• Hari Krishna Yalamanchili, Zhaoyuan Li, Panwen Wang, Maria P Wong, Jianfeng Yao, and Junwen Wang.
• Department of Biochemistry, The University of Hong Kong, Hong Kong (SAR), China Department of Pathology, The University of Hong Kong, Hong Kong (SAR), China.
• Nucleic Acids Res. 2014 Sep 1; 42 (15): e121.

AbstractConventionally, 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.© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

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