ArrayXPath: mapping and visualizing microarray gene-expression data integrated biological pathway resources usng SVG
Nucleic Acids Research, 2004 Jul 1;32:W460-W464
Concept diagram of ArrayXPath
ArrayXPath integrates the quinta-partite graph structure of cluster, gene, disease, pathway and GO-term associations from multiple resources.
Input to ArrayXPath
Input to ArrayXPath is a common tab-delimited text file for a (clustered)
<Probe ID>-<Cluster ID>-[<Expression level at conditioni>].
ArrayXPath dose not perform cluster analysis. The input format is designed primarily for partitional clustering algorithms (i.e., K-means and Self-Organizing Maps) but clustering results from hierarchical algorithms (i.e., dendrogram) may be applied by choosing a threshold carefully.
How to use ArrayXPath
Each node has link to gene information page by the GRIP
(Genome Research Informatics Pipeline) engine at SNUBI.
GO terms are enriched with a hyperlink to an GOChase-HistoryResolver
Pathway crosstalk (It should be performed after Fisher's exact testing or Fisher's exact testing with False Discovery Rate)
GO-based annotation and OMIM information (Homo sapiens only)
Example 10 clusters from the Human HeLa cell-cycle data analyzed by ArrayXPath.
(PathMeSH returns a list of disease-related pathways with statistical significance scores by integrating pathway resources, MeSH disease names, and OMIM Morbid Map.)
Upload your expression profile to find the best matching pathways!
10 clusters from the Human HeLa cell-cycle data analyzed by ArrayXPath.
100 clusters from the Human HeLa cell-cycle data analyzed by ArrayXPath.
50 clusters from the Mouse kidney data analyzed by ArrayXPath.
Example input datasets
One can download one of the example files and submit it to
ArrayXPath. (Whitfield ML et al. 2002)
Human HeLa Cell Cycle data set with 6 clusters containing 1,019 (filtered) genes. (Whitfield ML et al. 2002)
anti-GBM IgA nephropathy model (50 clusters containing 1,112
(filtered) genes). (Ref.)
Our dataset submitted to Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSM15078-GSM15092, GSE954-GSE958, and GSE969.
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