PAGE: Parametric Analysis of Gene Set Enrichment

By Seon-Young Kim, David J. Volsky. BMC Bioinformatics, 2005

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BackgroundGene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.ResultsWe developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level.ConclusionPAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/48a7652ee731507d556e2a3d09bd7a5786a0434a

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Citations in the corpus

Comprehensive literature review and statistical considerations for microarray meta-analysis (2012, methodology): “Pathway analysis (a.k.a. gene set analysis) is a statistical tool to infer correlation of differential expression evidence in the data with pathway knowledge from established databases (3,4).”
Growth Hormone Receptor Deficiency is Associated With a Major Reduction in Pro-aging Signaling, Cancer and Diabetes in Humans (2011, positive): “Gene set enrichment was tested with the PAGE method as described (73).”
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists (2008, neutral): “Other enrichment tools in the GSEA class using the ‘no-cutoﬀ’ strategy, such as ErmineJ (31), FatiScan (55), MEGO (36), PAGE (29), MetaGF, Go-Mapper (22) and ADGO (45), etc., employ parametric statistical approaches such as z -score, t -test, permutation analysis, etc.”
agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update (2017, methodology): “0-provided background and custom analyses, including search, singular enrichment anal-* ysis (SEA), parametric analysis of gene set enrichment (PAGE), SEACOMPARE, Batch SEA, DAG drawer and Scatter Plots (Figure 1).”
Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016, methodology): “Following upon the work of Tian and Kim 57,58 , we used the mean of the quantile-transformed rank positions as test statistic (enrichment score).”
Gene-set approach for expression pattern analysis (2008, methodology): “This point was briefly mentioned by Kim and Volsky [14] and Subramanian et al .”
Integrated Genomics Identifies Five Medulloblastoma Subtypes with Distinct Genetic Profiles, Pathway Signatures and Clinicopathological Features (2008, methodology): “Parametric analysis of geneset enrichment (PAGE) was performed for GeneOntology groups using the methods described by Kim and Volskey [32].”
EnrichNet: network-based gene set enrichment analysis (2012, methodology): “Gene set enrichment analysis (GSEA) methods, which in contrast to classical annotation enrichment analyses incorporate expression level measurements from an unfiltered dataset, including non-parametric approaches such as GSEA (Subramanian et al., 2005), Catmap (Breslin et al., 2004), ErmineJ (Lee et”
Nicotinamide mononucleotide, a key NAD+ intermediate, treats the pathophysiology of diet- and age-induced diabetes in mice (2011, methodology): “We also performed PAGE (Kim and Volsky, 2005) using these gene expression data and GO gene sets.”
Resveratrol delays age-related deterioration and mimics transcriptional aspects of dietary restriction without extending lifespan (2008, positive): “Gene set enrichment was tested using the PAGE method as previously described (Kim and Volsky, 2005) and pathway definitions from MSigDB (Subramanian et al., 2005 ) were selected based on the names and descriptions provided by MSigDB while blinded to the data.”
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update (2016, methodology): “Enrichr also does not have the ability to upload a background list, and it does not have implementation of parametric tests such as Gene Set Enrichment Analysis (GSEA) (40), Parametric Analysis of Gene set Enrichment (PAGE) (9), and our own Principal Angle Enrichment Analysis (PAEA) (41).”
GAGE: generally applicable gene set enrichment for pathway analysis (2009, positive): “We also applied GAGE, PAGE and GSEA to another large dataset describing type 2 diabetes progression that was analyzed by GSEA [4] and PAGE [5] previously (Additional file 1: Supplementary Table 6–7 and Supplementary Note 2).”
agriGO: a GO analysis toolkit for the agricultural community (2010, methodology): “Diﬀerent strategies for GSEA have been introduced already; we chose PAGE which was ﬁrst proposed by Kim and Volsky (21), because it is relatively straightforward, and accuracy is preserved while computation load is lower.”
MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data (2010, neutral): “Over the past 5 years, many diﬀerent algorithms have been developed for group-based enrichment analysis, including GSEA (9), GSEA-P (26), PAGE (27), globaltest (11), SAFE (12), SAM-GS (13) and GSA (14).”
The Mitochondrial-derived peptide MOTS-c promotes metabolic homeostasis and reduces obesity and insulin resistance (2015, neutral): “PAGE was analyzed as previously described (Kim and Volsky, 2005).”
GSVA: gene set variation analysis for microarray and RNA-Seq data (2013, neutral): “Finally, many GSE methods assume two classes (e.g. case/control) and evaluate enrichment within this context [19-22].”
Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, methodology): “Kim and Volsky (30) developed parametric analysis of gene set enrichment (PAGE) as an alternative to the popular GSEA.”