Hi,
Thanks in advance for any help. I have the latest R software (2.13.1) and
edgeR software (2.8.4). I'm running into a problem where I estimate a common
dispersion parameter of 0.0001 and when I subsequently estimate tagwise
dispersions using the default prior.n = 10, the summary statistics are
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.001 0.001 0.001 0.001 0.001 0.022
ie, all estimates are 10 times larger than the common dispersion estimate.
Since the method is supposed to shrink toward the common value this seems a
little surprising. When I increase prior.n to a large number I expect the
tagwise estimates to all converge to the common dispersion, but as you might
guess from the table above it converges to 0.001 = 10*common.
The data comes from the bioconductor package "yeastRNASeq" and it appears
from the description of the data that the two samples in each group are
actually from sequencing the same extraction of mRNA, ie not biological and
not even really technical replicates. So the common dispersion should be
zero as the counts should follow the poisson.
I cannot explain the behavior of the estimates but I'm afraid it might be
something in the code so I'll include that below.
library(yeastRNASeq)
data( geneLevelData )
d <- DGEList( geneLevelData , group = c( rep( "Mutant" , 2 ) , rep( "Wild" ,
2 ) ) )
d <- calcNormFactors( d )
d <- d[rowSums(d$counts) >= 5, ]
d <- estimateCommonDisp( d )
d$common.dispersion
[1] 0.000101
d <- estimateTagwiseDisp( d , prior.n = 10 )
summary( d$tagwise.dispersion )
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.001 0.001 0.001 0.001 0.001 0.022
d <- estimateTagwiseDisp( d , prior.n = 1000 )
summary( d$tagwise.dispersion )
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.001 0.001 0.001 0.001 0.001 0.001
It could just be an oddity of the data set itself but I don't have enough
experience using edgeR across different RNA-Seq experiments to know how
these methods should behave.
Thanks,
Sean
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