Co-clustering of biological networks and gene expression data

MOTIVATION: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.     

When Is Hub Gene Selection Better than Standard Meta-Analysis?

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to gene expression data and presents novel R functions for carrying out consensus network analysis, network based screening, and meta analysis.     

dna: An R package for differential network analysis

Differential network analysis provides a framework for examining if there is sufficient statistical evidence to conclude that the structure of a network differs under two experimental conditions or if the structures of two networks are different. The R package dna provides tools and procedures for differential network analysis of genomic data. The focus of this package is on gene-gene networks, but the methods are easily adaptable for more general biological processes. This package includes preprocessing tools for simultaneously preparing a pair of networks for analysis, procedures for computing connectivity scores between pairs of genes based on many available statistical techniques, and tools for handling modules of genes based on these scores. Also, procedures are provided for performing permutation tests based on these scores to determine if the connectivity of a gene differs between the two networks, to determine if the connectivity of a particular set of important genes differs between the two networks, and to determine if the overall module structure differs between the two networks. Several built-in options are available for the types of scores and distances used in the testing procedures, and additionally, the procedures provide flexible methods that allow the user to define custom scores and distances.

Availability

dna is freely available at The Comprehensive R Archive Network, http://CRAN.R-project.org/package=dna     

From Single Genes to Co-Expression Networks: Extracting Knowledge from Barley Functional Genomics

The paper reports an ‘in silico’ approach to gene expression analysis based on a barley gene co-expression network resulting from the study of several publicly available cDNA libraries. The work is an application of Systems Biology to plant science: at the end of the computational step we identified groups of potentially related genes. The communities of co-expressed genes constructed from the network are remarkably characterized from the functional point of view, as shown by the statistical analysis of the Gene Ontology annotations of their members. Experimental, lab-based testing has been carried out to check the relationship between network and biological properties and to identify and suggest effective strategies of information extraction from the network-derived data.Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users. These two authors contributed equally to this work