ArticleBMC bioinformatics2010
A benchmark for statistical microarray data analysis that preserves actual biological and technical variance.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-Analysis of Microarray Data of Rainbow Trout Fry Gonad Differentiation Modulated by Ethynylestradiol.PloS one · 2015Pooled it
- Pooled it
- Human Melanoma-Cell Metabolic Profiling: Identification of Novel Biomarkers Indicating Metastasis.International journal of molecular sciences · 2020Article
- Deciphering the metabolic perturbation in hepatic alveolar echinococcosis: aParasites & vectors · 2019Article
- Comparison of the Plasma Metabolome Profiles Between the Internal Thoracic Artery and Ascending Aorta in Patients Undergoing Coronary Artery Bypass Graft Surgery Using Gas Chromatography Time-of-Flight Mass Spectrometry.Journal of Korean medical science · 2019Article
- Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool.Frontiers in genetics · 2019Article
- How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?RNA (New York, N.Y.) · 2016Article
- Adaptation of a Bioinformatics Microarray Analysis Workflow for a Toxicogenomic Study in Rainbow Trout.PloS one · 2015Article
- Mining SOM expression portraits: feature selection and integrating concepts of molecular function.BioData mining · 2012Article
- From hybridization theory to microarray data analysis: performance evaluation.BMC bioinformatics · 2011Article
- PathEx: a novel multi factors based datasets selector web tool.BMC bioinformatics · 2010Article
- Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsets.BMC bioinformatics · 2010Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.
resultsOur novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.
conclusionsPerformance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better. AVAILABILITY: The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.