title: A MATLAB tool for pathway enrichment using a topology-based pathway regulation score.
abstract: BACKGROUND
  Handling the vast amount of gene expression data generated by genome-wide transcriptional profiling techniques is a challenging task, demanding an informed combination of pre-processing, filtering and analysis methods if meaningful biological conclusions are to be drawn. For example, a range of traditional statistical and computational pathway analysis approaches have been used to identify over-represented processes in microarray data derived from various disease states. However, most of these approaches tend not to exploit the full spectrum of gene expression data, or the various relationships and dependencies. Previously, we described a pathway enrichment analysis tool created in MATLAB that yields a Pathway Regulation Score (PRS) by considering signalling pathway topology, and the overrepresentation and magnitude of differentially-expressed genes (J Comput Biol 19:563-573, 2012). Herein, we extended this approach to include metabolic pathways, and described the use of a graphical user interface (GUI).
  
  
  RESULTS
  Using input from a variety of microarray platforms and species, users are able to calculate PRS scores, along with a corresponding z-score for comparison. Further pathway significance assessment may be performed to increase confidence in the pathways obtained, and users can view Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway diagrams marked-up to highlight impacted genes.
  
  
  CONCLUSIONS
  The PRS tool provides a filter in the isolation of biologically-relevant insights from complex transcriptomic data.
authors.affiliation: Department of Applied Computing, the University of Buckingham, Buckingham, MK18 1EG, UK. maysson.ibrahim@buckingham.co.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. maysson.ibrahim@buckingham.co.uk.
authors.forename: Maysson
authors.initials: M
authors.lastname: Ibrahim
authors.affiliation: Department of Applied Computing, the University of Buckingham, Buckingham, MK18 1EG, UK. sabah.jassim@buckingham.ac.uk.
authors.forename: Sabah
authors.initials: S
authors.lastname: Jassim
authors.affiliation: The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. mike.cawthorne@buckingham.ac.uk.
authors.forename: Michael Anthony
authors.initials: MA
authors.lastname: Cawthorne
authors.affiliation: The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Langlands
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics; D053858:Metabolic Networks and Pathways / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D015398:Signal Transduction / Q000235:genetics; D012984:Software