Paper Info
Title
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 estimate the abundances of highly related transcripts. In particular, considering factorizations based on transcript-fragment compatibility alone can result in a loss of accuracy compared to the per-fragment, unsimplified model. However, we show that such shortcomings are not an inherent limitation of approximately factorizing the underlying likelihood function. By considering the appropriate conditional fragment probabilities, and adopting improved, data-driven factorizations of this likelihood, we demonstrate that such approaches can achieve accuracy nearly indistinguishable from methods that consider the complete (i.e. per-fragment) likelihood, while retaining the computational efficiently of the compatibility-based factorizations.
Year
DOI
Venue
2017
10.1093/bioinformatics/btx262
BIOINFORMATICS
Field
DocType
Volume
Data mining,Data-driven,Computer science,Abundance estimation
Journal
33
Issue
ISSN
Citations 
14
1367-4803
2
PageRank 
References 
Authors
0.39
6
4
Name
Order
Citations
PageRank
Mohsen Zakeri131.08
Avi Srivastava2133.73
Fatemeh Almodaresi372.56
Rob Patro411112.98