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Activity Number:
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513
- Gene Expression Analysis
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Type:
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Contributed
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Date/Time:
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Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #324355
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View Presentation
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Title:
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Two-Step Mixed Model Approach to Analyzing Differential Alternative RNA Splicing
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Author(s):
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Li Luo* and Huining Kang and Scott A. Ness and Christine A. Stidley
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Companies:
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University of New Mexico Comprehensive Cancer Center and University of New Mexico and University of New Mexico Comprehensive Cancer Center and University of New Mexico
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Keywords:
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RNA-seq ;
Alternative Splicing ;
Gene Expression ;
two-step testing ;
mixed model ;
multiple testing
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Abstract:
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Background: 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 isoforms. Current statistical methods in detecting differentially expressed/spliced isoforms are limited and do not account for isoform dependence and the multi-dimensional multiple testing structure at both the gene- and isoform- level. Results: We developed a novel linear mixed effects model-based approach accompanied by a two-step hierarchical hypothesis testing framework for analyzing the complex alternative RNA splicing regulation patterns, which differentiates three types of genes with distinct differential expression/splicing patterns. In the initial screening test we identify genes with differentially expressed or spliced isoforms; in the confirmatory test we examine isoforms for genes that passed screening tests. Application to an RNA-Seq study of adenoid cystic carcinoma and simulation studies demonstrated that our method properly controls gene-level overall false discovery rate, maintains statistical power, and incorporates advanced experimental designs.
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Authors who are presenting talks have a * after their name.
