GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

BMC Bioinformatics

Software

David A. Magee¹  Kévin Rue-Albrecht²  Paul A. McGettigan³  Nicolas C. Nalpas⁴  David E. MacHugh⁵  Andrew C. Parnell⁶  Belinda Hernández⁷  Stephen V. Gordon⁸ 

[1] Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland;Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland;Centre for Pharmacology and Therapeutics, Division of Experimental Medicine, Imperial College London, Hammersmith Hospital, W12 0NN, London, UK;Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland;Novartis Pharmaceuticals, Elm Park Business Campus, Merrion Road, Dublin 4, Ireland;Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland;Proteome Center Tübingen, Interfaculty Institute for Cell Biology, University of Tübingen, Auf der Morgenstelle 15, 72076, Tübingen, Germany;Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland;UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland;UCD School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland;UCD School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland;UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland;UCD School of Veterinary Medicine, University College Dublin, Dublin 4, Ireland;UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland;

关键词: Gene expression;    Gene ontology;    Supervised learning;    Classification;    Microarray;    RNA-sequencing;    Functional genomics;   

DOI  :  10.1186/s12859-016-0971-3

 received in 2015-05-01, accepted in 2016-02-25,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundIdentification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors.ResultsWe introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples.ConclusionsGOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.

【 授权许可】

CC BY   
© Rue-Albrecht et al. 2016