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

Abstract

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.

Figure 1

Scoring pathway activity: gene sets versus regression. Two types of prior information, categorical and quantitative, may be combined with non-thresholded genome-wide expression data to derive a statistical measure of pathway-level activity. In (A) a pre-defined gene set (gray), such as those annotated by the Gene Ontology project, is scored using a t-test for its expression response (red = positive, green = negative) compared to all other genes. In (B) estimated interaction strengths (shades of gray), such as those derived from regulatory sequence analysis or ChIP-chip experiments, are correlated with the expression response of all genes. In both instances the result is a t-value (yellow = positive, blue = negative) that measures the change in mRNA expression associated with a category (A) or interaction (B).

Figure 2

Scoring GO categories: Fisher's exact test versus two-sample t-test. We analyzed gene expression data for the response to the ergosterol biosynthesis inhibitor Lovastatin as measured by Hughes et al. [27]. The two-sample t-test reveals that the mean expression level of genes in the GO category "ergosterol biosynthesis" is significantly higher than expected (dotted line; t = 7.4; P = 1.1·10^-13). Fisher's exact test can be used to score over-representation of the same GO category in the set of most induced genes. However, this requires one to first define a threshold for the expression fold-change of individual genes. The solid line shows how the P-value from Fisher's exact test depends on this threshold.