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

Hari Krishna Yalamanchili, Zhaoyuan Li, Panwen Wang, Maria P. Wong, Jianfeng Yao, Junwen Wang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)e121
JournalNucleic acids research
Volume42
Issue number15
DOIs
StatePublished - Sep 2 2014

ASJC Scopus subject areas

  • Genetics

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