Terrific, Ryan.  This was our thought as well and we are very grateful to have your expert advice.

Thank you,
Chris

On May 24, 2014, at 4:41 PM, Ryan <rct@thompsonclan.org<mailto:rct@thompsonclan.org>>
 wrote:

Hi Chris,

I think what you want to do here is normalize at the level of individuals. To that end, I would generate the full count matrix for each individual at the gene level (including all reads for each individual, not just ones that cover heterozygous loci) and use that to compute library sizes and normalization factors. Then I would propagate those library sizes and normalization factors to your allele count matrix. This will ensure that both alleles of each individual have the same normalization, and it will also ensure that all loci are normalized relative to the total RNA, which is not biased by where heterozygous alleles happen to occur.

-Ryan

On Sat May 24 14:10:25 2014, Christopher T Gregg wrote:

Hi,

We are examining the use of edgeR to analyze allele-specific count data from RNASeq experiments. In these studies, each biological replicate (n=18) has two columns: one with counts from the maternal allele and the other with counts from the paternal allele for each gene. Thus, the data is paired since these counts are parsed from the data for each each replicate. We wish to fit a glm to the data that tests for a main effect of the allele (counts ~ replicate + allele) to find genes that exhibit a significant allele expression bias.

My question relates to how to best handle the normalization of the counts in this case. EdgeR applies calcNormFactors to the columns, which disrupts the maternal:paternal count ratio for each gene in each sample. We are grateful for advice on how to best manage the analysis of this type of data.

best wishes,
Chris

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