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I'm using edgeR for differential analysis. Using glmTreat function I'm detecting differentially expressed genes between Tumor and Normal. I have set the arguments like below:

tr <- glmTreat(fit, contrast=contrast.matrix, lfc=2)
tab2 <- topTags(tr,n=Inf, adjust.method = "BH")
keep <- tab2$table$FDR <= 0.05

This gave me differentially expressed genes with logFC > 2 at FDR < 0.05 and genes with logFC < 2 at FDR < 0.05. [There are few genes with logFC 1.9, 1.89 also]

I have also tried with lfc=log2(2).

tr <- glmTreat(fit, contrast=contrast.matrix, lfc=log2(2))
tab2 <- topTags(tr,n=Inf, adjust.method = "BH")
keep <- tab2$table$FDR <= 0.05

To my surprise I see DE genes with logFC >=1, FDR < 0.05 and also logFC <=1 at FDR < 0.05.

Here is an example of DE genes I also found when I used lfc = log2(2)

GeneSymbols logFC   unshrunk.logFC  logCPM  PValue  FDR
DPYD-IT1    1.14493675  1.560389373 0.67468395  0.002972841 0.018704644
LINC00945   1.144288473 1.525191847 0.693845996 0.00651175  0.038064312
ITPKB-AS1   1.14046752  1.484991704 0.703887505 0.006620218 0.038585862
AC105206.1  1.122495266 1.4871905   0.699167758 0.006545299 0.038194633
LINC02066   1.120660287 1.558458881 0.66016102  0.004344743 0.026424298
AC090985.1  1.10235045  1.58968763  0.64343419  0.002745089 0.017426607
KCNJ6-AS1   1.100399688 1.717028637 0.613324799 0.002224039 0.01438451
LINC01494   1.090270138 1.490899364 0.676937984 0.006770608 0.039348027
AC099541.1  1.080957092 1.53817727  0.651356989 0.008449298 0.048356352
AL133396.2  1.012984207 1.512530231 0.63130643  0.008090259 0.046411928
AC011363.1  1.01207753  1.499708945 0.635740622 0.007226791 0.041797256

I'm very confused with these foldchange cutoffs.

Which one should I use to select differentially expressed genes based on fold change >=2 and FDR < 0.05?

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Fold-change >= 2 is the same as logFC (log2(fold-change)) >= 1, so your example is doing exactly what you want. logFC is generally easier to think in and work with than fold-change, since (aside from computational reasons) then increases and decreases in expression differ only in sign and are, thus, easier to compare in magnitude (e.g., it's easier to tell that logFCs of 1.5 and -1.5 are the same magnitude change than it is that 3 and 0.333 are the same magnitude change in the fold-change).

For prioritizing the results, typically you would then rank these filtered results by fold-change and relevance (either given background knowledge or for genes with known function).

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  • $\begingroup$ Thanks a lot for the clear explanation. I guess in my example lfc = 2 is wrong analysis in this case if I need Fold-change >= 2. $\endgroup$ – user3351523 Jun 20 '18 at 12:42

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