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      Two-step mixed model approach to analyzing differential alternative RNA splicing

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          Abstract

          Changes in gene expression can correlate with poor disease outcomes in two ways: through changes in relative transcript levels or through alternative RNA splicing leading to changes in relative abundance of individual transcript isoforms. The objective of this research is to develop new statistical methods in detecting and analyzing both differentially expressed and spliced isoforms, which appropriately account for the dependence between isoforms and multiple testing corrections for the multi-dimensional structure of at both the gene- and isoform- level. We developed a linear mixed effects model-based approach for analyzing the complex alternative RNA splicing regulation patterns detected by whole-transcriptome RNA-sequencing technologies. This approach thoroughly characterizes and differentiates three types of genes related to alternative RNA splicing events with distinct differential expression/splicing patterns. We applied the concept of appropriately controlling for the gene-level overall false discovery rate (OFDR) in this multi-dimensional alternative RNA splicing analysis utilizing a two-step hierarchical hypothesis testing framework. In the initial screening test we identify genes that have differentially expressed or spliced isoforms; in the subsequent confirmatory testing stage we examine only the isoforms for genes that have passed the screening tests. Comparisons with other methods through application to a whole transcriptome RNA-Seq study of adenoid cystic carcinoma and extensive simulation studies have demonstrated the advantages and improved performances of our method. Our proposed method appropriately controls the gene-level OFDR, maintains statistical power, and is flexible to incorporate advanced experimental designs.

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          Most cited references41

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Differential analysis of gene regulation at transcript resolution with RNA-seq.

            Differential analysis of gene and transcript expression using high-throughput RNA sequencing (RNA-seq) is complicated by several sources of measurement variability and poses numerous statistical challenges. We present Cuffdiff 2, an algorithm that estimates expression at transcript-level resolution and controls for variability evident across replicate libraries. Cuffdiff 2 robustly identifies differentially expressed transcripts and genes and reveals differential splicing and promoter-preference changes. We demonstrate the accuracy of our approach through differential analysis of lung fibroblasts in response to loss of the developmental transcription factor HOXA1, which we show is required for lung fibroblast and HeLa cell cycle progression. Loss of HOXA1 results in significant expression level changes in thousands of individual transcripts, along with isoform switching events in key regulators of the cell cycle. Cuffdiff 2 performs robust differential analysis in RNA-seq experiments at transcript resolution, revealing a layer of regulation not readily observable with other high-throughput technologies.
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              Detecting differential usage of exons from RNA-seq data

              RNA-seq is a powerful tool for the study of alternative splicing and other forms of alternative isoform expression. Understanding the regulation of these processes requires sensitive and specific detection of differential isoform abundance in comparisons between conditions, cell types, or tissues. We present DEXSeq , a statistical method to test for differential exon usage in RNA-seq data. DEXSeq uses generalized linear models and offers reliable control of false discoveries by taking biological variation into account. DEXSeq detects with high sensitivity genes, and in many cases exons, that are subject to differential exon usage. We demonstrate the versatility of DEXSeq by applying it to several data sets. The method facilitates the study of regulation and function of alternative exon usage on a genome-wide scale. An implementation of DEXSeq is available as an R/Bioconductor package.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                9 October 2020
                2020
                : 15
                : 10
                : e0232646
                Affiliations
                [1 ] Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico
                [2 ] University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico
                [3 ] Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico
                Emory University Rollins School of Public Health, UNITED STATES
                Author notes

                Competing Interests: NO authors have competing interests

                Author information
                http://orcid.org/0000-0002-4415-3573
                Article
                PONE-D-20-11155
                10.1371/journal.pone.0232646
                7546511
                33035235
                0d83ef8e-e10e-4699-b0e2-9c58d5fc68c2
                © 2020 Luo et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 April 2020
                : 24 September 2020
                Page count
                Figures: 4, Tables: 3, Pages: 20
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: R01CA170250
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000072, National Institute of Dental and Craniofacial Research;
                Award ID: R01DE023222
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: P30CA118100
                This work was supported by grants from the National Institutes of Health ( https://www.nih.gov/) National Cancer Institute (R01CA170250 to S.A.N.), National Institute of Dental and Craniofacial Research (R01DE023222 to S.A.N.), and was partially supported by UNM Comprehensive Cancer Center Support Grant NCI P30CA118100, the Biostatistics Shared Resource, and Analytical and Translational Genomics Shared Resource. The computational resources used in this work were provided by the University of New Mexico Center for Advanced Research Computing. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Gene Identification and Analysis
                Genetic Screens
                Physical Sciences
                Mathematics
                Probability Theory
                Random Variables
                Covariance
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and life sciences
                Genetics
                Gene expression
                RNA processing
                RNA splicing
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                RNA processing
                RNA splicing
                Biology and life sciences
                Molecular biology
                Macromolecular structure analysis
                RNA structure
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                RNA structure
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Molecular biology assays and analysis techniques
                Nucleic acid analysis
                RNA analysis
                Research and analysis methods
                Molecular biology techniques
                Molecular biology assays and analysis techniques
                Nucleic acid analysis
                RNA analysis
                Biology and life sciences
                Genetics
                Gene expression
                RNA processing
                Alternative Splicing
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                RNA processing
                Alternative Splicing
                Research and Analysis Methods
                Simulation and Modeling
                Custom metadata
                All data and the R code is publicly available to the research community at the Dryad Digital Repository (DOI: 10.5061/dryad.66t1g1k0h). A copy of the data sets and R scripts is also available at: http://www.unm.edu/~kanghn/software.htm

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