Thanks very much Simon !!! Indeed, you are right, the experiment depth is very low in all the samples, they are going for a new sequencing round with higher depth. I hope results get better then. Anyway, your recommendations will help for future situations like this. best regards. osvaldo On Fri, Sep 13, 2013 at 11:32 AM, Simon Anders <anders@embl.de> wrote: > Hi Osvaldo > > Looking through the excerpt of your result list, it seems that your read > counts are very low. Most miRNAs seem to have less than 50 reads, so it > might well that you simply have not sequenced deeply enough. > > Have you enriched your library for short RNA molecules, or is this > standard RNA-Seq? For how many miRNA genes do you have counts, and how many > are above 50? > > For now, independent filtering might help: Exclude all the genes with less > than, say, 20 or 40 counts (according to baseMean), and run the > Benjamini-Hochberg adjustment only on the remaining ones. I think we have a > chapter on the vignette about this. > > Also double-check teh normalization with MA plots. With so small counts, > the default location measure (the median) might work subobtimal; maybe try > the shorth. (See ?estimateSizeFactors). > > Also consider switching to DESeq2, which has better inferential power due > to an improved dispersion estimation scheme. > > Simon > > > > On 13/09/13 10:15, Osvaldo Graña wrote: > >> hi there !! >> I am analyzing miRNA-seq data with DESeq, and I am getting no significant >> results, and I cannot see why is so. >> >> My experiment has two conditions and two replicates per condition: >> >> I execute it in R as follows: >> *countTable = read.table ( >> >> "/mnt/TB3/ograna/Ozge_Uluckan/**miRNA-seq/Analysis/tables_for_** >> DESeq/cOB1_vs_tOB100" >> , header=TRUE, row.names=1) >> experiment_design = data.frame( >> row.names = colnames(countTable), >> condition=c("OB1","OB1","**OB100","OB100"), >> libType=c("single-end","**single-end","single-end","**single-end") >> ) >> library("DESeq") >> condition=factor(c("OB1","OB1"**,"OB100","OB100")) >> cds = newCountDataSet( countTable, condition ) >> cds = estimateSizeFactors( cds ) >> normalizedReadCounts = counts( cds, normalized=TRUE ) >> write.csv( normalizedReadCounts, >> file="cOB1_vs_tOB100.DESeq_**normalizedReadCounts.csv" ) >> cds = estimateDispersions( cds ) >> res = nbinomTest( cds, "OB1", "OB100" ) >> write.csv( res, file="cOB1_vs_tOB100.DESeq_**diffExp.csv" )* >> >> >> >> I am getting the following size factors: >> >>> sizeFactors(cds) >>> >> OB.1OU OB.2OU OB100.1OU OB100.2OU >> 1 1 1 1 >> >> >> the differential expression table sorted by 'padj' is as follows (I am >> showing just a small set): >> >> number id baseMean baseMeanA baseMeanB foldChange >> log2FoldChange pval padj >> *340 mmu-miR-204-5p 31.5 12.5 50.5 4.04 2.014355293 >> >> 6.466284630369E-005 0.122859408 >> 69 mmu-miR-1247-5p 57.5 32.5 82.5 2.5384615385 >> 1.3439544012 0.0010710548 1 >> 170 mmu-miR-15b-3p 99.25 135.5 63 0.4649446494 >> -1.1048691179 0.0024355731 1 >> 363 mmu-miR-214-3p 591.25 767 415.5 0.5417209909 >> -0.8843781007 0.0040302141 1 >> 1122 mmu-miR-664-3p 55.5 76 35 0.4605263158 >> -1.1186444965 0.0065510324 1 >> 630 mmu-miR-335-3p 9.5 15.5 3.5 0.2258064516 >> -2.1468413883 0.0093262341 1 >> 997 mmu-miR-574-3p 282 364.5 199.5 0.5473251029 >> -0.8695300681 0.0101138488 1 >> 176 mmu-miR-17-3p 29 41 17 0.4146341463 -1.2700891634 >> 0.0109905451 1 >> 1898 mmu-miR-99a-5p 1254.5 1568.5 940.5 0.5996174689 >> -0.7378856803 0.0140500521 1 >> 846 mmu-miR-467a-5p 11 17 5 0.2941176471 -1.7655347464 >> 0.019028208 1 >> 1900 mmu-miR-99b-5p 6246 7702.5 4789.5 0.6218111003 >> -0.6854517236 0.0204136071 1* >> >> >> >> There is no even one significant miRNA. Is it that I am missing something >> or doing something wrong? What does it mean that all the 'padj' values are >> '1' with the exception of the first one '0.122' ? >> Is there a way to change the method used to correct the p-values? >> >> thanks very much in advance !!! >> regards. >> >> >> >> ______________________________**_________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.e="" thz.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=""> >> >> > ______________________________**_________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.et="" hz.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=""> > -- Osvaldo Graña Bionformatics Unit, Structural Biology and Biocomputing Programme Spanish National Cancer Research Centre (CNIO) Melchor Fernández Almagro, 3 - 28029 Madrid +34 91 732 8000 (ext 3062) http://bioinfo.cnio.es www.cnio.es) <ograna@cnio.es> [[alternative HTML version deleted]]