Thanks very much for that detailed information James. I did do some pathway
analysis using fishers exact test but I now understand that I can only
filter out the original list when I subject it to further advanced
analysis. It may not work in my favor if I were to reduce some data at the
first stage itself.

Your reply helps to a great deal. Highly appreciate it.

Best regards,
Ekta


On 3 December 2013 21:45, James W. MacDonald <jmacdon@uw.edu> wrote:

> Hi EJ,
>
> This is the time to revisit the original hypothesis you are testing, or if
> you are trying to generate hypotheses, then what hypotheses you would find
> interesting.
>
> In general, a set of differentially expressed genes isn't particularly
> compelling, especially when you start to think about Biological relevance
> and measurement errors. For example, it's pretty easy to come up with a
> single gene that appears to be differentially expressed, but is in fact a
> false positive. And what does a single differentially expressed gene (if a
> true positive) mean in the greater Biological context of your experiment?
>
> So univariate statistics aren't that useful IMO in this context, and
> trying to reduce your set of genes to a tractable number that you can
> eyeballometrically test for 'interestingness' is likely not the way to go.
> In other words, taking a list of genes and scanning them to see if you can
> find particular genes that you already know are implicated in the process
> you are examining is not likely to be a useful exercise.
>
> Instead, you might re-consider the rationale for doing this experiment and
> see if there is something you can do to further that cause. As an example,
> perhaps you are trying to find pathways that are perturbed by some
> treatment. There are any number of ways to try to tease that sort of
> information out; you can do GO hypergeometric tests (or KEGG tests if you
> prefer). Or you could look for interesting gene sets at the Broad, and do
> GSEA against those. Or maybe you want to mine the data for interesting
> relationships using something like WGCNA (a google search will get you
> there). Note that WGCNA is particularly useful if you have other
> (preferably continuous) phenotypes.
>
> Given that you apparently have a big signal here, I would recommend trying
> to use that signal rather than chopping away at your data simply to reduce
> the dimensionality.
>
> Best,
>
> Jim
>
>
> On Tuesday, December 03, 2013 10:39:05 AM, Ekta Jain wrote:
>
>> Thank you James. I actually meant to ask that if I have a list of 500
>> genes and they all have fold change values in the range of 2-3, If i
>> condense the list based on cut off value of say 2.8 fold change I
>> still have a list of 200 some genes. How could I eliminate other genes
>> to include more parameters in the threshold to define them as showing
>> differential expression. I am not sure if there is any such thing but
>> I suppose there might some definition which helps to identify genes as
>> a perfect candidate for being differentially expressed.
>>
>> Appreciate your help,
>>
>> EJ
>>
>>
>> On 3 December 2013 20:42, James W. MacDonald <jmacdon@uw.edu
>> <mailto:jmacdon@uw.edu>> wrote:
>>
>>     Hi EJ,
>>
>>
>>     On Tuesday, December 03, 2013 10:04:01 AM, EJ [guest] wrote:
>>
>>
>>         Dear All,
>>         I used LIMMA for a dataset on Human plus 2.0 arrayto get fold
>>         change values for differentially expressed genes. I have a
>>         long list of 500 some genes with fold changes > 2 from the
>>         topTable function. How can I select genes which are most
>>         differentially expressed from this list?
>>
>>
>>     You will have to first define what you mean by 'most
>>     differentially expressed'. If you mean 'largest fold change' then
>>     please see ?topTable, specifically the sort.by <http://sort.by>
>>
>>     argument.
>>
>>     Best,
>>
>>     Jim
>>
>>
>>
>>         Thank you,
>>         EJ
>>
>>           -- output of sessionInfo():
>>
>>         R version 3.0.1 (2013-05-16)
>>         Platform: i386-w64-mingw32/i386 (32-bit)
>>
>>         locale:
>>         [1] LC_COLLATE=English_India.1252  LC_CTYPE=English_India.1252
>>         [3] LC_MONETARY=English_India.1252 LC_NUMERIC=C
>>         [5] LC_TIME=English_India.1252
>>
>>         attached base packages:
>>         [1] parallel  stats     graphics  grDevices utils     datasets
>>          methods
>>         [8] base
>>
>>         other attached packages:
>>         [1] limma_3.16.7       affy_1.38.1        Biobase_2.20.1
>>         BiocGenerics_0.6.0
>>
>>         loaded via a namespace (and not attached):
>>         [1] affyio_1.28.0         BiocInstaller_1.10.3
>>          preprocessCore_1.22.0
>>         [4] zlibbioc_1.6.0
>>
>>         --
>>         Sent via the guest posting facility at bioconductor.org
>>         <http://bioconductor.org>.
>>
>>         _________________________________________________
>>         Bioconductor mailing list
>>         Bioconductor@r-project.org <mailto:Bioconductor@r-project.org>
>>         https://stat.ethz.ch/mailman/__listinfo/bioconductor
>>
>>         <https://stat.ethz.ch/mailman/listinfo/bioconductor>
>>         Search the archives:
>>         http://news.gmane.org/gmane.__science.biology.informatics.__
>> conductor
>>
>>         <http://news.gmane.org/gmane.science.biology.informatics.
>> conductor>
>>
>>
>>     --
>>     James W. MacDonald, M.S.
>>     Biostatistician
>>     University of Washington
>>     Environmental and Occupational Health Sciences
>>     4225 Roosevelt Way NE, # 100
>>     Seattle WA 98105-6099
>>
>>
>>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>

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