ArticleBMC genomics2017
Assisted clustering of gene expression data using ANCut.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Assisted clustering of gene expression data using regulatory data from partially overlapping sets of individuals.BMC genomics · 2022Article
- Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion.Statistics in medicine · 2021Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- NCutYX: a package for clustering analysis of multilayer omics data.Bioinformatics (Oxford, England) · 2019Article
- Assisted graphical model for gene expression data analysis.Statistics in medicine · 2019Article
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- Assisted gene expression-based clustering with AWNCut.Statistics in medicine · 2018Article
- Overlapping clustering of gene expression data using penalized weighted normalized cut.Genetic epidemiology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundIn biomedical research, gene expression profiling studies have been extensively conducted. The analysis of gene expression data has led to a deeper understanding of human genetics as well as practically useful models. Clustering analysis has been a critical component of gene expression data analysis and can reveal the (previously unknown) interconnections among genes. With the high dimensionality of gene expression data, many of the existing clustering methods and results are not as satisfactory. Intuitively, this is caused by "a lack of information". In recent profiling studies, a prominent trend is to collect data on gene expressions as well as their regulators (copy number alteration, microRNA, methylation, etc.) on the same subjects, making it possible to borrow information from other types of omics measurements in gene expression analysis.
methodsIn this study, an ANCut approach is developed, which is built on the regularized estimation and NCut techniques. An effective R code that implements this approach is developed.
resultsSimulation shows that the proposed approach outperforms direct competitors. The analysis of TCGA (The Cancer Genome Atlas) data further demonstrates its satisfactory performance.
conclusionsWe propose a more effective clustering analysis of gene expression data, with the assistance of information from regulators. It provides a new venue for analyzing gene expression data based on the assisted analysis strategy.
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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.