ArticleBriefings in bioinformatics2014
Similarity of markers identified from cancer gene expression studies: observations from GEO.
Article in Briefings in bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrated Analyses of Gene Expression Profiles Digs out Common Markers for Rheumatic Diseases.PloS one · 2015Pooled it
- Replicability in cancer omics data analysis: measures and empirical explorations.Briefings in bioinformatics · 2022Article
- Integrating approximate single factor graphical models.Statistics in medicine · 2020Article
- Integrative analysis for identification of shared markers from various functional cells/tissues for rheumatoid arthritis.Immunogenetics · 2017Article
- Integrated analyses for genetic markers of polycystic ovary syndrome with 9 case-control studies of gene expression profiles.Oncotarget · 2017Article
- Common Marker Genes Identified from Various Sample Types for Systemic Lupus Erythematosus.PloS one · 2016Article
- Measures for the degree of overlap of gene signatures and applications to TCGA.Briefings in bioinformatics · 2015Article
- Article
- Fatty Acid binding protein 7 is a molecular marker in adenoid cystic carcinoma of the salivary glands: implications for clinical significance.Translational oncology · 2014Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Gene expression profiling has been extensively conducted in cancer research. The analysis of multiple independent cancer gene expression datasets may provide additional information and complement single-dataset analysis. In this study, we conduct multi-dataset analysis and are interested in evaluating the similarity of cancer-associated genes identified from different datasets. The first objective of this study is to briefly review some statistical methods that can be used for such evaluation. Both marginal analysis and joint analysis methods are reviewed. The second objective is to apply those methods to 26 Gene Expression Omnibus (GEO) datasets on five types of cancers. Our analysis suggests that for the same cancer, the marker identification results may vary significantly across datasets, and different datasets share few common genes. In addition, datasets on different cancers share few common genes. The shared genetic basis of datasets on the same or different cancers, which has been suggested in the literature, is not observed in the analysis of GEO data.
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Registered trials
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