Abstract
Exploring biological meaning from microarray data is very important but remains a great challenge. Here, we developed three new statistics: linear combination test, quadratic test and de-correlation test to identify differentially expressed pathways from gene expression profile. We apply our statistics to two rheumatoid arthritis datasets. Notably, our results reveal three significant pathways and 275 genes in common in two datasets. The pathways we found are meaningful to uncover the disease mechanisms of rheumatoid arthritis, which implies that our statistics are a powerful tool in functional analysis of gene expression data.
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Siu, H., Dong, H., Jin, L., Xiong, M. (2009). New Statistics for Testing Differential Expression of Pathways from Microarray Data. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_26
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DOI: https://doi.org/10.1007/978-3-642-02466-5_26
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