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. 2013;7 Suppl 4(Suppl 4):S10.
doi: 10.1186/1752-0509-7-S4-S10. Epub 2013 Oct 23.

Weighted set enrichment of gene expression data

Rehman Qureshi, Ahmet Sacan

Weighted set enrichment of gene expression data

Rehman Qureshi et al. BMC Syst Biol. 2013.

Abstract

Background: Sets of genes that are known to be associated with each other can be used to interpret microarray data. This gene set approach to microarray data analysis can illustrate patterns of gene expression which may be more informative than analyzing the expression of individual genes. Various statistical approaches exist for the analysis of gene sets. There are three main classes of these methods: over-representation analysis, functional class scoring, and pathway topology based methods.

Methods: We propose weighted hypergeometric and weighted chi-squared methods in order to assign a rank to the degree to which each gene participates in the enrichment. Each gene is assigned a weight determined by the absolute value of its log fold change, which is then raised to a certain power. The power value can be adjusted as needed. Datasets from the Gene Expression Omnibus are used to test the method. The significantly enriched pathways are validated through searching the literature in order to determine their relevance to the dataset.

Results: Although these methods detect fewer significantly enriched pathways, they can potentially produce more relevant results. Furthermore, we compare the results of different enrichment methods on a set of microarray studies all containing data from various rodent neuropathic pain models.

Discussion: Our method is able to produce more consistent results than other methods when evaluated on similar datasets. It can also potentially detect relevant pathways that are not identified by the standard methods. However, the lack of biological ground truth makes validating the method difficult.

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Figures

Figure 1
Figure 1
The glycosphingolipid biosynthesis-globo series KEGG pathway, with Entrez Genes detected on the array colored pink; this was the top-ranked pathway by weighted hypergeometric enrichment.
Figure 2
Figure 2
A compilation of the pathways selected by hypergeometric enrichment from the rodent neuropathic pain model datasets. Only pathways enriched in 2 or more datasets are shown.
Figure 3
Figure 3
A compilation of the pathways selected by chi-squared enrichment from the rodent neuropathic pain model datasets. Only pathways enriched in 2 or more datasets are shown.
Figure 4
Figure 4
A compilation of the pathways selected by weighted hypergeometric enrichment from the rodent neuropathic pain model datasets. Only pathways enriched in 2 or more datasets are shown.
Figure 5
Figure 5
A compilation of the pathways selected by weighted chi-squared enrichment from the rodent neuropathic pain model datasets. Only pathways enriched in 2 or more datasets are shown.
Figure 6
Figure 6
The butirosin and neomycin biosynthesis pathway. The nodes identified in the datasets are colored in pink. The node 2.7.11 corresponds to 3 different rat genes that were identified as significant.
See this image and copyright information in PMC

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References

    1. Ntzani EE, Ioannidis JPA. Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment. Lancet. 2003;362(9394):1439–1444. doi: 10.1016/S0140-6736(03)14686-7. - DOI - PubMed
    1. van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT. et al.Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–536. doi: 10.1038/415530a. - DOI - PubMed
    1. Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, Kim JYH, Goumnerova LC, Black PM, Lau C. et al.Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 2002;415(6870):436–442. doi: 10.1038/415436a. - DOI - PubMed
    1. Simon R. Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data. Br J Cancer. 2003;89(9):1599–1604. doi: 10.1038/sj.bjc.6601326. - DOI - PMC - PubMed
    1. Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM. et al.NCBI GEO: archive for functional genomics data sets--10 years on. Nucleic Acids Research. 2011;39(suppl 1):D1005–D1010. - PMC - PubMed

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