title: SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples.
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.
authors.affiliation: Department of Biochemistry, The University of Hong Kong, Hong Kong (SAR), China Department of Pathology, The University of Hong Kong, Hong Kong (SAR), China.
authors.forename: Hari Krishna
authors.initials: HK
authors.lastname: Yalamanchili
authors.affiliation: Centre for Genomic Sciences, L.K.S. Faculty of Medicine, The University of Hong Kong, Hong Kong (SAR), China.
authors.forename: Zhaoyuan
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Biochemistry, The University of Hong Kong, Hong Kong (SAR), China Department of Pathology, The University of Hong Kong, Hong Kong (SAR), China.
authors.forename: Panwen
authors.initials: P
authors.lastname: Wang
authors.affiliation: Department of Pathology, The University of Hong Kong, Hong Kong (SAR), China Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, China.
authors.forename: Maria P
authors.initials: MP
authors.lastname: Wong
authors.affiliation: Centre for Genomic Sciences, L.K.S. Faculty of Medicine, The University of Hong Kong, Hong Kong (SAR), China jeffyao@hku.hk.
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Yao
authors.affiliation: Department of Biochemistry, The University of Hong Kong, Hong Kong (SAR), China Department of Pathology, The University of Hong Kong, Hong Kong (SAR), China Department of Statistics & Actuarial Science, Faculty of Science, The University of Hong Kong, Hong Kong (SAR), China junwen@uw.edu.
authors.forename: Junwen
authors.initials: J
authors.lastname: Wang
mesh_terms: D017398:Alternative Splicing; D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D009369:Neoplasms / Q000235:genetics*; D020033:Protein Isoforms / Q000235:genetics* / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods*; D015398:Signal Transduction; D012984:Software
chemical_list: D020033:Protein Isoforms