ArrayXPath

ArrayXPath maps clustered microarray gene-expression profiles onto biological pathways to enable pathway-centered analysis and visualization of gene-pathway-disease-drug relationships.


Key Features:

  • Input format: Accepts clustered gene-expression profiles in a tab-delimited text format derived from microarray experiments.
  • Pathway integration: Maps microarray probe identifiers and gene-expression data onto known biological pathway elements.
  • Database integration: Incorporates Gene Ontology (GO), Medical Subject Headings (MeSH), and OMIM Morbid Map annotations for contextualization.
  • Correlation integration: Integrates correlations between genes, diseases, drugs, and pathways to provide a holistic view of interactions.
  • Statistical analysis: Computes Fisher's exact test and relative risk and applies false discovery rate (FDR) corrections for multiple comparisons.
  • Automated identifier mapping: Automatically maps various identifiers from microarray probes to pathway elements.
  • Visualization technology: Produces Scalable Vector Graphics (SVG) visualizations with JavaScript-enabled interactivity to explore mapped data.

Scientific Applications:

  • Pathway crosstalk analysis: Identifies and visualizes interactions between different biological pathways using mapped gene-expression data.
  • Disease mechanism elucidation: Highlights disease-associated genes within pathways to support identification of potential biomarkers and targets using GO, MeSH, and OMIM annotations.
  • Drug response analysis: Relates gene-expression changes to drug associations and pathway context to inform hypotheses about drug responses.

Methodology:

Maps tab-delimited clustered microarray gene-expression profiles to pathway elements, integrates GO, MeSH and OMIM Morbid Map annotations and gene/disease/drug/pathway correlations, automatically maps probe identifiers to pathway components, computes Fisher's exact test and relative risk with false discovery rate (FDR) correction, and generates SVG visualizations with JavaScript-enabled interactivity.

Topics

Details

Tool Type:
web application
Operating Systems:
Linux, Windows, Mac
Programming Languages:
JavaScript
Added:
2/10/2017
Last Updated:
12/10/2018

Operations

Publications

Chung HJ, et al. ArrayXPath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using Scalable Vector Graphics. Nucleic Acids Res. 2005; 33:W621-6. doi: 10.1093/nar/gki450

PMID: 15980549

Chung HJ, et al. ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics. Nucleic Acids Res. 2004; 32:W460-4. doi: 10.1093/nar/gkh476

PMID: 15215430

Documentation