Alternating EM algorithm for a bilinear model in isoform quantification from RNA-seq data.

ABSTRACT:

Motivation

Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform read distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide bias correction step(s), which is based on biological considerations-such as GC content-and applied in single samples separately. The main problem is that not all biases are known.

Results

We have developed a novel method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and is computed based on the simplifying assumptions. In contrast XAEM considers Xβ as a bilinear model with both X and β unknown. Join

SUBMITTER: Deng W 

PROVIDER: S-EPMC9883676 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature