Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data

Abstract

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.

Analysis of Gene Sets Based on the Underlying Regulatory Network

“…Rahnenführer et al (2004) demonstrated that the sensitivity of detecting relevant pathways can be improved by integrating information about pathway topology. Barry et al (2005) presented a permutation based procedure, called SAFE, that considers the underlying network structure.…”

A systems biology approach for pathway level analysis

“…Several pathway databases such as KEGG (Ogata et al 1999), BioCarta (http://www.biocarta.com), and Reactome (Joshi-Tope et al 2005) currently describe metabolic pathway and gene signaling networks offering the potential for a more complex and useful analysis. A recent technique, ScorePage, has been developed in an attempt to take advantage of these types of data for the analysis of metabolic pathways (Rahnenfuhrer et al 2004). Unfortunately, no such technique currently exists for the analysis of gene signaling networks.…”

Discovering statistically significant pathways in expression profiling studies

“…An alternative and more successful technique should consider the distribution of pathway genes in the entire list of genes (9)(10)(11)(12) as well as adjust for the correlation structure. In the innovative Gene Set Enrichment Analysis (GSEA) method (13), the following steps are applied: (i) all genes are ranked by using a signal-to-noise ratio; (ii) for each gene set, the distribution of gene ranks from the gene set is compared against the distribution for the rest of the genes by using the enrichment score (ES) based on a one-sided Kolmogorov-Smirnov statistic; (iii) class labels are permuted to generate a null distribution of ES; and (iv) statistical significance of the observed score is assessed for the top-ranking gene set by comparison with the null distribution of maximum scores from each permutation.…”