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I am interested to perform differential expression on affymetrix gene expression data by using simpleaffy package. I use
significant <- pairwise.filter(results,min.exp=log2(10),min.exp.no=2, fc=log2(1.5), tt= 0.05)
to select the gene showing fold change of greater than 1.5 and having t.test of 0.05 or better. However i get the results with fold change less than 1.5 also. The results are shown below. Please let me know if i am going wrong . Help appreciated, thanks
rowname fc.significant. 1 1552264_a_at -1.0039565 2 1552283_s_at 2.5965465 3 1552291_at -2.1498897 4 1552316_a_at 2.0454884 5 1552365_at 3.0642035 6 1552378_s_at -0.7997985
Oh, a real mistake. Thanks for making out. But in simpleaffy they claim log2(1.5) as fold change greater than 1.5, how it is??.
The idea is that you want to filter to only keep genes that show support for a 1.5x change between conditions. The numbers that are consumed by
fc
are in log2 space, and so are the fold change values that are reported, so you convert a 1.5x change on the "natural scale" to log2 scale.