Dear Simon, I have a question regarding the analysis of small RNAseq data using DESeq. While counting the reads per loci I have weighted the reads by the reciprocal of the places to which the read maps. I was wondering whether it is still proper to use the negative binomial test implemented in DESeq (after rounding the expression estimates) to determine which loci are differentially expressed? Best regards, Vedran Franke

On Jun 19, 2013, at 8:44 pm, Vedran Franke <vfranke at="" bioinfo.hr=""> wrote: > > Dear Simon, > > I have a question regarding the analysis of small RNAseq data using DESeq. > While counting the reads per loci I have weighted the reads by the > reciprocal of the places to which the read maps. > I was wondering whether it is still proper to use the negative binomial > test implemented in DESeq (after rounding the expression estimates) to > determine which loci are differentially expressed? > Dear Vedran DESeq is not intended to work with such values. However, the issue has nothing to do directly with the DESeq2 method or software, but a lot with the fact that it does not make scientific sense to ask from your data more than they contain. - if you want to look for differential expression of loci, then you need measurements that probe these loci's expression in a specific way (e.g. unique mapping reads) - if do not have such an specificity, then you need to aggregate your 'loci' into equivalence classes of things that are not distinguishable by your assay, until you have specificity. Hope this helps to proceed Best wishes Wolfgang > Best regards, > > Vedran Franke > > _______________________________________________ > 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

Hi Vedran > I have a question regarding the analysis of small RNAseq data using DESeq. > While counting the reads per loci I have weighted the reads by the > reciprocal of the places to which the read maps. > I was wondering whether it is still proper to use the negative binomial > test implemented in DESeq (after rounding the expression estimates) to > determine which loci are differentially expressed? No, for two reason: 1. DESeq expects raw counts. Your weighting violates the assumptions behind the nehative-binomial model. 2. Imagine two of your loci are quite similar, such that most reads that map to locus A also map to locus B. Further, imagine that you compare treated samples to control ones, and locus A gets upregulated in response to the treatment while locus B is unaffected. With your method of summerizing the data, all the additional reads that the upregulated locus A produces in the treatment samples will also be counted for locus B, and hence, you will wrongly conclude that both loci react to the treatment. Note that the second issue is a problem not only to NB-based method, but rather shows that you approach is in general not suitable for differential expression analyses. Simon