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A general modular framework for gene set enrichment analysis

By Marit Ackermann and Korbinian Strimmer
Topics: Methodology Article
Publisher: BioMed Central
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Provided by: PubMed Central
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    1. (2008). A comparison of statistical methods for gene set enrichment analysis.
    2. (2005). A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol
    3. (2007). Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics
    4. (2008). Black MA: Microarray-based gene set analysis: a comparison of current methods.
    5. (2004). Calculating the statistical significance of changes in pathway activity from gene expression data. Stat Appl Genet Mol Biol
    6. (2008). Category: using categories to model genomic data. Bioconductor Package Vignette
    7. (2008). Connections between the augmented bootstrap and the shrinkage covariance estimator. TEST
    8. (2008). Core Team: R: a language and environment for statistical computing.
    9. (2005). Draghici S: Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics
    10. (1996). Exact t and F tests for analyzing studies with multiple endpoints. Biometrics
    11. (2007). Extensions to gene set enrichment. Bioinformatics
    12. (2005). FA: Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics
    13. (2008). Formulating and testing hypotheses in functional genomics. Artif Intell Med
    14. (2007). Gene expression network analysis and applications to immunology. Bioinformatics
    15. (2001). Gene-expression profiles in hereditary breast cancer.
    16. (2008). Gene-set approach for expression pattern analysis. Brief Bioinform
    17. (2009). Glimm E: High-dimensional data analysis: selection of variables, data compression, and graphics – application to gene expression.
    18. (2008). GlobalANCOVA: exploration and assessment of gene group effects. Bioinformatics
    19. (2004). GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using gene ontology hierarchies.
    20. (2006). Grieve IC: Grouping gene ontology terms to improve the assessment of gene set enrichment in microarray data.
    21. (2003). Groop LC: PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet
    22. (2004). Houwelingen HC: A global test for groups of genes: testing association with a clinical outcome. Bioinformatics
    23. (2003). Identifying biological themes within lists of genes with EASE. Genome Biol
    24. (2007). Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics
    25. (1954). Kruskal WH: Measures of association for crossclassification.
    26. (2004). Large-scale simultaneous hypothesis testing: the choice of a null hypothesis.
    27. (2006). Lengauer T: Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics
    28. (2007). Lenhof HP: Computation of significance scores of unweighted gene set enrichment analyses.
    29. Limma: linear models for microarray data. In Bioinformatics and Computational Biology Solutions using R and Bioconductor Edited by: Gentleman
    30. (2005). Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA
    31. (2007). On testing the significance of sets of genes.
    32. (1922). On the interpretation of χ2 from contingency tables, and the calculation of P. J Roy Statist Soc
    33. (2008). Pathway analysis of microarray data via regression.
    34. (2006). PJ: A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinformatics
    35. (2005). PJ: Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci USA
    36. (2007). Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. Ann Appl Statist
    37. (2006). S: Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles. Bioinformatics
    38. (2003). SA: Global functional profiling of gene expression. Genomics
    39. (2007). Shmulevich I: ProbCD: enrichment analysis accounting for categorization uncertainty.
    40. (2001). Significance analysis of microarrays applied to the ionizing radiation response.
    41. (2008). Simultaneous inference: when should hypothesis testing problems be combined? Ann Appl Statist
    42. (2006). SK: Transcriptional profiling of aging in human muscle reveals a common aging structure. PLoS Genetics
    43. (2007). Strimmer K: Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach. Statist Appl Genet Mol Biol
    44. (2005). Testing differential gene expression in functional groups. Methods Inf Med
    45. (2000). The Gene Ontology Consortium: Gene Ontology: tool for the unification of biology.
    46. (2007). Tsai CA: Significance analysis of groups of genes in expression profiling studies. Bioinformatics
    47. (2005). Volsky DJ: PAGE: Parametric analysis of gene set enrichment.
    48. WS: Exploring gene expression data with class scores.
    49. (2007). Y: A multivariate extension of the gene set enrichment analysis.
    50. (2007). Y: Comparative evaluation of gene-set analysis methods.

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