This article is part of the supplement: Otto Warburg International Summer School and Workshop on Networks and Regulation
Review
Dissecting complex transcriptional responses using pathway-level scores based on prior information
-
* Corresponding author: Harmen J Bussemaker hjb2004@columbia.edu
1 Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, MC 2441, New York, NY 10027, USA
2 Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA
3 Swammerdam Institute for Life Sciences, University of Amsterdam, BioCentrum Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands
BMC Bioinformatics 2007, 8(Suppl 6):S6 doi:10.1186/1471-2105-8-S6-S6
Published: 27 September 2007Abstract
Background
The genomewide pattern of changes in mRNA expression measured using DNA microarrays is typically a complex superposition of the response of multiple regulatory pathways to changes in the environment of the cells. The use of prior information, either about the function of the protein encoded by each gene, or about the physical interactions between regulatory factors and the sequences controlling its expression, has emerged as a powerful approach for dissecting complex transcriptional responses.
Results
We review two different approaches for combining the noisy expression levels of multiple individual genes into robust pathway-level differential expression scores. The first is based on a comparison between the distribution of expression levels of genes within a predefined gene set and those of all other genes in the genome. The second starts from an estimate of the strength of genomewide regulatory network connectivities based on sequence information or direct measurements of protein-DNA interactions, and uses regression analysis to estimate the activity of gene regulatory pathways. The statistical methods used are explained in detail.
Conclusion
By avoiding the thresholding of individual genes, pathway-level analysis of differential expression based on prior information can be considerably more sensitive to subtle changes in gene expression than gene-level analysis. The methods are technically straightforward and yield results that are easily interpretable, both biologically and statistically.