Methodology article

Gene set enrichment analysis for non-monotone association and multiple experimental categories

Rongheng Lin¹ , Shuangshuang Dai² , Richard D Irwin³ , Alexandra N Heinloth⁴ , Gary A Boorman⁵ and Leping Li¹

¹  Biostatistics Branch, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA

²  Alpha-Gamma Technologies, Inc., Raleigh NC 27609, USA

³  Environmental Toxicology Program, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA

⁴  Laboratory of Molecular Toxicology, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA

⁵  Covance Inc., Vienna, VA 22066, USA

author email corresponding author email

BMC Bioinformatics 2008, 9:481doi:10.1186/1471-2105-9-481

Published:	14 November 2008

Abstract

Background

Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed.

Results

For continuous or ordinal phenotypic outcome, we propose to use as the local statistic the coefficient of multiple determination (i.e., the square of multiple correlation coefficient) R² from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA/SAFE framework to identify significant gene sets. Unsigned local statistics, signed global statistics and one-sided p-values are used to reflect our inferential interest. Furthermore, we describe a procedure for inference across multiple GSEA/SAFE analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes associated with liver injury as manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Potential statistical dependency resulting from the experimental design is addressed in permutation based hypothesis testing.

Conclusion

The proposed framework captures both linear and non-linear association between gene expression level and a phenotypic endpoint and thus can be viewed as extending the current GSEA/SAFE methodology. The framework for combining results from multiple GSEA/SAFE analyses is flexible to address practical inference interests. Our methods can be applied to microarray data with continuous phenotypes with multi-level design or the meta-analysis of multiple microarray data sets.