Hi all, >Comparing expression b/w genes (as opposed to across conditions for >a given gene) is not a standard use case within edgeR (yet) and >there are a number of issues that should be dealt with if you want >to do this well. I'm sure others on the list have more experience >and can comment more on this, but it seems you need to get a very >good handle on gene length (can you always trust the annotation?) >and other biases that (e.g. GC content, positional biases) that may >introduce differences in read counts that are different from gene to gene. Following up on this idea of the problems in comparing expression levels between different genes using RNA-Seq data... over a year ago I scanned a bunch of articles on RNA-seq and RPKM, and one of them actually spiked in a bunch (at least dozens?) of genes at supposedly the same concentration. However, when they compared RPKM values, they found up to 4 FC differences between these genes with supposedly the same concentration! I thought to myself, "Cool!", but as I was actually searching for other information, I failed to note which paper it was. And now I can't find it again. Anyone know what the paper was, or know of other papers that have also tested between-gene expression levels? RNA-seq has been touted for it's ability to compare expression levels between genes, but as Mark indicated, it turns out there are a variety of issues that hinder these comparisons. Thanks so much! Jenny >That said, edgeR has fairly general routines to analyze count data, >if you can sort out these other factors. > >Cheers, >Mark > >On 2011-01-29, at 10:37 AM, Sridhara Gupta Kunjeti wrote: > > > Opps I am sorry there is a typo in the subject - expression > > Thanks, > > Sridhara > > > > > > > > On Fri, Jan 28, 2011 at 6:37 PM, Sridhara Gupta Kunjeti > > <sridhara at="" udel.edu="">wrote: > > > >> Hi All, > >> I am using RNA-seq to study the expression levels of gene globally and as > >> well as group of genes. Plotsmear have given me a meaningful > information. I > >> believe the estimated norm.factor is directly included in the > model to test > >> DE, but the values themselves are not modified. My question is > if I want to > >> check compare the expression between genes (within a group) what > values do I > >> need to consider for this analysis. Any help or suggestions will be highly > >> appreciated. > >> > >> Thanks in advance! > >> Sridhara > >> > >> > >> > >> -- > >> Sridhara G Kunjeti > >> PhD Candidate > >> University of Delaware > >> Department of Plant and Soil Science > >> email- sridhara at udel.edu > >> Ph: 832-566-0011 > >> > > > > > > > > -- > > Sridhara G Kunjeti > > PhD Candidate > > University of Delaware > > Department of Plant and Soil Science > > email- sridhara at udel.edu > > Ph: 832-566-0011 > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > 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 > >------------------------------ >Mark Robinson, PhD (Melb) >Epigenetics Laboratory, Garvan >Bioinformatics Division, WEHI >e: mrobinson at wehi.edu.au >e: m.robinson at garvan.org.au >p: +61 (0)3 9345 2628 >f: +61 (0)3 9347 0852 >------------------------------ > > >_____________________________________________________________________ _ >The information in this email is confidential and intend...{{dropped:6}} > >_______________________________________________ >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