ArticlePloS one2010
A comprehensive and universal method for assessing the performance of differential gene expression analyses.
Article in PloS one, 2010. 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.
- Transcriptional profiling identifies caspase-1 as a T cell-intrinsic regulator of Th17 differentiation.The Journal of experimental medicine · 2020Article
- multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.Bioinformatics (Oxford, England) · 2019Article
- HiCcompare: an R-package for joint normalization and comparison of HI-C datasets.BMC bioinformatics · 2018Article
- RAD51 interconnects between DNA replication, DNA repair and immunity.Nucleic acids research · 2017Article
- Distinct patterns of innate immune activation by clinical isolates of respiratory syncytial virus.PloS one · 2017Article
- Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold.BMC bioinformatics · 2016Article
- DNA polymerase-α regulates the activation of type I interferons through cytosolic RNA:DNA synthesis.Nature immunology · 2016Article
- Differential outcome of TRIF-mediated signaling in TLR4 and TLR3 induced DC maturation.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- B-Cell and Monocyte Contribution to Systemic Lupus Erythematosus Identified by Cell-Type-Specific Differential Expression Analysis in RNA-Seq Data.Bioinformatics and biology insights · 2015Article
- Transgenic expression of microRNA-181d augments the stress-sensitivity of CD4(+)CD8(+) thymocytes.PloS one · 2014Article
- Signature MicroRNA expression patterns identified in humans with 22q11.2 deletion/DiGeorge syndrome.Clinical immunology (Orlando, Fla.) · 2013Article
- Literature aided determination of data quality and statistical significance threshold for gene expression studies.BMC genomics · 2012Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods.
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Registered trials
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