ArticleBMC genomics2011
Improving gene expression data interpretation by finding latent factors that co-regulate gene modules with clinical factors.
Article in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- MeDiA: Mean Distance Association and Its Applications in Nonlinear Gene Set Analysis.PloS one · 2015Article
- K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data.BioMed research international · 2015Article
- Hierarchical clustering of high-throughput expression data based on general dependences.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
2 authors.
Funding
Abstract
backgroundIn the analysis of high-throughput data with a clinical outcome, researchers mostly focus on genes/proteins that show first-order relations with the clinical outcome. While this approach yields biomarkers and biological mechanisms that are easily interpretable, it may miss information that is important to the understanding of disease mechanism and/or treatment response. Here we test the hypothesis that unobserved factors can be mobilized by the living system to coordinate the response to the clinical factors.
resultsWe developed a computational method named Guided Latent Factor Discovery (GLFD) to identify hidden factors that act in combination with the observed clinical factors to control gene modules. In simulation studies, the method recovered masked factors effectively. Using real microarray data, we demonstrate that the method identifies latent factors that are biologically relevant, and extracts more information than analyzing only the first-order response to the clinical outcome.
conclusionsFinding latent factors using GLFD brings extra insight into the mechanisms of the disease/drug response. The R code of the method is available at http://userwww.service.emory.edu/~tyu8/GLFD.
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