Network tuned multiple rank aggregation and applications to gene ranking

BMC Bioinformatics

Proceedings

Xianghong Jasmine Zhou¹  Wenhui Wang¹  Fengzhu Sun²  Zhenqiu Liu³ 

[1] Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, USA;Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, USA;Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, 220 Handan Rd, Shanghai, PR China;Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, USA;

关键词: Rank Aggregation;    Network;    CGI;    Gene Rank;    Endeavour;    RRA;   

DOI  :  10.1186/1471-2105-16-S1-S6

来源: Springer

【 摘 要 】

With the development of various high throughput technologies and analysis methods, researchers can study different aspects of a biological phenomenon simultaneously or one aspect repeatedly with different experimental techniques and analysis methods. The output from each study is a rank list of components of interest. Aggregation of the rank lists of components, such as proteins, genes and single nucleotide variants (SNV), produced by these experiments has been proven to be helpful in both filtering the noise and bringing forth a more complete understanding of the biological problems. Current available rank aggregation methods do not consider the network information that has been observed to provide vital contributions in many data integration studies. We developed network tuned rank aggregation methods incorporating network information and demonstrated its superior performance over aggregation methods without network information.The methods are tested on predicting the Gene Ontology function of yeast proteins. We validate the methods using combinations of three gene expression data sets and three protein interaction networks as well as an integrated network by combining the three networks. Results show that the aggregated rank lists are more meaningful if protein interaction network is incorporated. Among the methods compared, CGI_RRA and CGI_Endeavour, which integrate rank lists with networks using CGI [1] followed by rank aggregation using either robust rank aggregation (RRA) [2] or Endeavour [3] perform the best. Finally, we use the methods to locate target genes of transcription factors.

【 授权许可】

Unknown   
© Wang et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. 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 use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.