Format

Send to

Choose Destination
BMC Bioinformatics. 2016 Mar 11;17:126. doi: 10.1186/s12859-016-0971-3.

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

Author information

1
Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland.
2
Centre for Pharmacology and Therapeutics, Division of Experimental Medicine, Imperial College London, Hammersmith Hospital, London, W12 0NN, UK.
3
Novartis Pharmaceuticals, Elm Park Business Campus, Merrion Road, Dublin 4, Ireland.
4
UCD School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland.
5
UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland.
6
Proteome Center Tübingen, Interfaculty Institute for Cell Biology, University of Tübingen, Auf der Morgenstelle 15, 72076, Tübingen, Germany.
7
UCD School of Veterinary Medicine, University College Dublin, Dublin 4, Ireland.
8
Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland. david.machugh@ucd.ie.
9
UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland. david.machugh@ucd.ie.

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

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

PMID:
26968614
PMCID:
PMC4788925
DOI:
10.1186/s12859-016-0971-3
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center