ArticleBioinformatics (Oxford, England)2009
Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
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- Potential gene identification and pathway crosstalk analysis of age-related macular degeneration.Frontiers in genetics · 2022Article
- Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells.Scientific reports · 2021Article
- An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data.Frontiers in genetics · 2021Article
- Using sufficient direction factor model to analyze latent activities associated with breast cancer survival.Biometrics · 2020Article
- Statistical Approach for Biologically Relevant Gene Selection from High-Throughput Gene Expression Data.Entropy (Basel, Switzerland) · 2020Article
- Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges.Entropy (Basel, Switzerland) · 2020Review
- Enhancing reproducibility of gene expression analysis with known protein functional relationships: The concept of well-associated protein.PLoS computational biology · 2020Article
- Identification of Candidate Genes Associated with Charcot-Marie-Tooth Disease by Network and Pathway Analysis.BioMed research international · 2020Article
- HisCoM-PAGE: software for hierarchical structural component models for pathway analysis of gene expression data.Genomics & informatics · 2019Article
- HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data.Genes · 2019Article
- The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective.Metabolites · 2019Article
- Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm.Experimental and therapeutic medicine · 2019Article
- Crosstalk analysis of dysregulated pathways in preeclampsia.Experimental and therapeutic medicine · 2019Article
- Individualized Drug Repositioning For Rheumatoid Arthritis Using Weighted Kolmogorov-Smirnov Algorithm.Pharmacogenomics and personalized medicine · 2019Article
- Probabilistic prioritization of candidate pathway association with pathway score.BMC bioinformatics · 2018Article
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2 authors.
Funding
Abstract
motivationRecently, many univariate and several multivariate approaches have been suggested for testing differential expression of gene sets between different phenotypes. However, despite a wealth of literature studying their performance on simulated and real biological data, still there is a need to quantify their relative performance when they are testing different null hypotheses.
resultsIn this article, we compare the performance of univariate and multivariate tests on both simulated and biological data. In the simulation study we demonstrate that high correlations equally affect the power of both, univariate as well as multivariate tests. In addition, for most of them the power is similarly affected by the dimensionality of the gene set and by the percentage of genes in the set, for which expression is changing between two phenotypes. The application of different test statistics to biological data reveals that three statistics (sum of squared t-tests, Hotelling's T(2), N-statistic), testing different null hypotheses, find some common but also some complementing differentially expressed gene sets under specific settings. This demonstrates that due to complementing null hypotheses each test projects on different aspects of the data and for the analysis of biological data it is beneficial to use all three tests simultaneously instead of focusing exclusively on just one.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.