Gene-set approach for expression pattern analysis

Nam, Dougu; Kim, Seon‐Young

doi:10.1093/bib/bbn001

Cited by 297 publications

(231 citation statements)

References 47 publications

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“…Well-known examples include the gene set enrichment analysis [41] and the maxmean approach [42]. The reader is referred to [43,44] for comprehensive reviews of existing approaches on gene set analysis. Here we highlight the fact that gene set significance testing is different from pathway-guided biomarker discovery since they have different objectives.…”

Section: Knowledge-driven Group Formationmentioning

confidence: 99%

Stable feature selection for biomarker discovery

2010

Computational Biology and Chemistry

276

186

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Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchal framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development.

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Section: Knowledge-driven Group Formationmentioning

confidence: 99%

Stable feature selection for biomarker discovery

2010

Computational Biology and Chemistry

276

186

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“…Numerous genome-wide gene expression analyses have shown that single-gene analysis can find little similarity between two independent studies, but pathway-based analysis may find a number of pathways in common. 24 A pathway analysis is done to identify pathways that are significantly associated with the disease. In other words, we attempt to test whether the pathway is overrepresented by the genes that are significantly associated with the disease.…”

Section: Resultsmentioning

confidence: 99%

Gene and pathway-based second-wave analysis of genome-wide association studies

et al. 2009

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Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression data.

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“…There are two primary types of gene function enrichment analysis methods, individual gene analysis (IGA) and gene set analysis (GSA). [1][2][3] IGA identifies differentially expressed genes through a variety of methods and tests the difference of the proportion of differentially expressed genes between all genes and a given gene set. 4,5 GSA directly calculates gene subset scores using various statistical methods and calculates the significance level.…”

Section: Introductionmentioning

confidence: 99%

DBGSA: a novel method of distance-based gene set analysis

Wang

et al. 2012

J Hum Genet

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When compared with single gene functional analysis, gene set analysis (GSA) can extract more information from gene expression profiles. Currently, several gene set methods have been proposed, but most of the methods cannot detect gene sets with a large number of minor-effect genes. Here, we propose a novel distance-based gene set analysis method. The distance between two groups of genes with different phenotypes based on gene expression should be larger if a certain gene set is significantly associated with the given phenotype. We calculated the distance between two groups with different phenotypes, estimated the significant P-values using two permutation methods and performed multiple hypothesis testing adjustments. This method was performed on one simulated data set and three real data sets. After a comparison and literature verification, we determined that the gene resampling-based permutation method is more suitable for GSA, and the centroid statistical and average linkage statistical distance methods are efficient, especially in detecting gene sets containing more minor-effect genes. We believe that this distance-based method will assist us in finding functional gene sets that are significantly related to a complex trait. Additionally, we have prepared a simple and publically available Perl and R package

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Gene-set approach for expression pattern analysis

Cited by 297 publications

References 47 publications

Stable feature selection for biomarker discovery

Stable feature selection for biomarker discovery

Gene and pathway-based second-wave analysis of genome-wide association studies

DBGSA: a novel method of distance-based gene set analysis

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