On Thu, Jun 16, 2011 at 11:23 AM, Kevin R. Coombes <kevin.r.coombes at="" gmail.com=""> wrote: > If you filter the genes after performing the t-test, then I will not believe > the results. ?Filtering based on any criteria that knows how much the genes > differ between the two groups being contrasted (fold change, p-value, etc.) > is statistically and scientifically invalid. > > People have made (and continue to make) the argument that it is > safe/sound/reasonable to filter on criteria that do not rely on the results > of the statistical test. examples of these kinds of filters are ones that > look at the mean (max or some percentile) of the gene expression across the > entire data set, or at the variance or range across the entire data set. > > If you have no differential expression, then filtering is not going to > magically create it for you. ?I would advise one of the following options > [1] Rank the genes by the p-value or t-statistic (possibly filtered by fold > change) and perform PCR on the top ten to see if any of them can actualy be > confirmed. > [2] Run more arrays so you have enough replicates to provide adequate power > to discover smaller differences in expression than you can expect to find > with only two replicates per group. Kevin and I agree on these points. I would add a third option which is to use GSEA-like ideas or gene set tests to look for a signal of differential expression in larger gene sets. Sean > ? ?Kevin > > On 6/16/2011 9:17 AM, Stephanie PIERSON wrote: >> >> Dear bioconductor listers, >> >> I am analyzing agilent 2 color microarray data and i choose limma library >> to make normalization and statistical analysis because i only have 2 >> replicates per condition and i read in some paper that a moderated t test >> perform better when there are few replicates. >> >> The problem is that when i performed the statistical test on the whole >> data set ( 35000 probes ),i have no differential expression, ie, all the >> adjusted p value are comprise between 0.5 and 0.9. So, i have seen on the >> list that the question on prefiltering genes have already been asked : some >> people on the list recommand to do the normalization, model fitting, etc, >> and then filter out before doing the multiplicity adjustment. >> So, after the statistical analysis, i remove gene with log2FC<2 >> (ebayes$coefficients), and i perform the FDR. But once again, i have no adj >> pvalue < 0.05. >> >> So, i was wondering on wich criteria i could filter out genes before the >> multiple testing correction : pvalue ? log2FC ? other criteria ? >> >> I have a lot of variabily between replicates, ie, for many genes, i have a >> fold change <0 in one replicate (for example, -5) and >0 on the other one >> replicate (for example, 3) ... do you think i should remove those gene >> before the statistical analysis or i can keep them ? >> >> >> Thank you, >> Best wishes >> St?phanie >> >> >> > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >

2011/6/16 St?phanie <stephanie.pierson at="" etumel.univmed.fr="">: > Hi, Sean, > Thanks for your quickly reply ! > > When you say "variance across ALL samples" you mean filter gene according to > IQR ? Is there a function that permit me "to pick the top X% of the genes > with the highest variance" ? The genefilter package is a good place to look for gene filtering. > unfortunately, i'm student in a lab and it's impossible to enlarge sample > size ... That is really too bad, but all-too-common. Sean