On testing the significance of sets of genes

This paper discusses the problem of identifying dieren tially expressed groups of genes from a microarray experiment. The groups of genes are externally dened, for example, sets of gene pathways derived from biological databases. Our starting point is the interesting Gene Set Enrichment Analysis (GSEA) procedure of Subramanian et al. (2005). We study the problem in some generality and propose two potential improvements to GSEA: the maxmean statistic for summarizing gene-sets, and restandardization for more accurate inferences. We discuss a variety of examples and extensions, including the use of gene-set scores for class predictions. We also describe a new R language package GSA that implements our ideas.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  J. Sudbø,et al.  Gene-expression profiles in hereditary breast cancer. , 2001, The New England journal of medicine.

[3]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[4]  E. Dougherty,et al.  Gene-expression profiles in hereditary breast cancer. , 2001, The New England journal of medicine.

[5]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[6]  William Stafford Noble,et al.  Exploring Gene Expression Data with Class Scores , 2001, Pacific Symposium on Biocomputing.

[7]  Aniko Szabo,et al.  Multivariate exploratory tools for microarray data analysis. , 2003, Biostatistics.

[8]  Lev Klebanov,et al.  Multivariate search for differentially expressed gene combinations , 2004, BMC Bioinformatics.

[9]  Thomas Lengauer,et al.  Statistical Applications in Genetics and Molecular Biology Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data , 2011 .

[10]  Gordon K Smyth,et al.  Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2004, Statistical applications in genetics and molecular biology.

[11]  P. Park,et al.  Discovering statistically significant pathways in expression profiling studies. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Giovanni Parmigiani,et al.  Searching for differentially expressed gene combinations , 2005, Genome Biology.

[13]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Andrew B. Nobel,et al.  Significance analysis of functional categories in gene expression studies: a structured permutation approach , 2005, Bioinform..

[15]  Peng Xiao,et al.  Hotelling's T2 multivariate profiling for detecting differential expression in microarrays , 2005, Bioinform..

[16]  Kevin G Becker,et al.  Transcriptional Profiling of Aging in Human Muscle Reveals a Common Aging Signature , 2006, PLoS genetics.

[17]  Robert Tibshirani,et al.  Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma , 2005, PLoS medicine.

[18]  M. Newton,et al.  Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis , 2007, 0708.4350.

[19]  M. Newton.  Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis , 2008 .