ArticleCancer informatics2014
Preprocessing Steps for Agilent MicroRNA Arrays: Does the Order Matter?
Article in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 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
- 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
- Integrated analysis of the molecular mechanisms in idiopathic pulmonary fibrosis.International journal of medical sciences · 2021Article
- 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
- 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
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Authors and funding
3 authors.
Funding
Abstract
MOTIVATION/
backgroundPrevious publications on microarray preprocessing mostly focused on method development or comparison for an individual preprocessing step. Very few, if any, focused on recommending an effective ordering of the preprocessing steps, in particular, normalization in relationship to log transformation and probe set summarization. In this study, we aim to study how the relative ordering of the preprocessing steps influences differential expression analysis for Agilent microRNA array data.
methodsA set of 192 untreated primary gynecologic tumor samples (96 endometrial tumors and 96 ovarian tumors) were collected at Memorial Sloan Kettering Cancer Center during the period of 2000-2012. From this same sample set, two datasets were generated: one dataset had no confounding array effects by experimental design and served as the benchmark, and another dataset exhibited array effects and served as the test data. We preprocessed our test dataset using different orderings between the following three steps: quantile normalization, log transformation, and median summarization. Differential expression analysis was performed on each preprocessed test dataset, and the results were compared against the results from the benchmark dataset. True positive rate, false positive rate, and false discovery rate were used to assess the effectiveness of the orderings.
resultsThe ordering of log transformation, quantile normalization (on probe-level data), and median summarization slightly outperforms the other orderings.
conclusionOur results ease the anxiety over the uncertain effect that the orderings could have on the analysis of Agilent microRNA array data.
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