Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
IMPACT FACTOR increased in 2014: 1.127
5-year IMPACT FACTOR: 1.537
Rank 47 out of 122 in category Statistics & Probability in the 2014 Thomson Reuters Journal Citation Report/Science Edition
SCImago Journal Rank (SJR) 2014: 0.740
Source Normalized Impact per Paper (SNIP) 2014: 0.470
Impact per Publication (IPP) 2014: 0.926
Mathematical Citation Quotient (MCQ) 2014: 0.17
Volume 14 (2015)
Volume 13 (2014)
Volume 12 (2013)
Volume 11 (2012)
Volume 10 (2011)
Volume 9 (2010)
Volume 8 (2009)
Volume 6 (2007)
Volume 5 (2006)
Volume 4 (2005)
Volume 3 (2004)
Volume 2 (2003)
Volume 1 (2002)
Most Downloaded Articles
- A General Framework for Weighted Gene Co-Expression Network Analysis by Zhang, Bin and Horvath, Steve
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments by Smyth, Gordon K
- Sensitivity to prior specification in Bayesian genome-based prediction models by Lehermeier, Christina/ Wimmer, Valentin/ Albrecht, Theresa/ Auinger, Hans-Jürgen/ Gianola, Daniel/ Schmid, Volker J. and Schön, Chris-Carolin
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates by Lund, Steven P./ Nettleton, Dan/ McCarthy, Davis J. and Smyth, Gordon K.
Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data
1Max-Planck-Institute for Informatics, Saarbrücken, Germany
2Max-Planck-Institute for Informatics, Saarbrücken, Germany
3Max-Planck-Institute for Informatics, Saarbrücken, Germany
4Max-Planck-Institute for Informatics, Saarbrücken, Germany
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 3, Issue 1, Pages 1–29, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1055, June 2004
- Published Online:
We present a statistical approach to scoring changes in activity of metabolic pathways from gene expression data. The method identifies the biologically relevant pathways with corresponding statistical significance. Based on gene expression data alone, only local structures of genetic networks can be recovered. Instead of inferring such a network, we propose a hypothesis-based approach. We use given knowledge about biological networks to improve sensitivity and interpretability of findings from microarray experiments.
Recently introduced methods test if members of predefined gene sets are enriched in a list of top-ranked genes in a microarray study. We improve this approach by defining scores that depend on all members of the gene set and that also take pairwise co-regulation of these genes into account. We calculate the significance of co-regulation of gene sets with a nonparametric permutation test. On two data sets the method is validated and its biological relevance is discussed. It turns out that useful measures for co-regulation of genes in a pathway can be identified adaptively.
We refine our method in two aspects specific to pathways. First, to overcome the ambiguity of enzyme-to-gene mappings for a fixed pathway, we introduce algorithms for selecting the best fitting gene for a specific enzyme in a specific condition. In selected cases, functional assignment of genes to pathways is feasible. Second, the sensitivity of detecting relevant pathways is improved by integrating information about pathway topology. The distance of two enzymes is measured by the number of reactions needed to connect them, and enzyme pairs with a smaller distance receive a higher weight in the score calculation.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.