ArticleBMC genomics2015
Incorporation of subject-level covariates in quantile normalization of miRNA data.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comprehensive Construction of a Circular RNA-Associated Competing Endogenous RNA Network Identified Novel Circular RNAs in Hypertrophic Cardiomyopathy by Integrated Analysis.Frontiers in genetics · 2020Article
- A risk score model for the prediction of osteosarcoma metastasis.FEBS open bio · 2019Article
- Identifying factors associated with the direction and significance of microRNA tumor-normal expression differences in colorectal cancer.BMC cancer · 2017Article
- A comparison of multiple testing adjustment methods with block-correlation positively-dependent tests.PloS one · 2017Article
- Urinary microRNA-based signature improves accuracy of detection of clinically relevant prostate cancer within the prostate-specific antigen grey zone.Molecular medicine reports · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundMost currently-used normalization methods for miRNA array data are based on methods developed for mRNA arrays despite fundamental differences between the data characteristics. The application of conventional quantile normalization can mask important expression differences by ignoring demographic and environmental factors. We present a generalization of the conventional quantile normalization method, making use of available subject-level covariates in a colorectal cancer study.
resultsIn simulation, our weighted quantile normalization method is shown to increase statistical power by as much as 10 % when relevant subject-level covariates are available. In application to the colorectal cancer study, this increase in power is also observed, and previously-reported dysregulated miRNAs are rediscovered.
conclusionsWhen any subject-level covariates are available, the weighted quantile normalization method should be used over the conventional quantile normalization method.
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