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. 2013 Apr 15;14:128.
doi: 10.1186/1471-2105-14-128.

Enrichr: Interactive and Collaborative HTML5 Gene List Enrichment Analysis Tool

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Free PMC article

Enrichr: Interactive and Collaborative HTML5 Gene List Enrichment Analysis Tool

Edward Y Chen et al. BMC Bioinformatics. .
Free PMC article

Abstract

Background: System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement.

Results: Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios.

Conclusions: Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

Figures

Figure 1
Figure 1
Enrichr workflow. Enrichr receives lists of human or mouse genes as input. It uses 35 gene-set libraries to compute enrichment. The enrichment results are interactively displayed as bar graphs, tables, grids of terms with the enriched terms highlighted, and networks of enriched terms.
Figure 2
Figure 2
Validation of enrichment scoring methods. (a) Histogram of overall appearance of genes in gene sets within all the gene-set libraries implemented in Enrichr plotted on a log-log scale; b-c) Random gene lists are used to obtain enrichment analysis ranking using the Fisher exact test. Average ranks with their associated standard deviations are plotted against gene list length from the ChEA gene set library (b) and the GO Biological Process gene-set library (c); d-e) Ranks of specific transcription factors in enrichment analyses using the ChEA gene-set library by the various enrichment analysis scoring methods. Lists of differentially expressed genes after knockdown of the transcription factors with entries in the ChEA gene-set library were used as input; (d) Average rank for those factors comparing the three scoring methods; (e) histogram of cumulative ranks for the three methods.
Figure 3
Figure 3
Global view of signatures created using genes that are highly expressed in cancer cell lines and their matching human tissues. Enriched terms are highlighted on each grid based on the level of significance using various gene-set libraries, each represented by a different color. Circles are used to highlight specific clusters of enriched terms.

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