  Qualifying gene and isoform expression is one of the primary tasks for
RNA-Seq experiments. Given a sequence of counts representing numbers of reads
mapped to different positions (exons and junctions) of isoforms, methods based
on Poisson generalized linear models (GLM) with the identity link function have
been proposed to estimate isoform expression levels from these counts. These
Poisson based models have very limited ability in handling the overdispersion
in the counts brought by various sources, and some of them are not robust to
outliers. We propose a negative binomial based GLM with identity link, and use
a set of robustified quasi-likelihood equations to make it resistant to
outliers. An efficient and reliable numeric algorithm has been identified to
solve these equations. In simulations, we find that our approach seems to
outperform existing approaches. We also find evidence supporting this
conclusion in real RNA-Seq data.
