Dear all, I am a PhD candidate in Beijing Institute of Genomics, Chinese Academy of Sciences. Recently I have been worked with data analysis concerning RNA capture followed by high throughput sequencing. Four samples, 2 cases and 2 controls, were used for sequencing. My library preparation protocol is similar to workflow of Exome capture, except for the material used for capture was cDNA and the capture library was customized probes synthesized by Agilent. After mapping, I want to do DEG analysis utilizing DESeq, and I found the gene number would affect the results given by DESeq. So my question is whether DESeq compatible with limited genes (83 candidate genes for my project)? And would you please give me some suggestions about DEG analysis concerning candidate genes' RNA-seq? or, for my project, I could just calculate RPKM value for each gene, and identify DEGs simply by fold change > 2? Thank you! Sincerely, Jianming SHAO [[alternative HTML version deleted]]

hi Jianming, How many of these 83 candidate genes do you expect to be differentially expressed? The problem is that: imagine that in the case samples, all 83 candidate genes are upregulated. Without some way of assessing the sequencing depth (i.e. normalization using spike-in controls) it would be impossible to tell apart differential expression from sequencing depth. Mike On Tue, Sep 3, 2013 at 10:35 AM, 邵建明 <jianmingshao1987@gmail.com> wrote: > Dear all, > I am a PhD candidate in Beijing Institute of Genomics, Chinese > Academy of Sciences. Recently I have been worked with data analysis > concerning RNA capture followed by high throughput sequencing. Four > samples, 2 cases and 2 controls, were used for sequencing. My library > preparation protocol is similar to workflow of Exome capture, except for > the material used for capture was cDNA and the capture library was > customized probes synthesized by Agilent. After mapping, I want to do DEG > analysis utilizing DESeq, and I found the gene number would affect the > results given by DESeq. So my question is whether DESeq compatible with > limited genes (83 candidate genes for my project)? And would you please > give me some suggestions about DEG analysis concerning candidate genes' > RNA-seq? or, for my project, I could just calculate RPKM value for each > gene, and identify DEGs simply by fold change > 2? Thank you! > Sincerely, > > Jianming SHAO > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]