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SURVIV for survival analysis of mRNA isoform variation

Author

Listed:
  • Shihao Shen

    (Immunology and Molecular Genetics, University of California, Los Angeles)

  • Yuanyuan Wang

    (University of California, Los Angeles)

  • Chengyang Wang

    (University of California, Los Angeles)

  • Ying Nian Wu

    (University of California, Los Angeles)

  • Yi Xing

    (Immunology and Molecular Genetics, University of California, Los Angeles)

Abstract

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.

Suggested Citation

  • Shihao Shen & Yuanyuan Wang & Chengyang Wang & Ying Nian Wu & Yi Xing, 2016. "SURVIV for survival analysis of mRNA isoform variation," Nature Communications, Nature, vol. 7(1), pages 1-11, September.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11548
    DOI: 10.1038/ncomms11548
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    Cited by:

    1. Sean West & Sushil Kumar & Surinder K Batra & Hesham Ali & Dario Ghersi, 2019. "Uncovering and characterizing splice variants associated with survival in lung cancer patients," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-16, October.

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