Improved data-driven likelihood factorizations for transcript abundance estimation.

ABSTRACT:

Motivation

Many methods for transcript-level abundance estimation reduce the computational burden associated with the iterative algorithms they use by adopting an approximate factorization of the likelihood function they optimize. This leads to considerably faster convergence of the optimization procedure, since each round of e.g. the EM algorithm, can execute much more quickly. However, these approximate factorizations of the likelihood function simplify calculations at the expense of discarding certain information that can be useful for accurate transcript abundance estimation.

Results

We demonstrate that model simplifications (i.e. factorizations of the likelihood function) adopted by certain abundance estimation methods can lead to a diminished ability to accurately esti

SUBMITTER: Zakeri M 

PROVIDER: S-EPMC5870700 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature