SURVIV for survival analysis of mRNA isoform variation

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 patie...

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Main Authors:	Shen, Shihao, Wang, Yuanyuan, Wang, Chengyang, Wu, Ying Nian, Xing, Yi
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906168/

id	pubmed-4906168
recordtype	oai_dc
spelling	pubmed-49061682016-06-24 SURVIV for survival analysis of mRNA isoform variation Shen, Shihao Wang, Yuanyuan Wang, Chengyang Wu, Ying Nian Xing, Yi Article 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. Nature Publishing Group 2016-06-09 /pmc/articles/PMC4906168/ /pubmed/27279334 http://dx.doi.org/10.1038/ncomms11548 Text en Copyright © 2016, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Shen, Shihao Wang, Yuanyuan Wang, Chengyang Wu, Ying Nian Xing, Yi
spellingShingle	Shen, Shihao Wang, Yuanyuan Wang, Chengyang Wu, Ying Nian Xing, Yi SURVIV for survival analysis of mRNA isoform variation
author_facet	Shen, Shihao Wang, Yuanyuan Wang, Chengyang Wu, Ying Nian Xing, Yi
author_sort	Shen, Shihao
title	SURVIV for survival analysis of mRNA isoform variation
title_short	SURVIV for survival analysis of mRNA isoform variation
title_full	SURVIV for survival analysis of mRNA isoform variation
title_fullStr	SURVIV for survival analysis of mRNA isoform variation
title_full_unstemmed	SURVIV for survival analysis of mRNA isoform variation
title_sort	surviv for survival analysis of mrna isoform variation
description	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.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906168/
_version_	1613594058685415424

SURVIV for survival analysis of mRNA isoform variation

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