ArticlePloS one2014
MicroRNA array normalization: an evaluation using a randomized dataset as the benchmark.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- On data normalization and batch-effect correction for tumor subtyping with microRNA data.NAR genomics and bioinformatics · 2023Article
- Performance evaluation of transcriptomics data normalization for survival risk prediction.Briefings in bioinformatics · 2021Article
- Statistical Assessment of Depth Normalization for Small RNA Sequencing.JCO clinical cancer informatics · 2020Article
- A Post-Processing Algorithm for miRNA Microarray Data.International journal of molecular sciences · 2020Article
- Microarray Normalization Revisited for Reproducible Breast Cancer Biomarkers.BioMed research international · 2020Article
- A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples.Scientific data · 2018Article
- Article
- Whole blood microRNA expression may not be useful for screening non-small cell lung cancer.PloS one · 2017Article
- Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records.Yearbook of medical informatics · 2016Review
- Cautionary Note on Using Cross-Validation for Molecular Classification.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2016Article
- Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.BMC medical genomics · 2016Article
- Preprocessing Steps for Agilent MicroRNA Arrays: Does the Order Matter?Cancer informatics · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays.
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