ArticleBioData mining2015
Principal component gene set enrichment (PCGSE).
Article in BioData mining, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Coupling of response biomarkers between tumor and peripheral blood in patients undergoing chemoimmunotherapy.Cell reports. Medicine · 2025Trial
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- Scale-Invariant, Robust Sparse PCA for Large Data via Differentiable Penalties.Transactions on machine learning research · 2026Article
- Serum Proteomic Profiling Implicates a Dysregulated Neurohormonal-Inflammatory Axis in Post-Fontan Sinus Tachycardia.Journal of the American Heart Association · 2026Article
- Characterization of the Immunogenomic Landscape of Ovarian Cancer Uncovers a Distinct Subset of Endometroid Tumors Associated with High CST2 Expression and a Favorable Prognosis.Cancer research communications · 2026Article
- Multi-omics factor analysis identifies the Tensin 1-Fermitin family homologue 2-Fibronectin 1-Integrin signaling axis as a prognostic determinant in colorectal cancer.Molecular biomedicine · 2025Article
- Sparse matrix factorization robust to sample sharing across GWASs reveals interpretable genetic components.American journal of human genetics · 2025Article
- Multi-Omic Data Integration Suggests Putative Microbial Drivers of Aetiopathogenesis in Mycosis Fungoides.Cancers · 2024Article
- Representation and quantification of module activity from omics data with rROMA.NPJ systems biology and applications · 2024Article
- Multi-omics blood atlas reveals unique features of immune and platelet responses to SARS-CoV-2 Omicron breakthrough infection.Immunity · 2023Article
- A guide to multi-omics data collection and integration for translational medicine.Computational and structural biotechnology journal · 2023Review
- GenomicSuperSignature facilitates interpretation of RNA-seq experiments through robust, efficient comparison to public databases.Nature communications · 2022Article
- The proteogenomic subtypes of acute myeloid leukemia.Cancer cell · 2022Article
- A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.International journal of molecular sciences · 2021Review
- COCOA: coordinate covariation analysis of epigenetic heterogeneity.Genome biology · 2020Article
- Comparative genome-scale analysis of Pichia pastoris variants informs selection of an optimal base strain.Biotechnology and bioengineering · 2020Article
- Redefining malignant pleural mesothelioma types as a continuum uncovers immune-vascular interactions.EBioMedicine · 2019Article
- Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.Molecular systems biology · 2018Article
- Unsupervised gene set testing based on random matrix theory.BMC bioinformatics · 2016Article
- The Use of Signal-Transduction and Metabolic Pathways to Predict Human Disease Targets from Electric and Magnetic Fields Using in vitro Data in Human Cell Lines.Frontiers in public health · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundAlthough principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing interpretation methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with mainly zero loadings. Although useful when just a few variables dominate the population PCs, these methods can perform poorly on genomic data, where interesting biological features are frequently represented by the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the association of groups of genes with clinical outcomes, these methods have seen only limited application for testing the enrichment of gene sets relative to sample PCs.
resultsWe describe a novel approach, principal component gene set enrichment (PCGSE), for unsupervised gene set testing relative to the sample PCs of genomic data. The PCGSE method computes the statistical association between gene sets and individual PCs using a two-stage competitive gene set test. To demonstrate the efficacy of the PCGSE method, we use simulated and real gene expression data to evaluate the performance of various gene set test statistics and significance tests.
conclusionsGene set testing is an effective approach for interpreting the PCs of high-dimensional genomic data. As shown using both simulated and real datasets, the PCGSE method can generate biologically meaningful and computationally efficient results via a two-stage, competitive parametric test that correctly accounts for inter-gene correlation.
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Registered trials
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.