Hello,

I have RNA-seq data from 10 normal samples and 8 tumor samples, which I am using edgeR to analyze for differential expression (DE) between the tumors and the normals.  I have basically followed the workflow in the edgeR user's guide section 3.3.  It is known that there is a large RNA compositional bias in these normal tissue samples (i.e. the top 25 genes by raw counts account for 50-80% of the total reads), which is not present in the tumor samples, so normalization via edgeR's calcNormFactors() is presumably very important.  The results from the calcNormFactors() is printed below with anonymous samples.

                                 group                   lib.size                  norm.factors
Sample1               Normals               136765371           1.0567240
Sample2               Normals               116803340           0.5898912
Sample3               Normals               88783007             0.5880073
Sample4               Normals               314426955           0.6871909
Sample5               Normals               289961788           0.5574136
Sample6               Normals               296455983           0.3413478
Sample7               Normals               260923863            0.7353922
Sample8               Normals               118870482           0.7742314
Sample9               Normals               237556345           0.5113664
Sample10            Normals               126493394           0.3916818
Sample11            Tumors                 90611059             1.7934781
Sample12            Tumors                 93423641             2.0290747
Sample13            Tumors                 122360083           1.9691099
Sample14            Tumors                 80575136             1.9405350
Sample15            Tumors                104183711           1.7019891
Sample16            Tumors                 112372313           2.0484955
Sample17            Tumors                 102789103           1.8569770
Sample18            Tumors                 96733614             2.0323221

My first question is what is used as the reference in the default TMM method's calculation of the normalization factors?  The user's guide and other documentation claims that the reference is "the sample whose 75%-ile (of library-scale-scaled counts) is closest to the mean of 75%-iles."  Presumably the normalization factor for the reference sample should be 1.0, but none of my samples have a normalization factor of 1.0 (closest is sample 1 with 1.0567240).

My second question is should I be concerned about the large variation in normalization factors among the normals group, and the even larger difference in normalization factors between the normals and the tumors?  I guess it's not all that surprising that the normalization factors are very different between normals and tumors given the huge compositional bias in the normal samples, but is the TMM method robust enough to handle these differences?  Is TMM the best method for this type of normalization?

Thanks for your help!

-Jason

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