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. 2007 Nov 15;23(22):3100-2.
doi: 10.1093/bioinformatics/btm460. Epub 2007 Sep 21.

TAPPA: topological analysis of pathway phenotype association

Shouguo Gao  1 , Xujing Wang
Affiliations

TAPPA: topological analysis of pathway phenotype association

Shouguo Gao et al. Bioinformatics. .

Abstract

Extracting biological insight from microarray data is important but challenging. Here we describe TAPPA, a java-based tool, for identification of phenotype-associated genetic pathways utilizing the pathway topological measures. This is achieved by first calculating a Pathway Connectivity Index (PCI) for each pathway, followed by evaluating its correlation to the phenotypic variation. Our PCI definition not only efficiently captures the contributions from genes that show subtle but consistent changes in expression, but also naturally overweighs the hub genes that interact with a large number of other genes in the pathway. TAPPA also allows evaluation of sub-modules within a pathway and their association to phenotypes.

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Conflict of interest statement

Conflict of Interest: none declared.

Figures

Fig. 1
Fig. 1
An exemplary result of TAPPA to prioritize and visualize pathways. The data are from GEO (http://www.ncbi.nlm.nih.gov; accession number: GSE3330), gluc8 is selected as phenotype. The results accords with clinical view that type 2 diabetes may lead to ALS, both the PCI and hub gene SOD1 (geneid: 20655) highly correlate with the phenotype values (04682 and 0.4383). In mmu05030, the correlation coefficient between the average of normalized gene expression of all genes and phenotype is only 0.199.
See this image and copyright information in PMC

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