Lei Kong
Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China
More InformationCorresponding author: E-mail address: kongl@mail.cbi.pku.edu.cn (Lei Kong)
Received Date: 2018-02-26
Accepted Date:2018-08-13
Rev Recd Date:2018-08-11
Available Online: 2018-09-13 Publish Date:2018-09-20
Abstract
Abstract
Gene set enrichment (GSE) analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challenging due to the plethora of GSE tools and their discrepant performances. Several existing ensemble methods lead to different scores in sorting pathways as integrated results; furthermore, it is difficult for users to choose a single ensemble score to obtain optimal final results. Here, we develop an ensemble method using a machine learning approach called Combined Gene set analysis incorporating Prioritization and Sensitivity (CGPS) that integrates the results provided by nine prominent GSE tools into a single ensemble score (R score) to sort pathways as integrated results. Moreover, to the best of our knowledge, CGPS is the first GSE ensemble method built based on a priori knowledge of pathways and phenotypes. Compared with 10 widely used individual methods and five types of ensemble scores from two ensemble methods, we demonstrate that sorting pathways based on the R score can better prioritize relevant pathways, as established by an evaluation of 120 simulated datasets and 45 real datasets. Additionally, CGPS is applied to expression data involving the drug panobinostat, which is an anticancer treatment against multiple myeloma. The results identify cell processes associated with cancer, such as the p53 signaling pathway (hsa04115); by contrast, according to two ensemble methods (EnrichmentBrowser and EGSEA), this pathway has a rank higher than 20, which may cause users to miss the pathway in their analyses. We show that this method, which is based on a priori knowledge, can capture valuable biological information from numerous types of gene set collections, such as KEGG pathways, GO terms, Reactome, and BioCarta. CGPS is publicly available as a standalone source code at ftp://ftp.cbi.pku.edu.cn/pub/CGPS_download/cgps-1.0.0.tar.gz.Keywords: Gene expression,
Differential expression,
Gene set enrichment,
Support vector machine
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