Dissecting complex transcriptional responses using pathway-level scores based on prior information.
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.


CONCLUSIONS
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.
Bussemaker
Harmen J
HJ
Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, MC 2441, New York, NY 10027, USA. hjb2004@columbia.edu
Ward
Lucas D
LD
Boorsma
Andre
A
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*; D014158:Transcription, Genetic / Q000502:physiology*
D020543:Proteome; D014157:Transcription Factors