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 > [[alternative HTML version deleted]]