ArticleBMC bioinformatics2017
Integrated genomic analysis of biological gene sets with applications in lung cancer prognosis.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Linking genotype to phenotype in multi-omics data of small sample.BMC genomics · 2021Article
- Leveraging Methylation Alterations to Discover Potential Causal Genes Associated With the Survival Risk of Cervical Cancer in TCGA Through a Two-Stage Inference Approach.Frontiers in genetics · 2021Article
- Bayesian structural equation modeling in multiple omics data with application to circadian genes.Bioinformatics (Oxford, England) · 2020Article
- Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective.Metabolites · 2019Review
- FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods.BMC bioinformatics · 2019Article
- Multiple Omics Data Integration to Identify Long Noncoding RNA Responsible for Breast Cancer-Related Mortality.Cancer informatics · 2019Article
- A network-based signature to predict the survival of non-smoking lung adenocarcinoma.Cancer management and research · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
backgroundBurgeoning interest in integrative analyses has produced a rise in studies which incorporate data from multiple genomic platforms. Literature for conducting formal hypothesis testing on an integrative gene set level is considerably sparse. This paper is biologically motivated by our interest in the joint effects of epigenetic methylation loci and their associated mRNA gene expressions on lung cancer survival status.
resultsWe provide an efficient screening approach across multiplatform genomic data on the level of biologically related sets of genes, and our methods are applicable to various disease models regardless whether the underlying true model is known (iTEGS) or unknown (iNOTE). Our proposed testing procedure dominated two competing methods. Using our methods, we identified a total of 28 gene sets with significant joint epigenomic and transcriptomic effects on one-year lung cancer survival.
conclusionsWe propose efficient variance component-based testing procedures to facilitate the joint testing of multiplatform genomic data across an entire gene set. The testing procedure for the gene set is self-contained, and can easily be extended to include more or different genetic platforms. iTEGS and iNOTE implemented in R are freely available through the inote package at https://cran.r-project.org// .
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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.