SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples

SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples

Conventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exo...

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Main Authors: Yalamanchili, Hari Krishna, Li, Zhaoyuan, Wang, Panwen, Wong, Maria P., Yao, Jianfeng, Wang, Junwen
Format: Online
Language:English
Published: Oxford University Press 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150760/
id pubmed-4150760
recordtype oai_dc
spelling pubmed-41507602014-12-01 SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples Yalamanchili, Hari Krishna Li, Zhaoyuan Wang, Panwen Wong, Maria P. Yao, Jianfeng Wang, Junwen Methods Online Conventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet. Oxford University Press 2014-09-02 2014-07-17 /pmc/articles/PMC4150760/ /pubmed/25034693 http://dx.doi.org/10.1093/nar/gku577 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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 Yalamanchili, Hari Krishna
Li, Zhaoyuan
Wang, Panwen
Wong, Maria P.
Yao, Jianfeng
Wang, Junwen
spellingShingle Yalamanchili, Hari Krishna
Li, Zhaoyuan
Wang, Panwen
Wong, Maria P.
Yao, Jianfeng
Wang, Junwen
SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples
author_facet Yalamanchili, Hari Krishna
Li, Zhaoyuan
Wang, Panwen
Wong, Maria P.
Yao, Jianfeng
Wang, Junwen
author_sort Yalamanchili, Hari Krishna
title SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples
title_short SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples
title_full SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples
title_fullStr SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples
title_full_unstemmed SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples
title_sort splicenet: recovering splicing isoform-specific differential gene networks from rna-seq data of normal and diseased samples
description Conventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet.
publisher Oxford University Press
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150760/
_version_ 1613129847791419392

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