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Bioinformatics. 2018 Jun 1;34(11):1859-1867. doi: 10.1093/bioinformatics/bty004.

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.

Author information

1
Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center.
2
Department of Otolaryngology-Head and Neck Surgery.
3
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
4
Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, CA 94158, USA.
5
Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
6
Department of Mathematics & Statistics, The College of New Jersey, Ewing, NJ 08628, USA.
7
Division of Otolaryngology, Department of Surgery, University of California, San Diego, CA 92093, USA.
8
Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, RAS, Moscow 119333, Russia.

Abstract

Motivation:

Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.

Results:

We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data.

Availability and implementation:

SEVA is implemented in the R/Bioconductor package GSReg.

Contact:

bahman@jhu.edu or favorov@sensi.org or ejfertig@jhmi.edu.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
29342249
PMCID:
PMC5972655
[Available on 2019-06-01]
DOI:
10.1093/bioinformatics/bty004

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