ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics

Hee-Joon Chung; Mingoo Kim; Chan Hee Park; Jihoon Kim; Ju Han Kim

doi:10.1093/nar/gkh476

ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics

Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W460-4. doi: 10.1093/nar/gkh476.

Authors

Hee-Joon Chung¹, Mingoo Kim, Chan Hee Park, Jihoon Kim, Ju Han Kim

Affiliation

¹ Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul 110-799, Republic of Korea.

Abstract

Biological pathways can provide key information on the organization of biological systems. ArrayXPath (http://www.snubi.org/software/ArrayXPath/) is a web-based service for mapping and visualizing microarray gene-expression data for integrated biological pathway resources using Scalable Vector Graphics (SVG). By integrating major bio-databases and searching pathway resources, ArrayXPath automatically maps different types of identifiers from microarray probes and pathway elements. When one inputs gene-expression clusters, ArrayXPath produces a list of the best matching pathways for each cluster. We applied Fisher's exact test and the false discovery rate (FDR) to evaluate the statistical significance of the association between a cluster and a pathway while correcting the multiple-comparison problem. ArrayXPath produces Javascript-enabled SVGs for web-enabled interactive visualization of pathways integrated with gene-expression profiles.

MeSH terms

Cluster Analysis
Computer Graphics*
Data Interpretation, Statistical
Gene Expression Profiling*
Internet
Oligonucleotide Array Sequence Analysis*
Signal Transduction
Software*
Systems Integration
User-Computer Interface