  RNA-seq has become a de facto standard for measuring gene expression.
Traditionally, RNA-seq experiments are mathematically averaged -- they sequence
the mRNA of individuals from different treatment groups, hoping to correlate
phenotype with differences in arithmetic read count averages at shared loci of
interest. Alternatively, the tissue from the same individuals may be pooled
prior to sequencing in what we refer to as a biologically averaged design. As
mathematical averaging sequences all individuals it controls for both
biological and technical variation; however, is the statistical resolution
gained always worth the additional cost? To compare biological and mathematical
averaging, we examined theoretical and empirical estimates of statistical
efficiency and relative cost efficiency. Though less efficient at a fixed
sample size, we found that biological averaging can be more cost efficient than
mathematical averaging. With this motivation, we developed a differential
expression classifier, ICRBC, that can detect alternatively expressed genes
between biologically averaged samples. In simulation studies, we found that
biological averaging and subsequent analysis with our classifier performed
comparably to existing methods, such as ASC, edgeR, and DESeq, especially when
individuals were pooled evenly and less than 20% of the regulome was expected
to be differentially regulated. In two technically distinct mouse datasets and
one plant dataset, we found that our method was over 87% concordant with edgeR
for the 100 most significant features. We therefore conclude biological
averaging may sufficiently control biological variation to a level that
differences in gene expression may be detectable. In such situations, ICRBC can
enable reliable exploratory analysis at a fraction of the cost, especially when
interest lies in the most differentially expressed loci.
