Dataset Information

FDR-FET: an optimizing gene set enrichment analysis method.

ABSTRACT: Gene set enrichment analysis for analyzing large profiling and screening experiments can reveal unifying biological schemes based on previously accumulated knowledge represented as "gene sets". Most of the existing implementations use a fixed fold-change or P value cutoff to generate regulated gene lists. However, the threshold selection in most cases is arbitrary, and has a significant effect on the test outcome and interpretation of the experiment. We developed a new gene set enrichment analysis method, ie, FDR-FET, which dynamically optimizes the threshold choice and improves the sensitivity and selectivity of gene set enrichment analysis. The procedure translates experimental results into a series of regulated gene lists at multiple false discovery rate (FDR) cutoffs, and computes the P value of the overrepresentation of a gene set using a Fisher's exact test (FET) in each of these gene lists. The lowest P value is retained to represent the significance of the gene set. We also implemented improved methods to define a more relevant global reference set for the FET. We demonstrate the validity of the method using a published microarray study of three protease inhibitors of the human immunodeficiency virus and compare the results with those from other popular gene set enrichment analysis algorithms. Our results show that combining FDR with multiple cutoffs allows us to control the error while retaining genes that increase information content. We conclude that FDR-FET can selectively identify significant affected biological processes. Our method can be used for any user-generated gene list in the area of transcriptome, proteome, and other biological and scientific applications.

SUBMITTER: Ji RR

PROVIDER: S-EPMC3169954 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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FDR-FET: an optimizing gene set enrichment analysis method.

Ji Rui-Ru RR Ott Karl-Heinz KH Yordanova Roumyana R Bruccoleri Robert E RE

Advances and applications in bioinformatics and chemistry : AABC 20110315

Gene set enrichment analysis for analyzing large profiling and screening experiments can reveal unifying biological schemes based on previously accumulated knowledge represented as "gene sets". Most of the existing implementations use a fixed fold-change or P value cutoff to generate regulated gene lists. However, the threshold selection in most cases is arbitrary, and has a significant effect on the test outcome and interpretation of the experiment. We developed a new gene set enrichment analys ...[more]

PMID: 21918636

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Project description:Purpose: To demonstrate an interaction-based method for the refinement of Gene Set Enrichment Analysis (GSEA) results. Method: Intravitreal injection of miR-124-3p antagomir was used to knockdown the expression of miR-124-3p in mouse retina at postnatal day 3 (P3). Whole retinal RNA was extracted for mRNA transcriptome sequencing at P9. After preprocessing the dataset, GSEA was performed, and the leading-edge subsets were obtained. The Apriori algorithm was used to identify the frequent genes or gene sets from the union of the leading-edge subsets. A new statistic d was introduced to evaluate the frequent genes or gene sets. Reverse transcription quantitative PCR (RT-qPCR) was performed to validate the expression trend of candidate genes after the knockdown of miR-124-3p. Results: A total of 115,140 assembled transcript sequences were obtained from the clean data. With GSEA, the NOD-like receptor signaling pathway, C-type-like lectin receptor signaling pathway, phagosome, necroptosis, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, leukocyte transendothelial migration, chemokine signaling pathway, NF-kappa B signaling pathway and RIG-I-like signaling pathway were identified as the top 10 enriched pathways, and their leading-edge subsets were obtained. After being refined by the Apriori algorithm and sorted by the value of the modulus of d , Prkcd, Irf9, Stat3, Cxcl12, Stat1, Stat2, Isg15, Eif2ak2, Il6st, Pdgfra, Socs4 and Csf2ra had the significant number of interactions and the greatest value of d to downstream genes among all frequent transactions. Results of RT-qPCR validation for the expression of candidate genes after the knockdown of miR-124-3p showed a similar trend to the RNA-Seq results. Conclusion: This study indicated that using the Apriori algorithm and defining the statistic d was a novel way to refine the GSEA results. We hope to convey the intricacies from the computational results to the low-throughput experiments, and to plan experimental investigations specifically.

Project description:MotivationMuch research effort has been devoted to the identification of enriched gene sets for microarray experiments. However, identified gene sets are often found to be inconsistent among independent studies. This is probably owing to the noisy data of microarray experiments coupled with small sample sizes of individual studies. Therefore, combining information from multiple studies is likely to improve the detection of truly enriched gene classes. As more and more data become available, it calls for statistical methods to integrate information from multiple studies, also known as meta-analysis, to improve the power of identifying enriched gene sets.ResultsWe propose a Bayesian model that provides a coherent framework for joint modeling of both gene set information and gene expression data from multiple studies, to improve the detection of enriched gene sets by leveraging information from different sources available. One distinct feature of our method is that it directly models the gene expression data, instead of using summary statistics, when synthesizing studies. Besides, the proposed model is flexible and offers an appropriate treatment of between-study heterogeneities that frequently arise in the meta-analysis of microarray experiments. We show that under our Bayesian model, the full posterior conditionals all have known distributions, which greatly facilitates the MCMC computation. Simulation results show that the proposed method can improve the power of gene set enrichment meta-analysis, as opposed to existing methods developed by Shen and Tseng (2010, Bioinformatics, 26, 1316-1323), and it is not sensitive to mild or moderate deviations from the distributional assumption for gene expression data. We illustrate the proposed method through an application of combining eight lung cancer datasets for gene set enrichment analysis, which demonstrates the usefulness of the method.Availabilityhttp://qbrc.swmed.edu/software/.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:BackgroundGene Set Enrichment Analysis (GSEA) is a powerful tool to identify enriched functional categories of informative biomarkers. Canonical GSEA takes one-dimensional feature scores derived from the data of one platform as inputs. Numerous extensions of GSEA handling multimodal OMIC data are proposed, yet none of them explicitly captures combinatorial relations of feature scores from multiple platforms.ResultsWe propose multivariate GSEA (MGSEA) to capture combinatorial relations of gene set enrichment among multiple platform features. MGSEA successfully captures designed feature relations from simulated data. By applying it to the scores of delineating breast cancer and glioblastoma multiforme (GBM) subtypes from The Cancer Genome Atlas (TCGA) datasets of CNV, DNA methylation and mRNA expressions, we find that breast cancer and GBM data yield both similar and distinct outcomes. Among the enriched functional categories, subtype-specific biomarkers are dominated by mRNA expression in many functional categories in both cancer types and also by CNV in many functional categories in breast cancer. The enriched functional categories belonging to distinct combinatorial patterns are involved different oncogenic processes: cell proliferation (such as cell cycle control, estrogen responses, MYC and E2F targets) for mRNA expression in breast cancer, invasion and metastasis (such as cell adhesion and epithelial-mesenchymal transition (EMT)) for CNV in breast cancer, and diverse processes (such as immune and inflammatory responses, cell adhesion, angiogenesis, and EMT) for mRNA expression in GBM. These observations persist in two external datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) for breast cancer and Repository for Molecular Brain Neoplasia Data (REMBRANDT) for GBM) and are consistent with knowledge of cancer subtypes. We further compare the characteristics of MGSEA with several extensions of GSEA and point out the pros and cons of each method.ConclusionsWe demonstrated the utility of MGSEA by inferring the combinatorial relations of multiple platforms for cancer subtype delineation in three multi-OMIC datasets: TCGA, METABRIC and REMBRANDT. The inferred combinatorial patterns are consistent with the current knowledge and also reveal novel insights about cancer subtypes. MGSEA can be further applied to any genotype-phenotype association problems with multimodal OMIC data.

Dataset Information

FDR-FET: an optimizing gene set enrichment analysis method.

Publications

FDR-FET: an optimizing gene set enrichment analysis method.

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