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

Wenjiang Deng; Tian Mou; Krishna R Kalari; Nifang Niu; Liewei Wang; Yudi Pawitan; Trung Nghia Vu

doi:10.1093/bioinformatics/btz640

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

Journal article

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

Wenjiang Deng, Tian Mou, Krishna R Kalari, Nifang Niu, Liewei Wang, Yudi Pawitan and Trung Nghia Vu

Bioinformatics, Vol.36(3), pp.805-812

02/01/2020

DOI: 10.1093/bioinformatics/btz640

PMCID: PMC9883676

PMID: 31400221

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Abstract

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. Joint estimation of X and β is made possible by a simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. We use an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes. In a differential-expression analysis of a real single-cell RNA-seq dataset, XAEM achieves substantially better rediscovery rates in independent validation sets. Availability and implementation The method and pipeline are implemented as a tool and freely available for use at http://fafner.meb.ki.se/biostatwiki/xaem/. Supplementary information Supplementary data are available at Bioinformatics online.

Details

Title: Subtitle: Alternating EM algorithm for a bilinear model in isoform quantification from RNA-seq data
Creators: Wenjiang Deng - Karolinska Institutet
Tian Mou - Karolinska Institutet
Krishna R Kalari - Department of Health Sciences Research, MN 55905, USA
Nifang Niu - Mayo Clinic
Liewei Wang - Mayo Clinic
Yudi Pawitan - Karolinska Institutet
Trung Nghia Vu - Karolinska Institutet
Resource Type: Journal article
Publication Details: Bioinformatics, Vol.36(3), pp.805-812
Publisher: Oxford University Press
DOI: 10.1093/bioinformatics/btz640
PMID: 31400221
PMCID: PMC9883676
ISSN: 1367-4803
eISSN: 1460-2059
Number of pages: 8
Grant note: Swedish Cancer Fonden, the Swedish Research Council (VR) China Scholarship Council (CSC) Swedish Foundation for Strategic Research (SSF)
Language: English
Date published: 02/01/2020
Academic Unit: Stead Family Department of Pediatrics; Medical Genetics and Genomics
Record Identifier: 9984701547202771

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