> At 09:14 AM 7/16/2008, Abhilash Venu wrote:
>
>> Hi Sean,
>> Thank you for sharing the thoughts.
>> I have done the filtering, using the same code prior to the normalization,
>> and it started to show some changes. I am providing the topTable results,
>> the odds ratios started to show the positive change but still adj.P.Val is
>> showing little higher, So in this scenario, whether I should do more
>> stringent filtering before the analysis?
>>
>
> Hi Abhilash,
>
> As Sean said before, the goal of data pre-processing and filtering should
> not be to *get* the results you want, but rather to arrive at the most
> _correct_ results given the type of data that is generated. It's a big
> statistical no-no to try several different analysis methods and then pick
> the one that gives you the results you like best. I'm not sure why you tried
> filtering before doing normalization when you were already told that it's
> supposed to be done after normalization. I know it's frustrating to not have
> any "significant" genes, especially when you know there are expression
> changes due to the treatment. Remember that a FDR level of 0.05 is not a
> magical threshold of significance, rather the amount of false positives YOU
> are willing to tolerate in your gene list.  I've seen papers where they've
> used gene lists with 0.1 or even 0.2 FDR thresholds. Another route is to
> just use the top 50 or 100 genes, as these have the most evidence for DE,
> even if they don't surpass any reasonable FDR adjustment.
>
> Finally, remember that Affy arrays, and many other methods of expression
> measurement, are only measuring a tiny portion of the expected transcript.
> There are many known cases in which "expression" differences won't be
> reflected in that portion of the transcript. In these cases, the microarray
> data are "correct", even if they aren't telling you the entire story...
>
> Best,
> Jenny
>
>
>
>  GeneName            logFC        AveExpr         t         P.Value
>>  adj.P.Val        B
>> NUDT16L1          2.7559164  14.32567  10.098560  1.520399e-07
>> 0.0065018      4.829862
>> MGC4268          1.5820444   12.06414   7.695917   3.280927e-06
>> 0.061246       3.208160
>> AR                    1.7511488    10.19825   7.506490   4.296601e-06
>> 0.0612466       3.048297
>> LOC124220        0.9476445   15.51240   6.697382  1.431390e-05
>> 0.1530298      2.302016
>> A_24_P289130   1.7622555   11.07025    6.401121  2.272696e-05
>> 0.156454      2.001432
>> ZNF501              1.804305     10.69845  6.345654    2.481481e-05
>> 0.156454      1.943447
>> ADAM22             -1.650837  11.89608  -6.187425   3.195991e-05
>> 0.156454      1.77502        THC2351317        1.0793141  12.34347
>> 6.179724   3.235878e-05    0.156454      1.766717
>> AW276332          1.8253290  10.55792   6.147119   3.410664e-05
>> 0.1564544    1.731409
>> THC2323609       2.0122396  10.82117    6.076649    3.823291e-05
>> 0.15645     1.654439
>>
>>
>> Regards
>> Abhilash
>>
>> On Sat, Jul 12, 2008 at 10:32 PM, Sean Davis <sdavis2@mail.nih.gov>
>> wrote:
>>
>> > On Sat, Jul 12, 2008 at 11:26 AM, Abhilash Venu <abhivenu@gmail.com>
>> > wrote:
>> > > Hi Sean,
>> > >
>> > > Yes, thank you.
>> > >
>> > > Yet my problem of the data did not get sorted out. I have tried
>> different
>> > > filtering methods including gapfilter and a combination of IQR with
>> > pOverA
>> > > or cv etc. But my adj p values are above the FDR limit of 0.05 after
>> the
>> > > limma analysis. Also B values are generally -3.  As Gorden has
>> mentioned
>> > in
>> > > one of the previous mails, this is a indication of little evidance for
>> > > differential expression.
>> > >
>> > > What could be the reason for this. Is this really an indicative of
>> > absence
>> > > of differential expression?
>> >
>> > It sounds like it.  Though people think of filtering as a way to
>> > reduce the number of genes and improve the strength of signal after
>> > multiple-testing correction, I don't think that is the correct
>> > mindset.  Filtering is useful to remove probes from analysis that are
>> > not measuring anything interesting (no change across experiments) or
>> > are not well-measured.  So, the thought process should not be to do
>> > hypothesis testing and then, if negative, to do filtering to try to
>> > improve the situation, but to do filtering based on rational
>> > thresholds for removing uninteresting or less-than-credible values as
>> > part of a series of preprocessing steps.
>> >
>> > Sean
>> >
>> > > On Fri, Jul 11, 2008 at 4:17 PM, Sean Davis <sdavis2@mail.nih.gov>
>> > wrote:
>> > >
>> > >> On Fri, Jul 11, 2008 at 5:32 AM, Abhilash Venu <abhivenu@gmail.com>
>> > wrote:
>> > >> > Dear Dr. Huber,
>> > >> >
>> > >> > Thank you for the advice. I have tried the script that you have
>> > advised
