ArticleScientific data2018
A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples.
Article in Scientific data, 2018. 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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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
5 citing papers in PubMed.
- BatMan: Mitigating Batch Effects Via Stratification for Survival Outcome Prediction.JCO clinical cancer informatics · 2023Article
- On data normalization and batch-effect correction for tumor subtyping with microRNA data.NAR genomics and bioinformatics · 2023Article
- Article
- Performance evaluation of transcriptomics data normalization for survival risk prediction.Briefings in bioinformatics · 2021Article
- Making External Validation Valid for Molecular Classifier Development.JCO precision oncology · 2021Article
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Authors and funding
6 authors.
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Abstract
We set out to demonstrate the logistic feasibility of careful experimental design for microarray studies and its level of scientific benefits for improving the accuracy and reproducibility of data inference. Towards this end, we conducted a study of microRNA expression using endometrioid endometrial tumours (n=96) and serous ovarian tumours (n=96) that were primary, untreated, and collected from 2000 to 2012 at Memorial Sloan Kettering Cancer Center. The same set of tumour tissue samples were profiled twice using the Agilent microRNA microarrays: once under an ideal experimental condition with balanced array-to-sample allocation and uniform handling; a second time by mimicking typical practice, with arrays assigned in the order of sample collection and processed by two technicians in multiple batches. This paper provides a detailed description of the generation and validation of this unique dataset pair so that the research community can re-use it to investigate other statistical questions regarding microarray study design and data analysis, and to address biological questions on the relevance of microRNA expression in gynaecologic cancer.
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