Journal Article


David E MacHugh
Stephen V Gordon
Andrew C Parnell
David A Magee
Nicolas C Nalpas
Belinda Hernández
Paul A McGettigan
Kévin Rue-Albrecht



machine learning supervised learning time series data visualisation ontology learning gene expression profiles gene expression data computational biology

GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data. (2015)

Abstract Identification 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. We 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. GOexpress 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.
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Full list of authors on original publication

David E MacHugh, Stephen V Gordon, Andrew C Parnell, David A Magee, Nicolas C Nalpas, Belinda Hernández, Paul A McGettigan, Kévin Rue-Albrecht

Experts in our system

David E. MacHugh
University College Dublin
Total Publications: 82
Stephen V. Gordon
University College Dublin
Total Publications: 40
Andrew Parnell
Maynooth University
Total Publications: 45
David A. Magee
University College Dublin
Total Publications: 47
Nicolas C. Nalpas
University College Dublin
Total Publications: 14
Belinda Hernández
University College Dublin
Total Publications: 5
Paul A. McGettigan
University College Dublin
Total Publications: 46
Kevin Rue-Albrecht
University College Dublin
Total Publications: 12