>> > >> to
>> > >> > use. As you mentioned I have used the script after the
>> normalization,
>> > but
>> > >> > that has shown the following error, which I do not understand,
>> whether
>> > I
>> > >> am
>> > >> > using in the right way.
>> > >> >
>> > >> > MA<-normalizeBetweenArrays(log2(Rgene$G), method="quantile")#
>> > >> normalization
>> > >> >  rs = rowSds(MA)
>> > >> >  fx = fx[ rs > quantile(rs, 0.05), ]
>> > >> > Error: object "fx" not found
>> > >>
>> > >> Hi, Abhilash.  I think that line should read:
>> > >>
>> > >> fx = x[rs > quantile(rs,0.05),]
>> > >>
>> > >> Wolfgang was simply suggesting subsetting x by the results of sd
>> > filtering.
>> > >>
>> > >> Sean
>> > >>
>> > >> > Can you advise me on the same.
>> > >> > Thanks in advance.
>> > >> >
>> > >> > Abhilash
>> > >> >
>> > >> > On Fri, Jul 11, 2008 at 4:06 AM, Wolfgang Huber <huber@ebi.ac.uk>
>> > wrote:
>> > >> >
>> > >> >> Hi Abhilash
>> > >> >>
>> > >> >>
>> > >> >>  I am working with single color data from Agilent platform. After
>> the
>> > >> limma
>> > >> >>> analysis the adjusted p values were higher than 5% of FDR. At
>> this
>> > >> >>> instance
>> > >> >>> I am thinking of filtering the genes using genefilter. As my data
>> > set
>> > >> >>> contains only raw intensities of normal and test before the
>> > >> normalization,
>> > >> >>> where I am uisng 'normalizeBetweenArrays' command after log
>> > >> transforming
>> > >> >>> the
>> > >> >>> data.
>> > >> >>> In this scenario I am quite confused whether I should use the
>> filter
>> > >> >>> functions prior to normalization of after the normalization but
>> > efore
>> > >> >>> fitting the linear model?
>> > >> >>> As my data is not an expressionSet I cannot use the nonfilter
>> > commands,
>> > >> in
>> > >> >>> this case any suggestions of using other filtering methods?
>> > >> >>>
>> > >> >>> Appreciate the suggestions
>> > >> >>>
>> > >> >>>
>> > >> >> Such filtering is performed after normalisation, but it is
>> essential
>> > >> that
>> > >> >> the filter criterion does *not use the sample annotations*. E.g.
>> you
>> > can
>> > >> use
>> > >> >> for each gene the overall variance or IQR across the experiment.
>> > >> >>
>> > >> >> If x is a matrix with rows=genes and columns=samples, then this
>> can
>> > be
>> > >> as
>> > >> >> simple as:
>> > >> >>
>> > >> >>  rs = rowSds(x)
>> > >> >>  fx = fx[ rs > quantile(rs, lambda), ]
>> > >> >>
>> > >> >> where rowSds is in the genefilter package, and lambda is a
>> parameter
>> > >> >> between 0 and 1 that contains your belief in what fraction of
>> probes
>> > on
>> > >> the
>> > >> >> array correspond to target molecules that are never expressed in
>> the
>> > >> >> conditions you study.
>> > >> >>
>> > >> >> Also note that after such filtering, strictly speaking, the
>> nominal
>> > >> >> p-values from the subsequent testing could be too small - but one
>> can
>> > >> show
>> > >> >> that in typical microarray applications the bias is negligible
>> > (compared
>> > >> to
>> > >> >> the impact of other effects), and in any case the p-values can be
>> > used
>> > >> for
>> > >> >> ranking.
>> > >> >>
>> > >> >>  Best wishes
>> > >> >>        Wolfgang
>> > >> >>
>> > >> >>
>> > >> >> --
>> > >> >> ----------------------------------------------------
>> > >> >> Wolfgang Huber, EMBL-EBI, http://www.ebi.ac.uk/huber
>> > >> >>
>> > >> >
>> > >> >
>> > >> >
>> > >> > --
>> > >> >
>> > >> > Regards,
>> > >> > Abhilash
>> > >> >
>> > >> >        [[alternative HTML version deleted]]
>> > >> >
>> > >> > _______________________________________________
>> > >> > Bioconductor mailing list
>> > >> > Bioconductor@stat.math.ethz.ch
>> > >> > https://stat.ethz.ch/mailman/listinfo/bioconductor
>> > >> > Search the archives:
>> > >> http://news.gmane.org/gmane.science.biology.informatics.conductor
>> > >> >
>> > >>
>> > >
>> > >
>> > >
>> > > --
>> > >
>> > > Regards,
>> > > Abhilash
>> > >
>> > >        [[alternative HTML version deleted]]
>> > >
>> > > _______________________________________________
>> > > Bioconductor mailing list
>> > > Bioconductor@stat.math.ethz.ch
>> > > https://stat.ethz.ch/mailman/listinfo/bioconductor
>> > > Search the archives:
>> > http://news.gmane.org/gmane.science.biology.informatics.conductor
>> > >
>> >
>>
>>
>>
>> --
>>
>> Regards,
>> Abhilash
>>
>>        [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
>
> Jenny Drnevich, Ph.D.
>
> Functional Genomics Bioinformatics Specialist
> W.M. Keck Center for Comparative and Functional Genomics
> Roy J. Carver Biotechnology Center
> University of Illinois, Urbana-Champaign
>
> 330 ERML
> 1201 W. Gregory Dr.
> Urbana, IL 61801
> USA
>
> ph: 217-244-7355
> fax: 217-265-5066
> e-mail: drnevich@illinois.edu
>



-- 

Regards,
Abhilash

	[[alternative HTML version deleted]]

