ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems

Abstract

ToppCluster is a web server application that leverages a powerful enrichment analysis and underlying data environment for comparative analyses of multiple gene lists. It generates heatmaps or connectivity networks that reveal functional features shared or specific to multiple gene lists. ToppCluster uses hypergeometric tests to obtain list-specific feature enrichment P-values for currently 17 categories of annotations of human-ortholog genes, and provides user-selectable cutoffs and multiple testing correction methods to control false discovery. Each nameable gene list represents a column input to a resulting matrix whose rows are overrepresented features, and individual cells per-list P-values and corresponding genes per feature. ToppCluster provides users with choices of tabular outputs, hierarchical clustering and heatmap generation, or the ability to interactively select features from the functional enrichment matrix to be transformed into XGMML or GEXF network format documents for use in Cytoscape or Gephi applications, respectively. Here, as example, we demonstrate the ability of ToppCluster to enable identification of list-specific phenotypic and regulatory element features (both cis-elements and 3'UTR microRNA binding sites) among tissue-specific gene lists. ToppCluster's functionalities enable the identification of specialized biological functions and regulatory networks and systems biology-based dissection of biological states. ToppCluster can be accessed freely at http://toppcluster.cchmc.org.

Figure 1.

Schematic representation of the ToppCluster pipeline. Multiple genelists are named by the user and submitted through the ToppCluster interface. 17 different categories of annotations are available as of April 2010. The user can choose the categories to be included, the P-value cutoff, a method of correction for false discovery and the type of output. Functional enrichment analysis is done on each gene list and the results are collated into a single matrix. Significance P-values are log transformed (−log10) into scores and the genes per list that intersect each enriched feature are placed into a separate column. The results are delivered in the chosen output format.

Figure 2.

(A) An ‘Abstracted’ Network showing enriched Mouse Phenotype terms, microRNAs and transcription factors associated with five tissue-specific gene clusters—heart, kidney, liver, muscle and pancreas. (B) Dissected gene-level view of enriched Mouse Phenotype terms, microRNAs and Transcription Factors shared between the kidney and liver specific gene lists. MP, Mouse Phenotype; TFBS, transcription factor binding site; miRNA, microRNA.

Figure 3.

Gene-level network showing user-selected enriched terms from Gene Ontology, Mouse Phenotype, Co-expression, microRNAs and transcription factors for the kidney and liver-specific gene lists.