Jun Hu1, Jung-Ying Tzeng2. 1. Bioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, TaiwanBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, Taiwan. 2. Bioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, TaiwanBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, TaiwanBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, Taiwan.
Abstract
MOTIVATION: Gene set analysis is a popular method for large-scale genomic studies. Because genes that have common biological features are analyzed jointly, gene set analysis often achieves better power and generates more biologically informative results. With the advancement of technologies, genomic studies with multi-platform data have become increasingly common. Several strategies have been proposed that integrate genomic data from multiple platforms to perform gene set analysis. To evaluate the performances of existing integrative gene set methods under various scenarios, we conduct a comparative simulation analysis based on The Cancer Genome Atlas breast cancer dataset. RESULTS: We find that existing methods for gene set analysis are less effective when sample heterogeneity exists. To address this issue, we develop three methods for multi-platform genomic data with heterogeneity: two non-parametric methods, multi-platform Mann-Whitney statistics and multi-platform outlier robust T-statistics, and a parametric method, multi-platform likelihood ratio statistics. Using simulations, we show that the proposed multi-platform Mann-Whitney statistics method has higher power for heterogeneous samples and comparable performance for homogeneous samples when compared with the existing methods. Our real data applications to two datasets of The Cancer Genome Atlas also suggest that the proposed methods are able to identify novel pathways that are missed by other strategies. AVAILABILITY AND IMPLEMENTATION: http://www4.stat.ncsu.edu/∼jytzeng/Software/Multiplatform_gene_set_analysis/
MOTIVATION: Gene set analysis is a popular method for large-scale genomic studies. Because genes that have common biological features are analyzed jointly, gene set analysis often achieves better power and generates more biologically informative results. With the advancement of technologies, genomic studies with multi-platform data have become increasingly common. Several strategies have been proposed that integrate genomic data from multiple platforms to perform gene set analysis. To evaluate the performances of existing integrative gene set methods under various scenarios, we conduct a comparative simulation analysis based on The Cancer Genome Atlas breast cancer dataset. RESULTS: We find that existing methods for gene set analysis are less effective when sample heterogeneity exists. To address this issue, we develop three methods for multi-platform genomic data with heterogeneity: two non-parametric methods, multi-platform Mann-Whitney statistics and multi-platform outlier robust T-statistics, and a parametric method, multi-platform likelihood ratio statistics. Using simulations, we show that the proposed multi-platform Mann-Whitney statistics method has higher power for heterogeneous samples and comparable performance for homogeneous samples when compared with the existing methods. Our real data applications to two datasets of The Cancer Genome Atlas also suggest that the proposed methods are able to identify novel pathways that are missed by other strategies. AVAILABILITY AND IMPLEMENTATION: http://www4.stat.ncsu.edu/∼jytzeng/Software/Multiplatform_gene_set_analysis/
Authors: M Ashburner; C A Ball; J A Blake; D Botstein; H Butler; J M Cherry; A P Davis; K Dolinski; S S Dwight; J T Eppig; M A Harris; D P Hill; L Issel-Tarver; A Kasarskis; S Lewis; J C Matese; J E Richardson; M Ringwald; G M Rubin; G Sherlock Journal: Nat Genet Date: 2000-05 Impact factor: 38.330
Authors: Martin J Aryee; Wennuan Liu; Julia C Engelmann; Philipp Nuhn; Meltem Gurel; Michael C Haffner; David Esopi; Rafael A Irizarry; Robert H Getzenberg; William G Nelson; Jun Luo; Jianfeng Xu; William B Isaacs; G Steven Bova; Srinivasan Yegnasubramanian Journal: Sci Transl Med Date: 2013-01-23 Impact factor: 17.956
Authors: Cigall Kadoch; Diana C Hargreaves; Courtney Hodges; Laura Elias; Lena Ho; Jeff Ranish; Gerald R Crabtree Journal: Nat Genet Date: 2013-05-05 Impact factor: 38.330