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Woo, Sangsoon
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@woo-sangsoon-4745
Last seen 7.1 years ago

Dear all,
I am working on a ChIP-Seq data set.
I want to compare two groups having only one sample each group. (no
replicates in both group)
I generated count matrix which element is the number of reads within
gene region for each data set.
I applied edgeR and DESeq methods for this comparison.
For this case,
1. edgeR uses Poisson by setting common.disp=1e-6 (zero).
2. DESeq still uses NB by assuming there is no difference b/w two
samples to estimate dispersion.
The results are
1. edgeR identifies many genes with very small p-values / adjusted
p-value when I used common.disp approach.
2. edgeR gives none significant genes with tagwise.disp option.
3. DESeq does not identify any significant gene.
I think that p-values of #2 and #3 are based on summing over all sums
of counts that have a probability less than the probability under the
null hypothesis of the observed sum of counts. But #1 is based on
Poisson distribution with very small variation than actual data.
Am I right?
Looking at the raw counts for top genes is not helpful because it is
just comparing two numbers.
Which package is better for the case without replicate based on your
experiences?
Thanks for your help in advance.
Sangsoon