Cuffdiff is known to be very stringent, especially if the variance within your groups is quite high. If you are accustomed to R, then you can try to use methods such as DESeq or edgeR for differential expression analysis, otherwise, just disregard Cuffdiff's FDR and set thresholds for fold change and overall expression and see how far you get (though I would not recommend that).
I also recently read about another RNAseq pipeline called Grape, maybe that helps you further. See http://bioinformatics.oxfordjournals.../29/5/614.long
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Cuffdiff significance
Hi,
I have 3 sequenced transgenic Arabidopsis genomes (sequenced sing Tuxedo suite). I am looking for genes that exhibit significant differential expression in all 3 transgenic lines and ideally do not show significant differential expression in our WT control lines. When I use only genes that have an FDR<0.05 I get virtually no interesting genes (i.e., unknown proteins), but when I arbitrarily choose a fold change of 2 (chosen by my professor) and ignore the FDR, I get quite a few genes that are interesting to my professor.
Is there a way to justify using these genes even though cuffdiff says that the expression is insignificant?
Please let me know if this is not information, etc. I am very new at this.Tags: None
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